the analysis customer

Customer analysis: Definition, benefits & how to perform it the right way

Learn what customer analysis is, what the benefits are to investing in it, and how to perform it the right way.

What is customer analysis?

• Types of customer profiles
• Benefits of customer analysis
• How to do customer analysis

Customer analysis FAQs

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Understanding your customers and potential customers is a key aspect of being able to effectively market to them and build relationships with them. Careful customer analysis will provide you with the tools to convert more customers and keep them from churning for longer periods of time. In this post, we'll take a look at what customer analysis is, its benefits, and how to get started with it.

Customer analysis  is a combination of qualitative and quantitative research collected on your customers with the purpose of better understanding them in order to draw meaningful conclusions that will aid you in your marketing and outreach efforts. It allows you to determine the needs of your customers, the types of messaging they respond well to, and why they are or aren't purchasing your product. Armed with this information, you can make the changes necessary to increase growth. 

Two types of customer profiles you need to create

There are two different ways of looking at customers. Each of these will provide crucial insights into how to best target your messaging and guide future product decisions. 

Customer behavioral profile

Creating a customer behavioral profile consists of two parts. The first part is understanding their buying criteria. Using market research and analytics, you can determine which features are most appreciated by customers, which needs are being underserved, and what price points they're willing to pay. Once you understand your customers' needs and  willingness to pay , you can better tailor your product around those needs and price points. If you already have the features they value most, you can put additional effort into highlighting them in your marketing materials.

The second part of the behavioral profile is the purchasing patterns of your customers. What are they buying, and how frequently are they buying it? Which sales channels are they using to make a purchase, and where did they arrive from? What is the average time between first visit and first purchase? How long do they stay around before churning? All of these questions can inform your marketing decisions and allow for fine-grained tuning of your messaging and sales strategy.

Customer demographic profile

Demographic information such as age, location, income and education levels, gender, and more can give you a clearer picture of the type of messaging that will most resonate with customers and the marketing channels that can best reach them. The best way to determine which demographics to target for future marketing efforts is by looking at your existing customers. Building a marketing strategy around the types of people who are already purchasing is a great way to maximize the ROI of your marketing budget.

5 key benefits of performing customer analysis for any business

We've already touched lightly on some of the reasons that you should be doing customer analysis. Now let's take a closer look at some of the key benefits to show you just how important customer analysis can be to the sustained growth of your business.

1. Reduced churn & increased customer retention

One of the biggest reasons  customers churn  is because the product isn't fulfilling their needs. Customer analysis allows you to better understand what those needs are and tailor your product, your training materials, and your messaging to better address those needs before the customer churns. 

2. Smarter product improvements

Knowing customer needs is a good way to decide what new features and  improvements you should make to your product . By examining customer behavior to see which features are used and which are not, you'll have a better idea of what category of features customers find most useful, and which areas may need some improvement to increase usage. 

3. Personalized and targeted marketing strategy

The most successful marketing departments create buyer personas for their customers. These are fictional characters that represent the various segments of customers that are most likely to purchase your product. The personas give you a more accurate target when determining what the tone of your messaging should be. You can also use analytics to target, in specific ways, customers who exhibit a certain behavior. This two-pronged personalization strategy results in a much more efficient marketing process. 

4. Increased sales and revenue

When you're using buyer personas to communicate more effectively to potential customers, and using the other customer analysis you've performed, to more accurately address their pain points in both your messaging and your product development, you'll end up converting a higher number of potential customers into paying customers for  increased revenue . 

5. Lower CAC 

Increased revenue alone isn't always a good thing. If you end up spending more to get that revenue, you're moving downward in profitability. Customer analysis allows you to make every aspect of the customer journey more efficient, meaning you'll be able to close more sales and keep customers around longer without spending more on marketing or other  customer acquisition strategies .

How to do customer analysis in 8 steps

If your customer analysis is going to be effective, then it needs to be thorough. It's important that you gather the proper information and then apply it in the most meaningful way. The process can be outlined in eight broad steps:

1. Analyze your current customers

If you have a good analytics system in place, then you've already have a lot of data on how your customers behave. While market research looking outside of your customer base can help, looking at the people who have already purchased accomplishes the dual purpose of giving you specific access to how your product is used and providing you with information on the exact type of people likely to purchase from you. 

2. Perform customer profitability analysis to identify the most valuable customers

Your entire customer base can give you plenty of good insights about which features are popular and similar generic metrics. But in order to know which types of customers to reach out to and which improvements are going to make the biggest impact on churn reduction, it pays to look at the type of people who have spent the most and/or stayed with you the longest. Keeping those customers happy and bringing in more like them will give you the most growth. 

3. Perform customer needs analysis to discover the main purchase drivers

People make purchases based on needs. When you understand what your customers (and people similar to them) need from a product, you'll also understand what drives them to make a purchase. This data will play a big role in shaping the future development of your product and help you stay a step ahead of the competition. 

4. Perform customer segmentation analysis to create customer segments

Customers can be segmented in a variety of ways. You can divide them by common demographic traits, common profitability metrics, common shopping or app-usage behaviors, and more. The data itself and  customer segmentation  tools will help guide you by grouping your customers into logical segmentation buckets. The data will help you determine your most profitable customers for the step above, but also show you which are in danger of churning, so you can take steps to avoid it.

5. Use customer data to create buyer personas

Armed with the customer segmentation data, the next step is to get to work crafting a fictional version of the major segments. These  buyer personas  will serve as the fictional embodiment of your customers, so the marketing, sales, and onboarding teams can communicate with them in a way they'll be most responsive to.  

6. Acquire customer feedback

Analytics are a big driver of how you understand your customers, but they alone can't tell you everything. The best way to know what customers think is still the old-fashioned way of asking them. Getting routine  customer feedback  will keep you stay up-to-date on how customers feel about your product versus an ever-changing technological and competitive landscape.

7. Draw a customer journey map

Drawing a map of your current customer journey and comparing it with the feedback they've given and data you collected can help you identify points in the journey that are not currently being adequately addressed. 

The technological landscape is constantly changing. What was state-of-the-art and adored by customers one day can be old news the next. It's important to keep that in mind and ensure that customer analysis is an ongoing effort on your part, and not something you do once and forget about. 

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What should a customer analysis include?

When conducting a customer analysis, you should gather data about how customers are interacting with your product, what their pain points and needs are, what demographic and purchasing groups they belong to, and feedback directly from them about their feelings on your product.

How does customer data analysis help in the decision-making process?

Customer data analysis helps you to better understand what your customer's needs are and what features they interact with the most, to help guide you make accurate decisions for future product development. It also paints an accurate picture of who your customer is, allowing you to make more-informed decisions regarding ad-targeting and the tone of your ad copy and website text.

What are the types of customer analysis?

The two major types of customer analysis are behavioral analysis and demographic analysis. Behavioral analysis can be further divided into two subgroups: buying criteria and purchasing patterns.

Related reading

An introduction to customer analysis

Last updated

14 March 2023

Reviewed by

If you wanted to impress someone you liked, what would you do?

Most people would try to learn everything they could about their crush. They would find out their favorite hangout spot, best food, best movies, and whether they are night owls or morning people. Having this information makes it easier to impress the person.

The same applies to the relationship dynamic between companies and customers. For your business to win over your customers, you must have an in-depth understanding of their lives.

You need to understand their biggest pain points, goals, and what makes them tick. Accessing this information is possible through customer analysis.

Our guide explores what customer analysis is and how to use it to provide customers with better experiences.

• What is customer analysis?

Customer analysis is the practice of collecting qualitative and quantitative customer data to better understand your consumers. This data helps your company arrive at meaningful conclusions that inform how to market and connect with customers.

Customer analysis reveals crucial data such as:

Your customers’ greatest needs

The most significant hurdles to fulfilling these needs

The marketing channels and strategies customers respond best to

Why customers buy or don't buy your products

Demographics and purchasing groups your customers belong to

Your ideal-customer profiles

With access to this data, companies can iterate their marketing efforts and align marketing strategies with customer needs.

• Importance of customer analysis

Customer analysis is the foundation of great marketing. Marketing without customer analysis data is akin to starting a journey without a plan—it's bound to fail.

Here are some specific benefits of customer analysis.

Improving the customer experience

Customer analysis is a potent tool that uses key data to help you improve the customer experience. With concrete customer data, you can personalize all your marketing messages. The customer analysis data also helps you understand which messages work for which customers.

What’s more, customer analysis ensures you are marketing to the right target audience, minimizing risks and financial losses.

Increasing customer retention and reducing churn

Churn refers to the loss of customers, and it happens when your product fails to satisfy customer needs. A high churn rate can also happen when the experience customers have with your company doesn't align with their expectations.

However, you can reduce customer churn by conducting customer analysis. The data you collect can give you the insights you require to tailor experiences that align with customer needs. In turn, more customers will choose to stay.

Increasing revenue

Customer analysis can lead to better personalization, more relatable marketing, and ultimately an increased number of customers. In turn, your business will win over more customers, enjoy repeat business, and improve overall revenue.

• Types of customer analysis

While there are myriad different forms of customer analysis, we will focus on two of the most useful:

Behavioral analysis

Demographic analysis

Let's look at what each type entails and how you can leverage them within your business.

Behavioral customer analysis

This type of analysis encompasses two elements of customer behavior:

Buying criteria

Purchasing patterns

In buying criteria, you try to understand the features and products your customers appreciate most. Analyzing the buying criteria of your target audience also reveals unmet needs and the price customers are willing to pay. This way, you can align your marketing to highlight these features.

Purchasing patterns, on the other hand, explore the following:

Frequency of purchases

The type of products customers buy

The sales channels customers use to buy products

The time between a customer’s initial interaction and their purchase

This data helps you market to customers in ways relatable to them, and via channels they frequent.

Customer demographic analysis

This analysis evaluates data such as gender, age, income, location, and education levels to determine the type of marketing that would resonate best with your target audience.

For instance, if you find out that most of your customers are aged 18–25, the best marketing channels may be platforms such as Instagram and TikTok. Your messaging would likely be more casual, relatable, and conversational, to align with expectations from this age group.

• 4 steps to running an effective customer analysis

There are several steps to embarking on customer analysis. Here are the four most important ones:

1. Structure your existing customer base

Your database of current customers can provide incredible insights when structured correctly.

For example, even if you offer a single product, your customers buy for different reasons. For instance, if you sell accounting software, you might segment your customers into:

Small businesses

Medium enterprises

Freelancers

Solopreneurs

Personal use customers

All these groups of customers have different traits, pain points, and goals.

Start with customer segmentation by categorizing customers into groups, according to:

Demographic: age range, gender, income

Geographic: location-specific

Psychographic: values, beliefs, personality, interest

Technographic: based on the device or channels customers prefer, e.g., mobile vs. desktop, email vs. social media

Behavioral: habits, frequent actions, usage, and purchase behavior

Employment status: applies to B2C customers

Industry: applies when your audience is B2B

Business size: also common for B2B customer segmentation

Needs: the type of need customers are looking to fill

Value to the company: based on the customer lifetime value (CLV), repeat purchase rate, customer loyalty index

Some of this analytics data will be accessible through platforms such as Google Analytics and engagement reports from social media and email.

Once you have a feel for the different segments within your customer base, it will be easier to understand the right marketing strategies, timing, and messaging.

2. Interact with your customers

Hard data is great, but your customer's voice is also a vital component of customer analysis. However you go about it, the goal is to hear the customer's perspective.

There are several customer analysis methods you can employ here. Examples include:

Social media customer surveys

Telephone interviews

Focus groups

One-on-one interviews

You can also find your customers’ voices through other means, such as:

Online reviews

Social media comments

Email marketing responses

Customer support interactions (chatbot and call center logs)

3. Create ideal customer profiles

Once you segment customers and run surveys to discover their voice, it's time to create your ideal customer profile (ICP). The ICP represents how your most valuable customer can be characterized. The ideal customer profile provides the demographic data, behavior traits, values, pain points, and goals of your ideal customer. You can have one or more ICPs, depending on the nature of your business.

Knowing your ICP makes it easy to create personalized experiences for your customers because you now know what they look like and expect.

4. Map the customer journey

When does a customer first interact with your business? How long after the initial interaction do they purchase from you? Which channels do they frequent before buying?

You need to ask all these questions to map the customer journey correctly.

Simply put, the customer journey map showcases all the interactions and touchpoints a customer has with your brand. The customer journey map tells a story of the hurdles, emotions, and processes a customer encounters before buying from you.

In most cases, the customer journey moves as follows:

Consideration → exploration → comparison → testing → negotiation

Consideration often happens after the initial interaction. At this stage, the customer decides to put you on their list of brands to check out. If your brand convinces them you’re worth a shot, they will become more invested and start exploring you at a retailer, on a website, or via an app.

From there, the customer moves to comparison, where they read consumer reports, evaluate reviews, and ask friends or colleagues what they think about your products. If the reviews and reports impress customers, they move to testing. This could be in the form of visiting your store to have a feel and sense of the product. It could also be signing up for a free trial of your product or requesting a demo.

The final stage is negotiation. Here, a customer settles on a product, but they conduct further research about the price before buying.

When the customer journey is clearly defined like this, it's easier to deploy the right messaging at every journey step. In turn, customers experience tailored resources that help them decide and settle on a product with more certainty.

• Customer analysis is not a set-and-forget strategy

The first customer analytics study you perform should not be your last. For best returns, you must keep updating your customer analysis insights and iterating your ICP as consumer behavior changes.

Updating your customer analysis insights also helps you capture emerging target groups and marketing trends as they crop up.

Now that you have all the critical customer insights, it's time to ensure all the relevant people have access. You can achieve this through a simple Google Drive folder, but this can quickly become messy, and vital insights can slip through the cracks.

The best way to get the most out of your insights is to use a customer insights platform. Such a platform ensures all relevant stakeholders have access by centralizing all your insights into a single platform .

Learn more about customer analysis software

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How to Conduct An Effective Customer Analysis in 8 Steps

Effective customer analysis is the key to business success.

Understanding customers’ unique needs and expectations allow companies to better meet these needs. From marketing campaigns to onboarding programs, catering your product experiences to your customers improves satisfaction and retention rates.

Let’s take a closer look at what customer analysis is, its benefits, and how to get started.

• Customer analysis is the act of researching and analyzing customers to gather a complete profile of them and their behavior.
• Customer analysis is important because it helps you create a personalized marketing strategy , reduces churn and increases retention , helps you make smarter product improvements , and helps you identify your most valuable customers (your target market).
• To get started with customer analysis, start by structuring your existing customer base. Organize your customers into customer segments that share common characteristics.
• Then, collect direct customer feedback with in-app surveys . You can use CES , CSAT , and NPS surveys across the entire customer journey.
• Gather insights about your customers from internal teams. Every department likely has unique insights into customers based on their interactions with them. Share customer insights across departments so you don’t miss out on critical information.
• Leverage product analytics to do a customer behavior analysis and get a better understanding of how customers interact with your product.
• Perform social listening on social media and review sites to see an unfiltered look at what customers are saying about your brand.
• After completing your customer analysis, create user personas based on real customer traits. Buyer personas are great to turn to when planning your marketing campaigns or when you want to make product improvements.
• Build a customer journey map to make sure you’re hitting all the touchpoints and understanding how customers engage with your brand at every moment.
• The two top customer analysis tools of our choice are Semrush and Userpilot.
• With Userpilot , you can analyze customer behavior, run surveys, and create customer segments. Then you can build in-app product experiences to improve customer loyalty and satisfaction.
• With Semrush, you can see what marketing channels your website visitors come from (so you can prioritize the best channels). You can then build segments and create user personas.

What is customer analysis?

Customer analysis is the research you do to understand your target market and your customer base. Analyzing customers helps you build more in-depth and accurate customer profiles so you can meet their needs better.

Why is customer analysis important?

Here are the four main reasons why customer analysis is essential:

Create a personalized marketing strategy

Customer analysis helps you optimize your marketing campaigns .

You can then build a more informed marketing plan that engages customers in their preferred channel, with an impactful message, at the right time.

Reduce churn and increase customer retention

Customers churn because products don’t fulfill their expectations.

By understanding their challenges and pain points better, you can tailor your product, training, and experiences to their specific needs and retain them .

Make smarter product improvements

Customer analysis helps you make more informed product decisions. You’ll be able to create new features or make product improvements that will give customers the most value.

Take a look at your product analytics to see how current customers are using the product – are they using certain features more or less?

Observing customer behavior will help you determine what features customers find most useful and what areas may need improvement to increase usage.

I dentify your most valuable customers

With customer analysis, you can pinpoint exactly who your superstar customers are.

This will help determine which market segments to prioritize and how much effort to put into each one. Look at the type of people who have spent the most or stayed with you the longest – these are the ones with the highest customer lifetime value . Do they represent a certain demographic?

Focus on keeping those customers happy and acquiring more like them. They’re an ideal customer and catering to them will lead to faster productivity growth.

How to conduct a customer analysis

Try these nine strategies to conduct a customer analysis:

Structure your existing customer base

You should start the customer analysis process by identifying your potential customers and defining their specific characteristics.

The customer database is a great place for established businesses to begin gathering the above data. Most likely, you already have plenty of data, but you might need to structure it to make sense of it.

To do this, you need to divide your customer database into groups based on similar characteristics. This process of dividing up data is called segmentation .

There are lots of categories you can use to segment customers , including:

• Demographic (age, education, gender, etc.)
• Geographic location (countries, cities, urban or rural areas)
• Behavioral profile (how they interact with your product)
• Company size
• Psychographic (their challenges, goals, values, interests, etc.)

By understanding customer preferences through careful segmentation, companies can deliver targeted experiences, increase conversion rates and retain more customers in the long run.

Identify the needs of your customers

Through customer analysis, you can figure out what pain points you’re solving. Customers turn to your brand because they have a goal and they see you as the potential solution.

Doing this will help you to better understand how to create a post-purchase experience that promotes loyalty.

Collect direct customer feedback with in-app surveys

In-app surveys are one of the most commonly used and effective methods of collecting quantitative and qualitative data from your customers.

You can use different types of surveys at different touchpoints, such as customer effort score (CES), customer satisfaction (CSAT), and net promoter score (NPS) surveys.

Surveys that use both quantitative and qualitative methods give the most well-rounded insights. For example, you collect quantitative data with an NPS survey that asks customers to score how likely they are to recommend your product to a friend or colleague.

After collecting their response, you can trigger a follow-up open-ended question to get more context around their answer.

Invite your customers to one-on-one interviews

An interview can be a great way to empathize with your users as it provides insight into their values, perceptions, and experiences. In these discussions, you are free to ask specific questions while remaining open to participants’ viewpoints.

The advantages of interviews over other methods are they less time than some other user research methods and it’s easier to ask follow-up questions for more detail and accuracy.

You can invite your users to an interview using different in-app messages, such as slideouts.

Make sure to practice gratitude and incentivize them with a small reward , like a gift card.

Gather insights about your customers from internal teams

Businesses often have different departments, each with a different perspective on their customers’ needs . The insights you can gain about your customers from your internal teams can be extremely beneficial for developing a comprehensive view of them.

While sales teams may have a better understanding of customer needs, marketing teams may understand how customers respond to different content or visual stimuli. Meanwhile, Account Management and Customer Success teams may understand the customer’s day-to-day needs even better.

By leveraging all the available insights from across multiple departments, companies can capture a deeper understanding of their target audience and develop better strategies and provide quality experiences that generate growth.

Leverage product analytics

Lean into product usage data to understand customer behavior.

You can get insights into data such as:

• What features do customers use the most and the least
• Which paths do users take and how they navigate inside your product
• What links or call-to-actions users clicked
• What in-app messages have they already been exposed to

How do you collect product usage data ? One way is to use a product analytics tool to tag your UI patterns to see how customers interact with them.

Perform social listening

Keep an eye on public sources – customers often share their honest opinions about your brand on social media and review sites.

Social media: People tend to post on social media (unfiltered) when they have a strong opinion about something, good or bad. Monitor your tagged and untagged mentions on social media channels like Twitter, Facebook, LinkedIn, and Reddit to keep tabs on what people really think.

Review sites: Keep an eye on software review sites, like G2 and Capterra, to see what customer sentiment is like for your brand. Are the reviews overwhelmingly positive or not? What are common complaints? What do people appreciate the most about your product or service?

Social listening gives you a level of honest feedback that you wouldn’t be able to gather otherwise.

Create user and buyer personas

Once you have defined your target audience and identified the types of customers you wish to target, it’s time to start thinking about customer personas . Personas are fictionalized representations of your average customers, gathered and analyzed from all available data points.

Personas should contain enough details to represent the demographic you’re targeting, such as age, gender, occupation, income, location, and interests. Additionally, consider how your customers usually interact with your company and how they prefer to be contacted – e.g., by email or social media.

It is then possible to conduct more research on each persona – such as finding out what makes them tick, and what content they are most likely to engage with – to provide more innovative campaigns that target each segment directly.

Consider creating more than one buyer persona, as different customers will buy your products for different reasons.

Here’s an example of a user persona we created at Userpilot:

• UX Designer
• B2B SaaS with 11-50 employees
• Has found product-market fit
• They’re looking to automate onboarding

Team collaboration:

• Part of UI/UX team
• Collaborates with Product Marketing Manager
• Collaborates with UI designers and developers

Jobs to be done:

• User research to identify the needs of who they are designing for
• Prototyping and user testing to improve UX before launching
• Continuously iterate on solutions to make the product easy to use

Pains/challenges:

• Tapping into feedback collected by other departments, such as Marketing
• Identifying friction points in the user journey due to a lack of product analytics insights
• Hard to document and analyze qualitative data from surveys

Gains of using a Product Growth Platform:

• Build microsurveys to identify users’ needs and share the same data with multiple departments, using one platform only
• Goal tracking at each stage of the journey to help identify drop-off points in the journey
• Tag NPS surveys responses to easily identify patterns of what’s blocking the users from achieving their goals

A solid buyer persona profile can help the entire team better connect with customers and build a better product.

Build a customer journey map

Essentially, a journey map shows all the touchpoints and interactions that a customer must go through to reach their goal.

Considering that customers behave differently at each stage of the buyer’s journey, a customer journey map can help connect the dots and uncover the factors that determine whether their experience is positive.

Every persona should have its own customer journey map. The map will help you identify how to attract, engage, convert, and upsell specific personas and segments based on their challenges, questions, preferred platforms, and types of content.

Best tools for customer analysis

Here are the top two customer analysis tools to help you easily gather data and act on it:

Userpilot is a product growth platform that helps you improve product engagement and retain more customers.

They have multiple high-value features like event tracking, feature tagging, in-app flow builders, and more. As for customer analysis, with Userpilot you can run in-app surveys, track product behavior, tag UI elements, and create user segments based on customer data.

Then, you can put your insights into action by creating in-app product experiences that help boost your growth metrics.

Create contextual customer experiences using UI patterns, such as modals, tooltips, and banners.

Semrush is an all-in-one SEO, content marketing, competitor research, and social media marketing platform.

It’s an excellent tool for customer analysis since it shows you what channels your website visitors come from. Semrush is also as a template for categorizing your data to create user personas .

Also, Semrush Brand Monitoring can help you keep track of all mentions of your brand and product or service in real-time.

Customer analysis helps you increase profits, get more for your marketing efforts, and connect with your target customers.

Want to get started with customer analysis? Get a Userpilot Demo and see how you can do customer segmentation analysis and build in-app product experiences, code-free.

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Customer Analysis – 7 Steps to Customer Understanding

You don't communicate the same way with the different groups of people in your life – family, friends, colleagues. Nor do you buy them the same type of gifts. The same applies to your customers.

Your customers have unique needs, wants, likes and dislikes. Treating them as one uniform group won’t do you any favors. In fact, research shows that 71% of customers feel frustrated when a shopping experience feels impersonal.

What is customer analysis?

Customer analysis combines qualitative and quantitative research methods with the goal of better understanding of your customer base. Knowing what makes your customers tick means you’ll be able to cater to their specific needs.

It's a tide that lifts all boats: marketing can create campaigns with better wording, sales can come up with better pitches, product development will know what features to prioritize, etc.

Customer analysis typically moves through the following stages:

• Identifying who your customers are.
• Discovering their needs and their pain points.
• Grouping customers according to similar traits and behaviors.
• Creating a profile of your ideal customer(s).

Customer analysis can seem like a daunting task. In this post, I'll walk you through the steps that every company can start with.

1 Structure your existing customer database

The best place to start is with your existing customer database. You are likely already sitting on a wealth of data, although it might need some structuring in order to make sense out of it.

The first step in doing this is to divide your customer database into groups with similar characteristics. This process of dividing up data is called segmentation .

By segmenting your customers, you’ll be able to differentiate between customers and focus your marketing efforts on specific groups.

Customers are typically segmented into the following categories:

• Demographic: Age (range), gender, income
• Geographic: Location-specific
• Psychographic: Values, beliefs, interests, personality
• Technographic: Based on the device/platform a customer is using, i.e. desktop vs. mobile
• Behavioral: Behavioral segmentation methods , habits, frequent actions
• Needs-based: Specific needs for a product/service
• Value-based: Value to the company, typically measured by Customer Lifetime Value (CLV). This the amount of profit your company is expected to generate from a single customer over the whole period of their relationship with you.
• Industry: For B2B, what industry the customer belongs to.
• Business size: Also for B2B, the number of employees or the revenue size.

2 Identify your most valuable customers

The next step is to assess who your most valuable customers are.

The Pareto Principle holds true in most companies' customer bases: 80% of business comes from 20% of customers. It makes sense to first get a crystal-clear view of these customers before moving on to the other 80%.

You can identify your most valuable customers by taking important customer metrics and looking for patterns in your database. Do customers with certain characteristics tend to have a higher average customer lifetime value, for example?

In a previous post, we discussed the metrics you should be using to measure retention :

• Customer Lifetime Value (CLV)
• Customer Retention Rate
• Repeat Purchase Rate
• Redemption Rate

In another of our blog posts, we discussed effective methods for measuring loyalty :

• Repurchase Ratio
• Upselling Ratio
• Customer Loyalty Index (CLI)
• Customer engagement numbers

Your customer database also offers other valuable insights. For instance, demographic information about your customers may not affect product usage, but knowing their age and backgrounds will be important for how you communicate with them.

3 Talk to your customers

Data tells only part of the story. To get inside your customers’ heads and understand their needs, you need to talk to them. This involves reaching out, scheduling calls or asking them to come to the office. Interviewing is often paired with focus groups and usability studies.

One-to-one interviews. Interviewing customers in a one-to-one setting gives them the opportunity to share the emotions driving their purchases, their pain points and their deepest needs.

Asking the right questions is crucial here. This is where the 5 Whys technique comes into play: by asking ‘why’ five times, you peel away the layers of an issue and get to the root cause of a problem.

It's important to realize that people make 95% of their purchasing decisions based on emotions. To discover the emotional drivers behind purchases, an interview should feel like a conversation where your customer feels relaxed and safe enough to trust you with their true thoughts and feelings.

HelpScout provides excellent tips for conducting a customer interview which include:

• Asking open-ended questions
• Practicing active listening
• Paying attention to body language

Customer focus groups. A focus group gathers a selection of customers in a room to discuss specific topics under the guidance of a trained facilitator.

Due to the diversity of participants, focus groups can be useful in eliciting a wide range of opinions on your products and services. On the flipside, groupthink may inhibit free discussion or one person may dominate the group with their opinions.

The Call Centre Helper offers tips for running a successful focus group.

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Over 10,000 companies like Toyota and Hermes trust Userlike to connect with their customers every day - via website chat, WhatsApp, chatbots and more.

Usability study. Observing how just a few customers use the product can uncover problems with a website or an app. The key here is to observe with an open mind to avoid inattentional blindness .

You can document your observations through notes, video recordings or photographs.

4 Collect customer voices from the field

Although interviews are valuable, they are also biased. There is a selection bias, for example, with customers with positive experiences more willing to participate than those with negative ones. Also, the answers that people give don't always align with their actual behavior.

Because of this, it also makes sense to collect customer opinions from the field – from customer interactions, social media and review sites.

Service conversations. Your support department is in constant contact with your customers. They are the destination for product issues, pain points and improvement suggestions.

Adding live chat such as Userlike to your website provides a stock of conversations. Support emails are also another resource for gleaning insights. The benefit of these support channels is that they are written, which allows those doing the customer analysis to easily tap in.

Social media - sentiment analysis. If your customer has a brand experience or they want to rave about your new product, chances are, they’ll talk about it on social media. Unrestricted by survey-like questions, people tend to leave no-holds barred feedback on forums like Twitter, Facebook, Reddit and Quora.

Sentiment analysis tools can help to gauge tone and sentiment which are trickier to measure than vanity metrics such as likes and follower counts:

• Critical Mention
• Lexanalytics

Review sites. These can provide more concrete insights than social media forums. They also often make or break a customer’s decision to make a purchase. At Userlike, we regularly check Capterra and G2 .

Gregory Ciotti from HelpScout recommends apps like Trello and Campfire to gather feedback into a collective tool. This removes the headache of analyzing feedback from different, dispersed sources and keeps track of unmet customer needs.

5 Run surveys

Talking to customers and collecting customer voices from the field are qualitative methods. Their purpose is to get a sense of the motivations and concerns of your customers. However, these will be hypotheses at best. To test and proof these hypotheses, it makes sense to run customer surveys.

Let's say that by having gone through your database and talking to customers, you have gotten the intuition that your European customers care more about data privacy issues than your American customers. That could be a powerful insight, as it could inform how you tailor your messaging to these two audiences.

A survey could prove or disprove this hunch, by asking a significant number of customers to rate the importance of data privacy compared to other features.

In a previous blog post , we discussed various ways in which you can conduct a survey:

• In-app surveys
• Post-service surveys
• Long email surveys
• Embedded forms and surveys on your site

This list of do’s and don’ts will steer you in the right direction.

Survey templates:

• SurveyMonkey
• Google Forms

Conjoint analysis. This is a choice-based survey technique which asks customers to place a value on each product or service feature via hypothetical questions.

For example, “would you rather buy a phone plan at §80 with 1200 minutes and no option to rollover unused data or buy a plan at $60 with only 800 minutes but with the option to rollover data?”

This type of trade-off question shows what customers are willing to give up to get what they need. Resources to help you set up a conjoint analysis:

• QuestionPro

6 Create buyer personas

The next step after segmenting your customers is to use all the insights from the above steps to create customer personas of your target customer(s).

Customer personas are simplifications of reality. They are stereotypes of your most valuable customers, including their demographics, motivations, behavior patterns, goals and pain points.

The advantage of creating such personas is that they make it possible to think and talk about your customers. Our brains aren't wired to think about large groups of heterogeneous people. We can, however, think in individuals.

Since different customers may buy your products for different reasons, consider creating more than one buyer persona.

Hubspot provides free, customizable templates to help you get started.

7 Create a customer journey map

After you’ve conducted your customer analysis, you’ll want to analyze the data collected to identify common themes and patterns.

As a customer behaves differently at each stage of the buyer’s journey, a customer journey map can help to connect the dots, and uncover pain points and factors that could make or break their experience.

A journey map is a visual representation of all the touchpoints and interactions a customer has to go through to reach his goal.

In the following example of customer journey map, we can see that there are a number of points at which the customer is at risk of churning:

• The information presented on interfaces doesn’t live up to the descriptions in the commercial.
• The customer has to exert a lot of effort to search for and view new cars.

By mapping the customer’s journey, the car company can take measures to improve messaging and the mobile app to prevent churn at the early stages.

How do you create a customer journey map? For each of your customer personas:

• List all the touchpoints
• List all possible actions
• Chart emotions for each action
• Identify pain points
• The Nielson Norman Group : offers a step-by-step guide to customer journey mapping as well as a downloadable template.
• HubSpot : You can download different types of customer journey map templates here.

Ever-expanding customer insights

Running a customer analysis is one of the most valuable things you can do for your business, but it's not a set-it-and-forget-it tactic. There will always be more insights to be won.

What's more, the market never sits still. New products, competitors, circumstances, targets groups, etc. will enter the scene. The companies that are able to make the most sense out of this complexity are in the best position to benefit from it.

Stay close to your customers and re-run some form of customer analysis on a regular basis.

Chomparani is passionate about reading, learning and all things marketing. When she's not looking for her next book to read, she's hosting or going to quizzes.

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Published October 31 st 2023

Complete Guide on How to Conduct an Effective Customer and Segmentation Analysis

Follow this detailed guide to conducting customer analysis and segmentation and learn how to target your customers with the right messages.

The purpose of undertaking customer analysis as part of a business plan is to examine in-depth the consumers most likely to purchase your product or service. Brands can establish different groups of customers and the needs of those customers. By understanding what motivates them to purchase, brands can build their business around providing solutions to those needs.

So, how can we define customer analysis?

What is customer analysis, and why is it important? 

Customer analysis is the process of examining, understanding, and developing in-depth knowledge about the consumers most likely to convert into customers by purchasing your product or service.

Customer analysis is a critical component of market research and business strategy. The customer analysis process involves systematically collecting and examining data and insights about a company's existing and potential customers. 

This type of analysis aims to discover consumer purchase drivers and how an organization can effectively fill the gap with its product offerings. 

The goal is to identify and segment different groups of customers based on their unique traits, motivations, and needs. Organizations can explore demographics, psychographics, interests, behaviors, and other characteristics that make up a customer profile or buyer persona. 

What is a customer profile or buyer persona?

A customer profile (or buyer persona) is a dossier containing a detailed record of the ideal consumer interested in purchasing your product or service. Organizations use buyer personas to tailor their marketing strategies, product development, and sales efforts to better align with customer preferences and expectations. 

How can brands benefit from doing customer analysis?

Insights from customer analysis can help companies enhance customer satisfaction, target the right audience with tailored messages, uncover market trends, and make informed decisions, ultimately driving business growth and success in a competitive marketplace. 

Customer analysis empowers businesses to bridge the gap between what they offer and what their customers genuinely need, ensuring that products and services provide practical solutions to consumer pain points.

Stages of customer analysis

Customer analysis should move through three different stages.

• You first need to identify who your current customers are. The more detailed understanding you have of your customers, the better. This one group of customers should then be split into subgroups with similar traits and motivations. You can also identify target customers you are not yet reaching.
• Customer analysis must then show what the needs of these different customer groups are.
• You then need to work out what bridges these two, identifying how the company’s products meet the needs of each customer group. How do you provide solutions to their pain points?

What is a customer analysis model?

Any analysis should start with asking clarifying questions that can help establish the reason for the analysis and create a framework for evaluating the data. 

The customer analysis model represents the framework marketers and insights professionals can follow when diving deeper into the customer data to surface consumer preferences and interests.

Researchers often cite the 5W and 6W models, which stand for the Who, What, When, Where, Why, and, lastly, Why not of the data. 

This approach to analyzing customer data can help reveal not only who your target audience is and what they might be interested in but also what they dislike and the reasons behind it.

5 Key steps to conducting effective customer and segmentation analysis

To conduct effective customer and segmentation analysis, organizations should follow the following five key steps:

1. Identify and segment your existing customer base:

• Identify your current customers and gather as much detailed information as possible.
• Segment these customers into distinct groups with similar traits and motivations.
• Identify potential target customers you still need to reach

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2. Define segment criteria:

• Ensure that your segmentation criteria are measurable, observable, substantial, and financially justified (the effort and resources required to target and serve a particular customer segment are justified by the potential return on investment).
• Consider whether marketing messages can be tailored to each segment.
• Evaluate the size and accessibility of each segment to determine the focus.

3. Develop customer profiles and personas:

• Create detailed buyer personas that include background, demographics, communication preferences, and challenges.
• Gather qualitative data in the form of quotes to humanize the personas.
• Visualize a human behind your potential buyer rather than an abstract idea when crafting your personas.

4. Discover customer needs and pain points:

• Engage with customers through surveys, social media, and direct dialogs to understand their needs (e.g., create an open feedback loop or run Q/As on social).
• Analyze past customer actions and explore relevant online platforms like Quora, Reddit, and LinkedIn groups.
• Use social intelligence to monitor discussions and content shared within your persona groups.

5. Connect customer needs to your brand:

• Work with internal stakeholders to determine how your brand can meet the needs and solve the problems of each customer group.
• Emphasize solutions over features, focusing on making customers' lives easier.
• Apply these insights to marketing, sales, and product development to better align with customer expectations and drive business success.

Identify your customers

You can learn more about your customers in a variety of ways, and a mix of research methods will give you the most accurate results. It is best to gather as much information as possible and avoid thinking details are irrelevant. Details like age, gender, location, demographics, and psychographics are all important, but so are their interests, other brands they like, publications they read, and so on.

Talking to them and running a survey will be the best way of hearing about them in their own words, although that does come with biases. Reduce this by complementing that research with sales and CRM data and speaking to customer-facing employees. Once you have identified these groups, social data can elaborate your understanding by providing a more holistic view of the groups.

Your guide to learning about consumers online

Learn how social listening can inform your primary research objectives

It’s also worth considering at this stage whether or not the buyer and end-user are the same person. In a B2B setting, the buyer might hold budget responsibility but not actually use the service or product themselves. 

In a B2C setting, there are several situations when a buyer might not be the end user; a toy water pistol or a diamond ring are both unlikely to be used by the purchaser.

Do customer segmentation in groups

You cannot undertake an accurate customer analysis without segmenting your audience into groups whose members are homogenous and distinct from other groups. Your segmentation criteria should be:

• Measurable : Your analysis should identify the size of a market segment so that you can decide to what extent efforts should be focused on the segment.
• Distinguishable : Observable differences that are clearly defined must exist in order to characterize segments.
• Substantial : The market needs to be large enough to justify segmenting, with each segment substantial enough to make it worthwhile.
• Financial : There will be additional costs when marketing to multiple, separate groups, so the predicted income must exceed these costs.
• Accessible : Your marketing messages should be accessible to each market segment. Different groups will respond better to different forms of advertising.

Develop a customer profile analysis

Use your data, segmentation criteria, and some educated guesswork to develop your buyer personas. It helps to have personas to visualize a human rather than aiming for an abstract idea.

Elements to include in a buyer persona include:

• Background and responsibilities , including job title, career path, and consumers’ primary job responsibilities.
• Demographics , including gender, age, income, family, and location.
• Communication : Which channels do they prefer? What is their demeanor? Do they have an assistant?
• Media and influencers : Which publications do they follow, and which individuals are leading the conversation in their world?
• Challenges vs proposition : The challenges they face in implementing their primary job goals and how your product or service can help them overcome those issues.
• Objections : Common reasons why this persona wouldn’t choose your product.
• Common language : What language should you use to appeal to their needs?
• Quotes : Adding some qualitative data in the form of quotes can really help to bring the personas to life and remind you there are real people behind these aggregated models.

Discover your customers’ needs

The next step in customer analysis is to get a good idea of what the customer’s needs are. By understanding their needs, several departments can gear their output towards answering these questions rather than taking an “If you build it, they will come” approach.

There are numerous ways to discover what your customers’ pain points are.

• The best way is to ask them. A survey is great if you can get enough responses, and online services like SurveyMonkey can keep the cost down.
• Consider the past actions of the group, such as the percentage that have purchased a similar product at some time in the past.
• Look at questions asked on Quora or Reddit .
• For B2B businesses, looking at job adverts for your target customers can give you an insight into their day-to-day work and problems.
• Join LinkedIn professional groups to get an insight into questions and discussions.
• Social intelligence can again help to understand the issues faced by customers. You can begin by building an audience of your personas and then monitor that group for questions asked, relevant content shared (such as how-to guides), and discussions.

How does your brand meet the needs of the customer?

Once you’ve done your research and outlined your different customer groups and their needs, you should connect the dots to your brand and identify how you meet those needs. This section of customer analysis should just be a matter of discussing and brainstorming with internal partners.

Solution-based answers should come out of this process. Instead of merely listing features, concentrate on showcasing how these features address the challenges your customers and prospects have. Always focus on benefits ahead of features. 

Solving the problems that customers face on a daily basis will resonate with them much better than shouting about a shiny new feature. Focus on how you can make their lives easier and more enjoyable.

This will obviously be reflected in marketing, but these insights can help sales and product development as well, tightening your focus to better match your customer’s needs.

Ready to make your customer and segmentation analysis?

Systematically conducting a customer and segmentation analysis is not merely a recommended practice but essential for any business aiming to thrive in today's competitive landscape. 

By understanding the core principles of customer analysis and following the five key steps to conducting an effective analysis outlined in this blog, you can unlock your brand’s true potential and foster lasting success. 

The journey of customer analysis starts from analyzing your existing customers, mirroring buyer personas after them, and ultimately connecting the dots between your brand and the solutions you provide to address your potential customers’ pain points. 

The insights from conducting a customer analysis are not just data; they are the compass that guides your marketing, sales, and product development efforts to better align with your customers’ needs. 

Now it's your turn: How are you going to harness the power of customer analysis to drive business success in 2024?

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Customer Analysis: What It Is and How to Do It

Understanding your customers is essential. Customer needs, customer behavior, customer engagement — it’s all essential to building a winning brand.

That’s where customer analysis comes in.

What is Customer Analysis?

This involves examining and analyzing your customers from all angles and using that data to gain insight into their:

• Characteristics
• Preferences
• Pain points

The end goal of customer analysis is to use quantitative and qualitative data to fully understand your customer base so you can deliver the best possible experience end-to-end.

From the moment when brand awareness first begins to the moment someone purchases your product and everywhere in between, customer analysis supplies you with objective data so you understand the ins and outs.

In turn, you can make data-driven decisions for every aspect of the customer experience rather than merely relying on speculation or “a hunch.”

Performing customer analysis can be done using a mix of tools and strategies, such as:

• Customer analytics
• CRM platforms
• Direct customer feedback via surveys and interviews
• Market research
• Social media monitoring
• Customer experience analytics
• Customer sentiment analysis
• Customer segmentation analysis

The questions are, “How exactly does customer behavior analysis help your business?” and “How do you go about doing it?”

Let’s find out.

Customer Analysis Benefits

There are three main ways businesses can benefit from using customer analysis.

First, it gives you a holistic understanding of your customer base so you can see the big picture.

You know what’s motivating your customers, what they’re looking for, and how to deliver at every stage of the customer journey.

Whether you’re trying to get potential customers to officially enter your sales funnel, looking to raise customer satisfaction among existing customers, or wanting to increase customer loyalty to maximize customer retention, it all starts with customer analysis.

Next, it helps improve your interactions with customers.

Once you know what makes them tick by understanding their preferences, tendencies, characteristics, and so on, you can communicate more effectively and provide a more satisfying experience.

Your marketing team, for example, could adjust your marketing campaign to ensure they’re reaching out through the right channels. Or, your customer support team could “speak their language” when handling questions or complaints.

Third, you can optimize your products.

As you gain more insight through customer analysis, you can pinpoint strengths and weaknesses. From there, you can make the right adjustments to develop the best possible products with features your customers love and use.

Put all of this together, and it can have a dramatic impact on your business, including boosting customer engagement and satisfaction, raising your Net Promoter Score, lowering customer attrition, maximizing customer lifetime value, and more.

How to Do Effective Customer Analysis

1. create a customer journey map.

One of the best starting points is to use customer data analytics to map out the entire customer journey from start to finish.

For instance, here’s how you can use Woopra’s customer journey analytics to get a detailed overview of how customers start from being first-time visitors to requesting a demo to signing up for a free trial to upgrading to a paid version to ultimately, churning.

Besides simply knowing what the sequence of steps looks like, you can also see how many people dropped out and how many continued at each stage.

In turn, business analytics like this provides actionable insight into specific areas of the customer journey that need improvement.

Or if you wanted to go more granular, you could examine particular sections of the customer journey like when someone signs up to become a customer.

In this example, you can follow this section step-by-step so you can see this leg of the customer journey in detail and use data science to enhance the experience.

The possibilities are endless, and creating these types of maps is perfect for helping get your bearings with customer analysis.

2. Analyze Current Customers Using Customer Analytics

After you’ve done customer journey mapping, the next step we recommend is getting a firm grasp on your existing customers.

• Who are your current customers?
• What are their behaviors?
• What motivates them to buy and stick with you?
• Which factors contribute to a positive existing customer experience?
• What issues are harming the customer experience?
• What’s causing customer churn?

These are just a few questions you can find answers to by leveraging customer analytics platforms like Amplitude, Adobe Analytics, and Woopra . (Google Analytics won’t be quite robust enough for this.)

Here are some examples.

One is to see which advertising mediums lead to the most signups from your marketing effort.

Another is to measure how many customers return after using a core product feature over time.

Another is to look at retention reports to see what customer retention is over time.

As you can see, customer data analytics can measure behavior at all stages of the customer journey.

By generating detailed customer data like this, you can gain a clear perspective on what the current customer experience is like so you’ll know how to proceed moving forward and which areas to prioritize.

3. Gather Customer Feedback

Sometimes the simplest customer data is the best data. Therefore, basic customer feedback is something that shouldn’t be overlooked.

After creating a customer journey map and analyzing current customers, you should be able to get a good sense of the customer experience by obtaining feedback through:

• Customer surveys
• Customer interviews
• Feedback forms on your website or email
• Customer support interactions
• Net Promoter Score

For instance, you could ask customers to rate how satisfied they were with a recent experience or how likely they are to recommend your brand to friends or family.

You could ask some of your most highly engaged customers to participate in one-on-one interviews to explain their experience firsthand (offering an incentive like a sizable discount or gift card should increase participation).

Or you could use a Net Promoter Score to determine what percentage of your customers are detractors, passives, and promoters.

By getting direct feedback like this, trends should quickly emerge, and it should supply you with the customer data needed to dramatically improve the customer experience.

4. Measure Customer Sentiment Across Reviews and Social Media

Using the three aforementioned customer analysis strategies should give you a pretty solid understanding of your customers. But you can take it one step further by analyzing their collective sentiment.

There are two main ways to go about this.

One is to read unbiased reviews from real customers to determine what customer sentiment is as a whole and pinpoint specific strengths and weaknesses.

A great resource for finding reviews for business software and services is G2 .

Simply enter your company name, and you can find in-depth information on what customers are saying.

Here’s what Zapier’s G2 review page looks like.

With over 1,200 reviews and a rating, the Zapier team can get a good sense of consumer sentiment.

And by looking at individual reviews, they can see what users like most about the platform or what issues they’ve encountered.

There’s even a handful of videos from Zapier users, which really helps to connect the dots.

Besides that, you can use social listening tools like Brandwatch to monitor brand mention volume,…

… sentiment, and more.

If you’re looking for detailed customer sentiment analysis and want to hear it straight from the horse’s mouth, these are excellent tools to use.

In addition to painting an unbiased picture of what your customers think about your brand, you can also track overall growth.

5. Collect Customer Profile Data

Most brands will have multiple customer personas.

As HubSpot explains , “Depending on your business, you could have as few as one or two personas or as many as 10 or 20,” with each representing a specific target customer segment.

The more fleshed-out your customer profiling becomes, the better able you’ll be to put yourself in your customers’ shoes and envision them as real people rather than as merely abstract concepts.

This is also instrumental for customer segmentation, as the data you generate should help you neatly segment customers into different groups.

To accomplish this, you’ll want to collect customer profile data and use it to create two or more personas based on things like:

• Demographic information - Age, gender, role within a company, location, etc.
• Behavioral information - Product usage, purchase frequency, features used, and interactions
• Needs and pain points - Specific issues they’re having using your product

Here are some other types of information you may have on your customer base that can be helpful for creating customer personas.

The more customer data you use, the more clear each buyer persona should become, and the deeper understanding you should have of your audience. Further, this data can spill over to customer segmentation for an added plus.

6, Forecast Future Customer Behavior

Besides using data to analyze what’s happened in the past and what’s happening currently with customers, it’s equally important to forecast future behavior.

Doing so enables you to better anticipate customer needs and take measures to ensure you effectively meet those needs to attain high customer satisfaction — something you can do by using predictive analytics.

Say, for example, you wanted to predict how long users will engage with a new product release before engagement slows down.

You could use a trends analytics report to track product engagement from a prior product release over time.

Based on your findings, you could predict with a high amount of accuracy what engagement would likely look like if you had another product release later on down the road.

If the engagement level isn’t where you want it to be, you could then strategize ways to increase it to maximize the impact of subsequent product releases.

Or if you wanted to know what subscription MRR growth was by vertical over time, you could track that to see which verticals your audience is most receptive to.

This could, in turn, guide your future decision-making to ensure you focus on the right verticals to raise customer satisfaction while also boosting profitability.

7. Measure Customer Retention

While analyzing the steps in the customer journey leading up to a purchase is incredibly important, it’s also critical that you measure what happens post-purchase — especially customer churn/retention.

A certain amount of churn is inevitable and part of the customer lifecycle. But if it gets out of hand, it can lead to a host of problems like revenue loss, lower profitability, decreased customer satisfaction, and reduced brand loyalty.

Further, it can indicate problems with your marketing strategy, sales funnel, onboarding, customer service, and the product itself.

By staying on top of customer retention, however, you can usually spot small issues before they become larger and also capitalize on opportunities.

One example of measuring customer retention is tracking the number of mobile logins over a period of time. Here’s an example.

Another is to analyze engagement and churn.

Or, you can zero in on specific areas, such as what percentage of users come back after making an in-app purchase.

There are many directions you can go, and all should help you keep churn from getting out of control while also improving the overall customer experience.

Covering All the Bases with Customer Analysis

Again, one of the most critical components of finding customer success is first understanding your customer base.

You want to know their needs, behaviors, tendencies, preferences, pain points, and more.

The most direct route to accomplishing this is by using customer analysis, as it allows you to efficiently gather the information needed to ensure data-driven decision-making.

It’s just a matter of 1) using the right tools and resources to gather your data and 2) applying customer insight to address issues and capitalizing on opportunities.

And while it usually takes some time to iron out all the kinks, the incremental progress should add up over time.

Full insight into the customer journey. No SQL required.

Get started with Woopra for free to see who your customers are, what they do and what keeps them coming back.

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Customer Analysis: Using Data to Know Your Customers Better

Knowing who your customers are and what drives them to buy your product or serviceare key to business growth and success.

Through customer analysis, companies can identify different groups of customers and the needs of those customers to help them build more personalized customer experiences, and stand out in a competitive marketplace.

What Is Customer Analysis?

Customer analysis is the process of analyzing customer data to extract insights and inform business decisions. Customer analysis uses market analysis techniques to understand existing users in order to reach new customers.

By analyzing all sorts of quantitative and qualitative data , brands can divide customers into groups based on shared characteristics, uncover customer pain points , and understand how their products or services solve customer needs. This allows businesses to create personalized experiences based on factors like buying behavior, gender, age, interest, and beyond.

Customer analysis is all about understanding the needs, satisfaction, and pain points of existing customers, so you can improve the customer experience (CX) of customers you already have and use predictive analysis to attract new ones.

Why Is Customer Analysis Important?

When you consider that it’s 5 times more expensive to acquire new customers than retain your current ones, it makes sense that you need to need to find out why they may be leaving and focus on keeping them.

No matter how much time you’ve spent on product development and testing, you won’t really know how well your product or service works until it’s out in the real world. You need to understand how your business actually serves your customers with direct surveys and analysis of customer support interactions, social media data, and online reviews.

Understanding the voice of customer and implementing a customer feedback loop are key to always staying in touch with your customers and improving products and services.

Sometimes just letting your customers know that you’re listening to them can be a huge boon to business, especially when data tells us that 58% of customers would leave a company if they don’t get the support they expect . That’s why it’s always important to close the customer feedback loop by sending a message once a problem has been solved. This will let your customers know that they’ve been heard.

Customer analysis may seem like a daunting task, but there’s more customer data available than ever, and when you put the right processes and tools in place, you can be certain that you’re doing everything possible to truly understand your customers.

5 Steps to Customer Analysis

Follow this 5-step process for customer analysis and get the results you need for top-of-class, data-driven decisions.

• Segment your customers
• Collect customer data
• Discover customer pain points
• Define buyer personas
• Define product-market fit

1. Segment your customers

The more you understand your customers and the nuances of demographics, the better you’ll be set up to complete the subsequent steps of customer analysis.

The 4 basic types of market segmentation are:

• Demographic

Demographics are all about who your customers are. The demographics you need to focus on will depend on what kind of business you have. Is your product or service generally occupation-specific or targeted to a certain age group? Different demographics include: income, age, gender, occupation, family status, and education.

Where do you customers live? And do you want to research new markets for potential customers?

• Psychographic

What are the personalities of your customers? What is specific to their lifestyles? What are their values and beliefs? Psychographic segmentation can be quite subjective because it’s often about customers’ own perceptions of who they are. But it can offer a lot of insight because it’s often quite specific to individual products and markets.

This is where hard data, like sales figures, purchase patterns, and brand awareness come in. Behavioral data should be a fairly easy segmentation because you’re either already collecting it or you can use surveys and focus groups to uncover it.

2. Collect customer data

When diving into customer analysis, data is usually just a few clicks away. This is where you want to focus on voice of customer (VoC) to understand your customers’ holistic view of your brand and your products or services.

You can pull product reviews from all over the web: Consumer Reports , TrustPilot , and ConsumerSearch , for general product reviews; TrustRadius , Capterra , and G2 , for software; and TripAdvisor , Yelp , and Google My Business for hospitality.

You might also gather your social media data and send customer satisfaction surveys at different stages of the customer journey to learn about your customers’ preferences, opinions, and experiences.

Or explore customer data that’s already in your CRM, sales database, or with your website analytics.

3. Discover customer pain points

What’s NOT working for your customers? Where do they have problems with your products or services?

Voice of customer analysis and customer feedback loop will allow you to follow the entire customer journey and find the pain points along the way.

With SaaS companies, for example, onboarding is often an issue. Maybe you just need to educate your customers more thoroughly or use FAQs and chatbots to handle their most frustrating issues.

CSAT and NPS surveys can be helpful here to ask the questions you need answered at any point on the customer journey – have them pop-up in your app or on your website after customer journey touchpoints, to find out what is causing customer pain.

When your customers are properly segmented and you’re collecting the data you need, you’ll have it covered.

4. Define buyer personas

Once you’ve followed the above steps, it’s time to create buyer personas that will guide your analysis. Different customers buy your products for different reasons, so you can create a number of personas based on buying trends and motivations.

Use the data to uncover patterns. What is the value each persona gets from your product or service?

This is where qualitative data from open-ended survey responses , product reviews, and social media responses can be particularly handy. Use quotes and opinions in your customers’ own words to help explain and define individual personas.

5. Define product-market fit

Uncovering buyer personas leads directly into market fit. What are the challenges each customer group is facing and how does your product or service help overcome them? If your company is solving the problems of some personas but not others, then the latter isn’t a good product-market fit.

You might analyze product reviews, mentions on social media, and open-ended survey responses to learn what customers like and dislike about your product. Maybe your customer service is amazing, but users feel your mobile app is hard to navigate.

Using machine learning tools can help you identify prospective customers, in email responses to your sales or marketing campaigns. Then, you can use this data to analyze your conversion rates and detect upsell opportunities. Companies like SugarCRM have already implemented AI to gain predictive customer intelligence , so they can predict when a customer is likely to convert or leave.

AI-guided machine learning text analysis tools, like MonkeyLearn not only make customer analysis a lot easier, but also help you gain insights about your customers.

With MonkeyLearn Studio , you can easily build custom text analysis tools to analyze your customer data, and visualize your customer analytics in a striking dashboard, like the one below:

How to Use Your Customer Analysis Results

Performing a thorough customer analysis is one of the most useful things you can do for your business, and it will help in myriad ways. Here’s how to put it to work across your company:

Marketing strategies

If you know who your customers are and which channel of communication they use most often, you can reach out to them in a more effective way, lowering customer acquisition costs. By targeting customers that you know will be more interested in your products (and, therefore, more likely to buy them) you can deliver messages that resonate with them.

Product development

Use customer feedback to find out what works for your customers and what your product might be missing. You may even discover new use cases and new features that you’d never considered based solely on customer feedback. Finally, prioritize your product roadmap based on your customer analysis by detecting which features or updates customers mention most often – perhaps there are features from a select group of customers that you want to prioritize.

Improve customer retention.

Knowing what drives customer satisfaction in each segment and what makes customers churn can help you design data-driven strategies to boost customer retention and brand loyalty.

By segmenting customers, you can identify which group provides a higher return for your company, allowing you to focus more on nurturing those customers.

Acquire new customers

Once you understand who your best customers are and what the ideal product-market fit is, you’ll know which are the ideal customers to target in the future. And, by following customer feedback from all over the web, you’ll know exactly where to find them.

Start Your Customer Analysis Journey

Customer analysis gives you valuable information to drive your business forward. By identifying customer segments, and understanding the needs of different customers, you’ll be able to create meaningful experiences and attract the right clients.

Knowing what influences your customers’ buying decisions can offer you valuable insights to boost sales. Also, keeping an eye on trends and understanding customer needs can help you come up with new products to serve your customers better.

Text analysis tools can help you perform customer analysis on your text data in a fast, reliable, and accurate way. Schedule a demo with MonkeyLearn and we’ll show you how to better understand your customers.

Inés Roldós

September 9th, 2020

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7 benefits of customer analytics and how it can boost your business

Diving deep into customer analytics doesn’t just benefit your business by helping you make data-based decisions to increase sales and drive product growth—it benefits your customers, too, as you learn how to better meet user needs.

But with so much data out there, it’s hard to know exactly what numbers to look for and how to take action on customer analytics to reach your business and user goals.

Last updated

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This article guides you through seven benefits of collecting and analyzing customer analytics, including how to use these insights to understand your customers’ product experience (PX) , with actionable tips on how you can make changes and updates to create user delight and drive business growth .

Ready to discover how customer analytics can help you build a better product?

Hotjar’s tools help you better understand your customers and make more informed product decisions

7 ways customer analytics help you build a user-centric product experience

A strong customer analytics process helps you understand who your customers are, how they behave, and how satisfied they are with your company or product experience.

Customer analytics are as important as financial reports in gauging the health of your business.

Let’s take a look at the most significant benefits of customer analytics to help you understand how to use them to drive business growth. Customer analytics help you:

1. Increase the efficiency of your marketing strategy

Paying close attention to the individual customer experience (CX) gives you marketing insights that take you beyond quantitative data. Diving deep into metrics like page views and time on page is great for spotting trends and identifying problem areas. 

But combining these statistics with qualitative data—such as customer feedback, interviews, or survey responses—helps you prioritize the most effective marketing tools and techniques.

Gathering quantitative and qualitative data helps your team figure out where to focus your customer analytics strategy efforts, and whether you need to trim unnecessary processes from your marketing budget or expand your customer reach more efficiently.

Use customer analytics software to capture accurate customer data and start identifying factors that will boost your company’s success.

Try to get any demographic info about your visitors that might be relevant to your particular business. You can collect some of this from your standard web analytics platforms, through your signup process, or via third parties, like targeted ad platforms. For example, if you run ads on Facebook targeted at guests who live in a particular geo-region or have certain interests, you can segment the traffic coming in from those campaigns to enrich your customer data.

2. Reduce customer churn

It’s no secret that retaining your current customers is a lot less costly than acquiring new ones. This is especially true for SaaS companies where subscription renewal is a significant contributor to sales numbers. Staying on top of customer analytics helps you reduce your customer churn rate and maintain a strong relationship with existing users.

Use Hotjar (that's us!) to deploy exit-intent Surveys and ask your customers why they're canceling their subscriptions or leaving for a competitor to eliminate your users' obstacles and frustration and ensure they're satisfied. Identifying common traits among customers who churn helps you identify users most at risk of leaving, so you can take extra steps to incentivize them to stay .

Businesses deal with clients without assessing their performance or customer satisfaction. Without a proper analysis, a business may not know where to improve and how to ensure the clients remain satisfied to be repeat buyers. With thorough customer data analytics, a business will be well set up to analyze any flaws in its deliverables. It will help cut out practices or staff behaviors that are not up to standard, and help businesses deal with clients appropriately.

3. Increase sales

When you truly understand your customers and what motivates them, it's much easier to increase your sales.

Staying on top of key ecommerce customer analytics helps you understand whether your users are making purchases, to identify what's driving or preventing transactions, so you can validate product decisions and prioritize updates.

For example, an ecommerce company looking to drive web purchases could segment customer analytics like click-through rates, newsletter signups, and conversion rates by device. If the analytics show low rates of engagement and conversion for mobile users compared with desktop, it’s a good sign you need to optimize your mobile user experience (UX). 

Once customer analytics point you toward a potential issue or opportunity, use product experience insights to prioritize actions and fixes. If your ecommerce company has low conversions on mobile, for example, watch Hotjar Recordings , filtered by device type, to see exactly where your mobile users are struggling to navigate .

4. Lower customer acquisition cost

Understanding what drives your current users to make additional purchases or renew subscriptions helps you lower the acquisition cost of new customers. For example, if you’re designing a social media campaign to target new leads and drive traffic to your website, knowing what excites your users by measuring interactions, click-through rates, and more will help you create personalized and engaging posts.

Define your user pain points and the gaps other companies may have in their product experience. Then, track customer acquisition key performance indicators (KPIs), like impressions, conversion rate, and customer acquisition cost, in your analytics to better understand your ideal customer persona and discover how you can highlight your product solution and positioning to stand out in your industry.

5. Validate product updates and features

Monitoring customer analytics is a great way to decide which product updates and features to prioritize. For example, tracking the user flow and customer touchpoints —like call-to-action (CTA) buttons, landing pages, and points of exit—on your website helps you understand what your customers are hoping to find, so you can use your insights as the basis for a new navigation feature. Feature usage metrics also give you a sense of what your users are most interested in. 

It’s also important to simply ask your users what they’d like to see , using interviews, surveys, and feedback tools.

Gauging your users’ responses to potential features or mockups helps you find issues before launch and saves you valuable time by ensuring you don’t invest resources into features customers aren’t interested in.

If you’re releasing small bug fixes or cosmetic product updates when customers have indicated that they need major crash fixes or a new navigation feature, they might seek out a competitor that offers them a better user and product experience. Closely monitoring customer analytics—and going deeper with qualitative customer insights—helps you prioritize the updates and features your customers want the most .

6. Create a more personalized customer experience

Using customer analytics helps you discover which touchpoints in the customer experience are most important for personalized marketing and how you can implement these specialized interactions across the customer journey.

Creating personalized customer content can be tricky, so it’s important to have spot-on data that will effectively drive conversions and engagement.

When businesses fully embrace integrating customer analytics into decision-making and creating a personalized customer experience, they see an improvement in important KPIs, including:

Overall customer satisfaction

Customer loyalty

Customer lifetime value

Pro tip : make your personalized marketing efforts data-led by running experiments like A/B tests and changing key conversion variables.

For example, use Heatmaps to test your website changes and see which page version works better and increases user engagement. A data-informed approach removes the guesswork from your decision-making and tells you which page elements lead to improved conversions.

Hotjar Heatmaps help you see what attracts attention or what gets ignored on your site or digital product, so you can optimize your content for your users.

7. Better customer service

Great customer service is crucial to create satisfied and loyal users, brand credibility, and product advocacy . But how can you determine which areas of your customer service experience you need to improve? You guessed it—customer analytics.

Metrics like overall ratings, customer satisfaction scores (CSAT), customer retention rates (CRR), average support ticket time, and more helps your team determine where your support for your users is falling short.

Hotjar’s Feedback and Survey tools are a great way to ask users how they feel about your customer service: they help you identify common pain points you need to optimize.

Start implementing customer analytics processes

Strategically using customer analytics helps you build your brand, improve your reputation for great products and amazing service, and gain a loyal customer base.

The more you keep track of customer analytics and collect insights that help you understand your customers and their needs, the better an experience you create.

Hotjar’s tools help you better understand customers and make more informed decisions about your product.

Frequently asked questions about customer analytics benefits

What are the benefits of customer analytics.

The benefits of customer analytics include:

Increased efficiency

Reduced customer churn

Higher sales

Lower customer acquisition costs

Validated product updates/features with customer-centric insights

More personalized customer experiences

Better customer service

Who uses customer analytics?

All businesses can benefit from customer analytics, but specific teams who may be more focused on customer analytics include marketing, product, sales, customer service, etc.

What is an example of customer analytics?

An example of customer analytics can be as simple as measuring how many website visitors you have, tracking which pages or elements they engage with, or monitoring your conversion rate.

Ecom customer analytics metrics

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PESTLE Analysis

Insights and resources on business analysis tools

Customer Analysis: What, How, Types, 5W Model

Last Updated: Aug 19, 2023 by Abdul Momin Filed Under: Business

What’s the one thing that matters to businesses the most? Customers. The prime objective of any business is to maximise its profits. Therefore, businesses pay special attention to customers since customers help businesses grow.

Customers keep changing their needs and preferences. So to stay relevant in the market and match their customers’ expectations, businesses have to closely examine their customers. This is why businesses use customer analysis.

By conducting effective customer analysis, businesses can increase their customer base and retain current customers for a long time. A high customer base increases the profits of a business and helps it expand.

This article is a guide to customer analysis. It discusses customer analysis and its uses in detail. Besides that, this article also sheds light on how to conduct customer analysis and its types. In the end, this article also discusses the 5W customer analysis model. So let’s start with discussing the customer analysis in detail.

What Is A Customer Analysis?

Customer analysis is collecting and analyzing customer information to gain insights into their needs, preferences, behaviors, and characteristics.

This information is used to create a customer profile or persona, which can help businesses develop effective marketing strategies and improve their products or services to better meet customer needs.

Customer analysis typically involves collecting data from various sources, such as surveys, interviews, focus groups, sales data, and social media analytics. This data is used to identify patterns and trends in customer behavior, such as purchasing habits, product usage, and brand loyalty.

Businesses can also use customer analysis to identify customer segments and target their marketing efforts to specific groups of customers. Ultimately, customer analysis aims to help enterprises to understand their customers better and create strategies to build stronger relationships with them.

By understanding what customers want and need, businesses can improve customer satisfaction , increase loyalty, and drive sales and revenue growth.

Now that we have discussed what customer analysis is let’s take a look at how to conduct customer analysis.

How To Do A Customer Analysis

Businesses analyze their customer base with the help of customer analysis. To conduct the customer analysis, several steps are required.

Analyze The Data

A robust analytics system is crucial for businesses as it provides valuable data on customer behavior .

However, businesses should also look beyond their current customer base to gain a complete understanding of their target audience . By doing so, they can obtain insights into the specific types of individuals who are most likely to purchase from them.

Businesses use various data collection methods , such as surveys, to gather this additional data. For example, the data collected in the customer analysis is qualitative and quantitative. These surveys allow them to reach individuals who may not be current customers but fit the profile of their ideal customers.

By gathering information on these individuals, businesses can better understand their needs, preferences, and buying behaviors to tailor their marketing strategies and products accordingly.

Customer Segmentation

Customer segmentation is dividing customers into groups based on common characteristics or behaviors. By segmenting customers, businesses can better understand and target different groups of customers based on their specific needs and preferences.

Businesses divide customers on various bases, such as demography. This involves grouping customers based on demographic factors such as age, gender, income, education level, and marital status.

For example, a business that sells baby products may segment its customers by age, targeting new parents with infants, toddlers, or young children. Moreover, the division is also done based on the customers’ behaviors.

For example, a business that sells beauty products may segment its customers by purchasing habits, targeting customers who frequently purchase high-end products or have shown interest in specific types of products.

Besides these, other segmentations are also used, such as psychographic and geographic segmentation.

Identifying Customers Needs

Identifying customer needs in customer analysis means understanding customers’ specific requirements and expectations when interacting with a business.

This involves gathering information about the problems, desires, preferences, and pain points that customers experience and using this information to improve the customer experience and meet customer expectations.

There are different methods through which the needs of customers can be identified. For example, businesses use reviews, surveys, and customer interaction tools to learn about customer needs.

Data collected in this step would be qualitative as well as quantitative. Once businesses have identified customer needs, they can use this information to improve their experience, develop new products and services, and tailor marketing strategies to meet customer expectations.

For example, a business that identifies a need for faster shipping times can offer expedited shipping options or partner with a logistics provider that can provide more immediate delivery. This can help improve customer satisfaction, increase customer loyalty, and drive revenue growth.

Develop Customer Profiles

Creating customer profiles is an essential step in conducting customer analysis. Customer profiles are descriptions of your customers that include relevant demographic and behavioral information .

They help you better understand who your customers are, what motivates them, and how they behave. This can help businesses to tailor their marketing and product strategies to meet their needs better.

Businesses create customer profiles for different segments of their target audience. For example, businesses may have one profile for first-time customers and another for repeat customers. Each profile should be based on data and insights gathered through customer analysis.

Creating profiles helps businesses to understand their target audience and its behavior. This information can create targeted marketing campaigns and product offerings that appeal to each profile’s unique characteristics.

Additionally, customer profiles can help businesses identify areas where they may be falling short in meeting the needs of their customers, allowing them to make improvements to their business strategies .

Types of Customer Analysis

Now that we have discussed the process of conducting customer analysis let’s look at different types of customer analysis.

Customer Sentiment Analysis

Customer sentiment analysis is a technique used to analyze and understand customers’ emotions, opinions, and attitudes toward a brand, product, service, or specific topic.

It uses natural language processing (NLP) and machine learning algorithms to analyze customer feedback from various sources, such as social media, customer reviews, surveys, and customer support interactions.

The analysis focuses on identifying the sentiment behind the text, whether positive, negative, or neutral. The sentiment is determined by analyzing the words and phrases used in the text and the context in which they were used.

Businesses can gain valuable insights into customers’ perceptions of their brand, products, or services by conducting customer sentiment analysis.

It can help businesses identify areas where they need to improve their customer experience, product features, or customer service and identify trends and patterns in customer feedback.

Customer Feedback Analysis

Customer feedback analysis is collecting and analyzing customer feedback to understand their satisfaction level, identify areas for improvement, and make data-driven decisions to improve customer experience.

The analysis typically involves using text analytics and data mining techniques to identify trends and patterns in customer feedback. For instance, the analysis can help identify common customer feedback themes such as product quality, customer service, and pricing.

The insights gained from customer feedback analysis can be used to improve various aspects of the business, such as product design, customer service, marketing, and sales. For example, suppose customers frequently mention poor customer service. The business can improve its customer service processes and training in that case.

Customer Profitability Analysis

Customer profitability analysis is a technique businesses use to evaluate the profitability of individual customers or customer segments. The analysis is conducted by calculating the revenue generated by each customer or customer segment and subtracting the costs associated with serving that customer or segment.

Customer profitability analysis aims to identify the customers or segments most profitable for the business and allocate resources accordingly. It helps businesses make strategic decisions on maximizing profitability by focusing on the most profitable customers or segments and minimizing resources spent on less profitable ones.

The insights gained from CPA can be used to make data-driven decisions on pricing, marketing, and product development strategies. For example, a business may identify that a particular customer segment is more profitable than others and develop targeted marketing campaigns to attract more customers in that segment.

Alternatively, a business may identify a less profitable customer segment and discontinue products or services that are not profitable for that segment.

Customer Journey Analysis

Customer journey analysis is a technique businesses use to understand the customer experience from the customer’s perspective. It involves mapping out a customer’s various touchpoints with a business, from initial awareness to post-purchase follow-up, and analyzing the customer’s behavior and emotions at each stage.

Customer journey analysis aims to better understand the customer experience and identify areas where the business can improve customer satisfaction and loyalty.

By analyzing the customer journey, businesses can identify pain points, opportunities for improvement, and areas where they can differentiate themselves from competitors.

Customer Loyalty And Retention Analysis

Customer loyalty and retention analysis is a technique businesses use to understand and improve customer loyalty and retention. It involves analyzing customer behavior and identifying factors influencing customer loyalty and retention.

The goal of customer loyalty and retention analysis is to identify the customers most likely to remain loyal to the business and develop strategies to retain them. By understanding customer behavior, businesses can create more effective retention programs and increase customer lifetime value .

5W Model of Customer Analysis

The 5W model is a framework used to organize information and gather details about a specific topic or event. First, let’s discuss the 5W model for customer analysis.

This component focuses on the demographic and psychographic characteristics of customers. It answers questions such as who the customers are, what their age, gender, income, and lifestyle are, and what their needs and preferences are.

What component focuses on the products or services that customers are interested in? It answers questions such as what customers look for in a product or service, what features they find most important, and what benefits they seek.

The “Where” section focuses on the channels and locations where customers interact with the business. It answers questions about where customers shop or research products, what channels they prefer to use for communication, and where they live or work.

This section focuses on the timing of customer interactions with the business. It answers questions such as when customers are most likely to make a purchase, when they are most receptive to marketing messages, and when they are most likely to require customer service.

“Why” in the 5W model represents motivations and emotions that drive customer behavior . It answers questions such as why customers choose one product or service over another, why they are loyal to a particular brand, and why they may be dissatisfied with a product or service.

Customer Analysis: Final Word

Customer analysis helps businesses in analyzing their actual and potential customers. Businesses gain insights into customers’ needs, preferences, behaviors, and characteristics by conducting customer analysis.

After gaining the insights, businesses match customers’ preferences in the market to stay relevant. In this article, we discussed the significance and the customer analysis process.

Moreover, this article also discussed five different types of customer analysis. These analyses provide further detailed information about customer and market loyalty, journey, feedback, profitability, and sentiment.

In the end, this article analyzed the 5W model of customer analysis. In this section, all five components, who, what, where, when, and why, were analyzed concerning customers.

Businesses often use customer analysis since knowing customers deeply help businesses to increase their market share and maximize their profits.

• Perspectives
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What Is Customer Analytics? Customer Data Analysis 101

Learn more about customer analytics—the practice of analyzing customer data across channels.

It's easy for brick-and-mortar stores to know their customers and anticipate their needs, but it’s a lot harder to understand customer behavior, especially when their only contact with you is through a digital touchpoint like your website.

Luckily, it’s still possible to learn more about your customers through customer analytics .

That said, it’s important to collect and analyze customer data the right way. Identifying trends in customer behavior and staying compliant with data privacy regulations can be challenging without the right tools.

When you collect and analyze your customer data using a digital analytics tool, you can provide personalized customer experiences , make product decisions, and monitor customer sentiment toward your company.

• Customer analytics is the process of looking at customer data from various channels to get a clear picture of your audience’s characteristics and behavior.
• There are different types of customer analysis, such as user behavior analysis, attitudinal analysis, and demographic analysis.
• Use the insights from your customer analysis to improve your products, services, or business processes.

Introduction to customer analytics

Customer analytics is the practice of analyzing customer data across channels to better understand customer behavior and make more informed business decisions.

With data from behavioral analytics —which involves collecting customer behavior data through surveys, interviews, focus groups, and analytics tools—businesses get valuable insights into customer preferences, buying habits, and in-product experiences.

Using customer analytics helps organizations develop more informed marketing and product strategies, optimize the customer experience, and improve customer retention .

Types of customer data analysis

To get the information you need to make sales, marketing, and product decisions, you’ll need to run different types of customer data analysis.

Demographic analysis

A demographic analysis focuses on the characteristics of your customers, like age, gender, income, and location. You can use this information to help identify customer segments and target markets, tailor marketing campaigns, and create products or services for specific audience needs.

Behavioral analysis

With behavioral analysis , you can closely examine how your customers interact with your company. For example, you might analyze user behavior to see how your audience uses your mobile app or product or what makes people abandon their shopping carts. You can also see how often customers purchase and how long they remain customers. This type of analysis helps you find patterns in customer loyalty and purchasing behavior.

Attitudinal analysis

Attitudinal analysis, or sentiment analysis, provides insights into the emotions or beliefs that customers have about your company and its products. It uses natural language processing tools and machine learning to gain insight into overall customer satisfaction to help shape your marketing and customer service strategies. For example, if one of your products gets negative feedback, you can analyze customer reviews for patterns to see why they’re dissatisfied.

This type of analysis also helps you be proactive about customer churn . If you notice negative patterns in how a customer communicates with your company, you’ll know it’s time to step in and address any issues before they end their customer journey with you.

How customer analytics works

To begin analyzing information about your customers, you’ll need to collect data from various sources and consolidate that information into a database

Gather first-party data from your customers, whether it’s through surveys, questionnaires, feedback forms, or your social media pages. You’ll also want to collect information from your digital analytics platform to look at website metrics (bounce rates, traffic sources, and new signups) and product metrics (activation rates, monthly active users, and customer lifetime value).

Make sure you also have a customer database ready to store your customer data in one spot. These databases help you comply with data privacy laws and secure customer data. Additionally, they provide data visualization tools so you can easily spot trends and correlations without sifting through volumes of data.

Once you’ve identified your key customer data points, you’d normalize the data across all data sources to ensure it’s consistent and comparable. This could involve standardizing data, such as changing all dates to a single format or merging data from many sources into a single database. Then it’s time to analyze your customer data to understand their needs and preferences.

Take your analysis further by using customer segmentation to organize customers into groups (“ cohorts ”) based on certain characteristics, like demographics, devices, or in-product behaviors. Using customer segments makes it easier to personalize your marketing and provide a seamless omnichannel experience.

Common data analysis tools

Data analysis tools can help you perform accurate and effective customer analytics. Some of these tools include:

• Amplitude : This Digital Analytics Platform helps companies understand user behavior across mobile apps, websites, and other digital touchpoints. It also enables you to build customer profiles and create segments to target your audience with the most relevant content.
• Tableau: Use this data visualization and business intelligence software to create interactive visualizations and dashboards.
• Microsoft Power BI: This business intelligence platform helps build interactive dashboards and reports from data sources. It offers features like data modeling, data visualization, and data exploration.

Applications for customer data analytics

Customer data analytics provides your business with powerful knowledge, but what can you do with that information once you have it? Your analysis can help you make data-driven decisions across your organization, including:

• Marketing: Use customer segmentation to build personalized marketing campaigns that resonate with each segment of your customer base. For example, you might send customers who are least likely to make a repeat purchase a discount to encourage loyalty and conversion.
• Products: Use product analytics data to gain insight into how to improve your product or service. For example, you might find users consistently dropping off before completing onboarding. You could use this insight to A/B test new onboarding journeys that increase activation.
• Customer service: Say you do a customer churn analysis and find several of your customers are at risk of leaving. This is a sign that your customer service agents or customer success managers need to intervene.

Challenges in customer data analytics

While customer analytics gives you valuable insights, it’s not without challenges.

If you’re not using a data analytics tool to help you comply with data privacy laws, then you have a lot of data and personally identifiable information (PII) to manage on your own. Some of that can easily slip through the cracks, and you’ll face stiff penalties.

Laws like the General Data Protection Regulation (GDPR), which is a data privacy law in the European Union, help protect customer privacy and require organizations to take appropriate measures to protect the personal data they process. Each GDPR violation can cost you up to 20 million euros, or 4% of your annual revenue, depending on which is greater. Data privacy will likely keep evolving, so you need a solution that can pivot with your company when that happens.

Analyze customer data securely and effectively with Amplitude

With Amplitude, you’ll gain the insights you need to make smarter, data-driven decisions about your business, products, and customers. Easily visualize trends in your customer data and run analyses to improve your bottom line. Our platform also provides the tools to comply with GDPR and other data protection laws. Sign up for a free account today to start analyzing your customer data.

About the Author

More best practices.

What is customer analytics? Definition, types & examples

Ever wondered what really goes on in the minds of your customers? Imagine having a roadmap that not only navigates through their preferences and behaviors but also guides your marketing and sales strategies directly towards boosting your revenue.

What is customer analytics?

• Types of customer analytics

Applying customer analytics

Types of customer data.

• Stages of customer data collection

Implementing customer analytics

Challenges of customer analytics.

• Frequently asked questions
• What is behavioral data & why is it important?
• What is a heatmap? How to create and analyze them
• What is user analytics? An in-depth guide to user analysis
• What is product analytics? Your ultimate guide

Say hello to customer analytics, offering a treasure trove of insights from a mix of sources. But this is not just about collecting data; it's about unlocking the stories hidden within. By understanding the journey your customers take, you're equipped with the tools to refine your offerings, elevate their experiences, and yes, tackle the inevitable challenges like data quality, privacy concerns, and the seamless integration of this knowledge into your strategies.

Key takeaways

Customer analytics helps businesses understand customer preferences and behaviors to optimize marketing and sales strategies.

Data collection and analysis are essential for identifying patterns and trends in the customer journey.

Businesses must address data quality, privacy, and integration challenges when using customer analytics.

Customer analytics describes the process of systematically examining and interpreting data related to customer behavior, preferences, and engagement patterns.

It involves leveraging various analytical techniques, including data mining, predictive modeling, and machine learning, to uncover insights that inform strategic decisions.

Analyzing customer interactions across multiple touchpoints and platforms allows businesses to optimize marketing strategies, enhance customer experiences, and drive sustainable growth.

It's essential to gather data from various sources such as websites, mobile apps, social media, and customer feedback surveys. By analyzing this information, you can identify patterns and trends that reveal valuable information about your customers.

6 types of customer analytics

Customer analytics empower teams to make informed business decisions, personalize customer experiences, and drive loyalty and growth.

Let's look a bit deeper into six key types of customer analytics that can transform how you connect with your audience.

1. Customer acquisition analytics

Customer acquisition analytics focus on understanding how new customers find and choose your business. These insights help in optimizing marketing strategies and increasing the effectiveness of acquisition campaigns.

For example, a business might analyze data from various marketing channels to identify which ones are most efficient in attracting new customers, enabling a more targeted allocation of resources.

2. Customer segmentation analytics

Segmentation analytics break down your customer base into distinct groups based on shared characteristics like demographics, purchasing behaviors, or product preferences . This type of customer data analysis allows for more personalized marketing efforts.

For example, a retail company might use segmentation analytics to identify a group of high-value customers who frequently purchase premium products and then target them with specialized offers.

3. Customer engagement analytics

Engagement analytics measure how actively involved your customers are with your brand across various touchpoints. This can include website visits, social media interactions, and email opens.

For instance, a software company might track how users interact with its application features to determine which ones are most engaging and thus prioritize future development efforts.

4. Customer satisfaction analytics

Satisfaction analytics assess how happy customers are with your products, services, and overall brand experience. Surveys, feedback forms, and review analyses are common methods used.

An e-commerce platform could analyze customer reviews and ratings to identify areas of improvement in product quality or customer service.

5. Customer retention analytics

Retention analytics focuses on understanding why customers continue to choose your brand and what factors contribute to loyalty. This includes analyzing repeat purchase rates and customer lifecycle behaviors.

A subscription-based service might use retention analytics to identify the key factors that lead to subscription renewals and focus on enhancing those aspects.

6. Customer lifetime value analytics

Customer Lifetime Value (CLV) analytics predict the total value a customer will bring to your business over the entirety of their relationship with you. This helps in identifying the most valuable customers and optimizing resource allocation accordingly.

A financial services company might use CLV analytics to prioritize relationship management efforts for clients projected to have the highest long-term value.

To get started, you can use customer analytics to improve your business and:

Identify your target customers : By analyzing customer data , you can identify the demographics and preferences of your most valuable customers and focus your marketing efforts on them.

Optimize your marketing campaigns : Use the insights gained from customer analytics to tailor your campaigns, ensuring that they resonate with your customers and drive engagement.

Personalize your customer service : Analyzing your customers' preferences and behaviors will allow you to offer a more personalized experience, making them feel valued and boosting loyalty.

Track your social media campaigns : With customer analytics, you can track the performance of your social media campaigns and adjust your strategies accordingly.

Every interaction with a customer, from logging into your website to the more detailed aspects of their profile, is a data point. This data is invaluable in understanding who your customers are and how they interact with your brand. Let’s simplify and look at the main types of customer data and why they matter.

Personal data

Personal data covers the essentials about your customers, encompassing information that identifies an individual or is directly associated with them. It's straightforward but critical for personalizing experiences and ensuring transactions are smooth.

Examples of personal data include:

Name, birth dates, street address

Email address

Payment details

Engagement data

Engagement data (or interaction data) tracks how customers interact with your brand and content across various platforms. It’s about seeing where and how customers engage most.

Examples of engagement data include:

Email open rates

Click-through rates

Social media likes and shares

Behavioral data

Behavioral data reveals the actions and patterns of customers as they interact with a company's products or services. It allows teams to predict customer behavior , understand preferences, and improve product offerings.

Examples of behavioral data include:

Website visits and browsing behavior

Purchase history

App usage statistics

Attitudinal data

Attitudinal data captures customers' feelings towards your brand, including satisfaction levels, sentiments, and their purchase criteria. It’s about getting into the minds of your customers to align your offerings with their expectations better.

Examples of attitudinal data include:

Purchase criteria

Customer satisfaction

User sentiment

Stages of collecting and storing customer data

Let's distill the customer data journey into four main stages to understand your audience's preferences and actions clearly.

Data collection

To start collecting customer data, you need to gather information from various touch points such as websites, apps, social media, and customer surveys. This will help you understand your customers' preferences, behaviors, and needs.

Some valuable sources of customer data include demographic details, purchase history, and website behavior metrics. Be sure to use various methods like online surveys, behavior tracking tools, and preference indicators to gather a comprehensive view of your customers.

Data sorting

Once you have collected the data, it's important to sort and filter it in order to focus on the most relevant information. You can segment your data based on factors like demographics, purchase history, and user preferences. This will help you target specific customer segments and tailor your marketing strategies

Make use of predictive analytics to forecast future customer behaviors and preferences, making it easier for you to engage with your audience effectively.

Data storage

Properly storing the customer data you've collected ensures its security and accessibility. Various customer data platforms (CDPs) are available that are specifically designed to store and manage customer data securely. By using a CDP, you'll be able to access and consolidate data from various sources, making insights easier to uncover.

Be sure to always comply with data privacy regulations in your region, like GDPR (for Europe) or CCPA (for California), when handling customer information.

Data analysis

The final step is to analyze the collected and stored data, so you can make informed decisions that benefit both your customers and your business. Metrics such as Customer Lifetime Value (CLV), Net Promoter Score (NPS), and user engagement can provide valuable insights.

Tools like Google Analytics, Adobe Analytics, and Fullstory are useful for understanding customer behavior data on your site or app.

Getting started with customer analytics is about understanding your customers better and ensuring your strategies align with their needs.

Let's take a quick look at making the most out of customer analytics to improve those all-important customer relationships.

Best practices and strategies

When implementing customer analytics in your business, it's important to follow four simple best practices to ensure success.

Know your customers: Segmenting customers based on their behavior patterns, demographic data, or product preferences, you can tailor your campaign to suit their individual needs and preferences.

Keep your customers close: Customer retention strategies also play a crucial role in optimizing your data analytics efforts. By identifying trends, patterns, and triggers that lead to customer churn, you can take proactive steps to prevent it, increasing customer loyalty and reducing customer acquisition costs.

Visualize your data: When it comes to data visualization, don't underestimate the power of a well-designed dashboard. Clear and concise visuals will allow you to understand your customer data better, identify opportunities, and communicate your insights more effectively with your team members.

Valuable integrations and tools

Effective customer analytics strategies require seamless integration of various tools and technologies. A well-designed customer analytics tools ecosystem will help you collect, analyze, and report on customer data more efficiently.

Consider incorporating popular customer relationship management (CRM) solutions that can better manage customer data across various touchpoints.

Additionally, investing in tools that specialize in predictive customer analytics, for example, can give you insights into future customer behavior, allowing you to make data-driven business decisions for marketing campaigns, customer retention, or product development.

Implementing customer analytics comes with its challenges. Here, we'll outline three key obstacles and offer strategies to navigate them effectively, keeping your analytics efforts compliant and impactful.

1. Cookies and privacy concerns

With the increasing reliance on customer analytics, it's crucial to ensure that your organization is managing cookies and user data responsibly. This is because these tools often involve the collection of personal data, which can raise privacy concerns among customers.

Always be transparent about the data you're collecting and how it's being used. Furthermore, it's essential to comply with relevant data protection regulations, such as GDPR and CCPA, to avoid facing potential legal issues.

2. Security and ethical considerations

Another challenge in customer analytics strategies involves addressing security concerns. Ensuring that your customer's personal data is kept safe and secure should be a top priority.

Invest in robust cybersecurity measures and follow best practices for data management and storage. In addition to security, it's critical to consider the ethical implications of customer data usage, such as avoiding biased algorithms or discriminating against certain customer segments.

3. Cost and ROI analysis

The cost of customer analytics software can vary, making it essential for businesses, particularly small ones, to assess the potential return on investment (ROI) before committing to these strategies.

Consider factors like the cost of employing data analysts, software, and infrastructure needed to support your analytics initiatives.

When assessing the profitability of your customer analytics efforts, keep in mind that it may take time to see tangible results. Factors that can contribute to a higher ROI include increased customer loyalty, improved targeting for marketing campaigns, and enhanced decision-making.

Here are some tips to help you better assess the ROI of your customer analytics investments:

Establish clear objectives and metrics for your analytics efforts.

Regularly monitor the impact on key performance indicators (KPIs), such as revenue, retention, and customer satisfaction.

Continuously evaluate and adjust your analytics strategy as needed.

Collaborating with a customer data analytics partner can also provide access to advanced capabilities and insights, allowing for more effective data-driven strategies.

Customer analytics holds the key to personalized customer experience

Getting into customer analytics is pretty straightforward—simply put, it’s all about understanding your customers better to make smarter business decisions.

It's like having a direct line to what your customers are thinking and wanting. Sure, there's a bit of a learning curve with the tools and data, but the insights you gain are invaluable. They help you fine-tune your marketing, improve your products, and ultimately, keep your customers coming back for more.

Think of it this way: every bit of customer feedback, every purchase, and every click they make is a piece of the puzzle. And while piecing it all together might seem challenging, it’s definitely worth it. The better you understand your customers, the more you can tailor your offerings to meet their needs.

Frequently asked questions about customer analytics

What's the difference between customer analytics and product analytics.

Customer analytics focuses on understanding customer behavior and their preferences throughout the buying process, while product analytics allows product managers assess how users interact with a product and it's features.

What are the benefits of using customer analytics in business?

Leveraging customer analytics helps businesses pinpoint what keeps customers coming back (or what's making them leave), enabling you to fine-tune your product for better customer engagement and loyalty.

How does consumer data analytics differ from traditional market research?

Consumer data analytics provides real-time insights based on your product's actual user behaviors, while traditional market research relies on surveys and focus groups, often reflecting self-reported behavior.

What customer metrics should I track?

While there are many customer metrics you could track to understand various aspects of your business, consider focusing on key indicators like customer satisfaction scores (CSAT) for immediate feedback on your offerings, net promoter score (NPS) to measure loyalty and referral potential, customer lifetime value (CLV) for insights into long-term profitability, and churn rate to grasp retention challenges.

Customer Analytics: 6 Important Types and How to Use Them

What makes your customers tick? This might sound like a simple question, but every business knows it’s not. On the contrary, you need robust customer analytics to understand complex customer behavior – especially in a digital world where customers go through multiple stages and are willing to conduct a whole load of research to determine what to buy and when.

What are customer analytics?

Customer analytics (or consumer analytics for B2C) is the process of collecting and analyzing behavioral customer data across a range of channels, devices, and interactions. These analytics give you the insight necessary to form strategies, products, and services that your customers will want to engage with.

For all types of customer analytics, you may need to use techniques like data collection and segmentation, modeling, data visualization, and more.

Want to learn more? Then read on to see the importance and overarching categories of analytics, as well as six types of customer analytics that can work well for your business.

The importance of customer analytics

Some business benefits of the different types of customer analytics are:

• Higher customer satisfaction and retention
• Lower lead generation and acquisition costs
• Increased sales and revenue
• Better brand awareness
• Increased user/customer engagement

That’s because data tells you what you need to do to achieve each and every revenue and growth goal — from how to encourage customer engagement to how to promote customer loyalty. You can’t improve what you can’t measure, as the saying goes. 

And this is especially because consumers are more informed and selective than ever. To truly understand them, and their needs, you need the right data.

It’s no coincidence that successful companies are heavily data-driven. According to McKinsey, companies making intensive use of customer analytics are 2.6 times more likely to have a significantly higher ROI and three times as likely to generate above-average revenue growth than competitors.

What can you do with customer analytics?

Once you begin tracking and analyzing customer data , you can use them to answer questions about customer behavior and make important business decisions. For example, you can identify ways to:

• Increase personalization (both of content and product)
• Send the right message at the right time
• Focus the right campaigns to the right audience
• Make sure experiences throughout the customer journey are positive
• Aid product development, and marketing and sales as a whole

One thing to consider is, given the many different types of analytics (that we’ll see below), you’ll need buy-in from multiple teams and data from a range of disparate sources to complete the full picture. To do this, you can use the unified customer view that customer experience platforms offer .

The future of customer analytics

Find out why omnichannel views and flexibility is the best approach for customer experience analytics.

The 4 main categories of customer analytics

Here are the four categories of analytics with customer analytics examples:

• Descriptive analytics . Gives you insight into past customer behavior. (Example: 30 percent of customers returned product X within a month of purchase).
• Diagnostic analytics . Helps you understand the “why” behind customer behavior. (Example: 50 percent of customers think product X is not what they expected).
• Predictive analytics . Helps you predict future customer behavior. (Example: In the fall of 2020, purchases of product X are expected to decline).
• Prescriptive analytics . Provides suggestions on how you can influence or address customer behavior. (Example: Social media campaigns and online ads can increase sales of product X by 25 percent).

The different kinds of customer analytics always fall under one of these four categories.

6 useful types of customer analytics

• Customer journey analytics
• Customer experience analytics
• Customer engagement analytics
• Customer lifetime analytics
• Customer loyalty and retention analytics
• Voice of customer analytics

1. Customer journey analytics

This type of customer analytics focuses on understanding the customer’s interactions with your brand – from their initial research on your product or service to the actual purchase and beyond.

That’s why they may involve a mix of data points from different interactions. For example, organic and non-organic traffic to your product pages contains insight about the initial stages of the customer journey: research and information gathering. Shopping cart abandonment rate can tell you how many customers leave their shopping cart before completing a purchase.

Think about what metrics would help you evaluate the particular customer journey steps you’ve identified as important to your business.

Map the customer journey

Learn how to map the buyer's journey from the awareness stage to beyond purchase.

2. Customer experience analytics

Customer experience analytics shed light on how your customers feel when they interact with your brand. An important aspect to these metrics is to do with customer support (e.g. time to resolution) and customer onboarding (e.g. user adoption and time to value). If you’re using a platform to track support tickets or email and live chat, you’ll probably have easy access to customer support metrics.

Another part of customer experience is CSAT scores. In other words, how satisfied your customers are with your services (think diagnostic analytics). CSAT surveys are easily administered via email or via software like Delighted and ChurnZero after important events (such as training or purchasing). CSAT surveys help you, in part, evaluate the quality of your customer onboarding, too. 

You may also find analytics like customer effort scores useful to gain insight into customer experience.

Pay attention to qualitative data as well. For example, if a customer sends you a complaint email, reply appropriately and then note down the complaint (even an excel would do). If you continue to get similar complaints, report on them to discuss actions.

3. Customer engagement analytics

Customer engagement analytics may be divided into two categories: analytics for engagement with your product/service and analytics for engagement with your brand (e.g. web analytics). 

Customer success teams may track user engagement with your product (e.g. usage metrics ), but engagement marketing is also common: investigating and influencing the relationship of your brand with interested customers.

For example, you could segment your website visitors, and see how they interact with your content and calls-to-action, and their navigation paths, and then target them with personalized ads or email content based on that. Email marketing metrics, like click rates and click through rates, as well as social media engagement, can give you insight into how to increase customer engagement, too. 

Bear in mind that customer engagement is relevant to every stage of the customer journey, so it’s natural the two types of customer analytics will overlap.

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4. Customer loyalty and retention analytics

This type of analytics measures how loyal your customers are. How many of your buyers are repeat customers ? What percentage of your customers churn? These metrics tell you whether your customers like you more than other similar businesses.

Perhaps the most popular way to measure loyalty is the NPS (Net Promoter Score) survey. It’s the tried and tested “would you recommend us to a friend” question. If you’re thinking of implementing NPS surveys, check out our Ultimate Guide to NPS .

Also, customer churn and customer retention are metrics that could point to big problems down the line. Combine them with customer experience metrics to find ways to create proactive customer retention programs .

5. Customer lifetime analytics

In a broad sense, customer lifetime overlaps with customer journey and customer experience. But, an important additional metric in this type of analytics is the Customer Lifetime Value (CLTV). This metric shows you how much revenue you can expect from a single customer throughout the entire business relationship.

The way to calculate this metric may be different depending on your business – sometimes, bringing in a consultant may work better in identifying the right formula for your company. But, a simple way to calculate this metric is by multiplying your average retention rate by the average number of purchases, and then multiplying the product by the average deal total.

Of course, there are more complex (and therefore more accurate) ways to calculate this metric. Also, segmenting this metric based on type of customer helps you see which ones are more valuable, and should be the audience of your more expensive marketing campaigns.

You can use CLTV in different ways to inform your decisions. For example, if you see it decline, that signals an issue with repeat customers. If it’s lower than what you spend on acquisition and marketing campaigns, then you’re probably spending too much without getting enough back.

6. Voice of customer analytics

The voice of the customer is a self-explanatory idea: it’s what your customer says that’s relevant to your business. With these analytics, you capture customer opinions, preferences, and expectations.

Voice of the customer analytics also refers to CSAT and NPS surveys, social media posts and interactions, and really anything that lets you listen to your customers’ thoughts. Take a structured approach to surveying customers by following best practices like asking the right questions, digging into demographics, and choosing the right medium.

Customer analytics tools

Here are some tools that can help you gather and report on customer data:

Acquire is a customer engagement platform that helps you keep interaction history with customers in one place , so you can have data from multiple channels (including live chat, website, social media, chatbot, and more) at your fingertips. It also keeps data of your team’s actions (e.g. time to respond to customer questions) to identify better ways to serve customers.

Kissmetrics

Kissmetrics helps you understand user behavior across your website (e.g. looking into which elements bring more conversions).

IBM Watson Customer experience analytics

IBM Watson Customer experience analytics helps you visualize and analyze the customer journey.

Google Analytics

Google needs no introduction; GA has a robust array of tools to measure traffic, behavior, and attribution on your website.

Hootsuite analytics help you monitor your social media performance and get valuable insights for future campaigns.

Hotjar helps you drill down to web visitor behavior by giving you tools like heatmaps, recordings, and more.

Best practices of using customer analytics

If you want the best possible results from your data, make sure you:

• Look at customer interactions from an omnichannel point of view . As we mentioned, there’s software that provides a single customer view, but you should also look into data from a range of different and relevant sources.
• Pay attention to qualitative data . Some customers provide their opinion freely and make it really easy for you to spot problems and find solutions. Don’t ignore any customer voice, whether it’s a reply to your CSATs or a proactive email.
• Make predictions and test solutions . Don’t stay at the diagnostic stage – make active predictions and choose different solutions to test out. Especially when it comes to customer engagement, you may need to evaluate various options to find out what actually sticks.

How do you use customer analytics? What benefits have you seen? Let us know in a comment below.

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How to Write a Customer Analysis Section for Your Business Plan

Free Customer Analysis Template

Ayush Jalan

• February 12, 2024

A successful business idea equips customers with the tools necessary to help them reach their goals and fulfill their needs—professional or personal. To create such products and services that meet (and exceed) your customers’ expectations, you need to study their personas via customer analysis.

Customer analysis is a vital part of your business plan that helps you identify, define, and understand your customer base. Analyzing your customers is also crucial for creating a successful marketing plan, as it helps you communicate better with your customers.

In this article, you will learn how to conduct a customer analysis section for your business plan paired with a customer analysis example to help you create customer personas to study their personality traits, goals, challenges they face, and more.

What Is Customer Analysis?

Customer analysis is a comprehensive understanding of your customer base. It helps identify and describe your ideal customer. Through this in-depth analysis, you determine their needs, challenges, goals, and other important considerations. Given this info, it then helps you understand how effectively your products cater to them.

It further helps you optimize your strategic marketing process to create targeted advertisements, customize and prioritize specific features during product development, and make adjustments in your current business plan to align with your customer’s ever-changing demands.

How to Write a Customer Analysis Section

Writing a customer analysis includes extensive research and collecting data from various sources. This data consists of qualitative and quantitative aspects which help you write an accurate customer analysis for your business plan.

Writing a customer analysis has four main steps:

Step 1: Identify your customers

The primary step is to identify your potential customers and define their specific characteristics about them. The attained factual information is segmented into the following categories:

• Demographic: Age, gender, income
• Geographic: Location, type of area (Rural, suburban, urban)
• Psychographic: Values, interests, beliefs, personality, lifestyle, social class
• Technographic: Type of technology the buyer is using; tech-savviness
• Behavioral: Habits, frequent actions, buying patterns
• Industry (For B2B): Based on the industry a company belongs to.
• Business size (For B2B): Size of the company

To obtain the above data, a great place to start for established businesses is your customer database. If you aim to expand this information, you can use your existing communication channels to gather further details through surveys.

If you are a startup, conducting an audience analysis  might seem impossible as you don’t have an existing customer base. Fortunately, there are numerous ways through which you can study your potential customers. A few of them are:

• Identifying who would benefit from your product/service
• Analyzing your competitors to understand their target customers
• Using social media to prompt potential buyers to answer questionnaires

Want to create a Customer Persona in Easy Steps?

Generate valuable customer insights in minutes with Free Customer Persona Generator .

Step 2: Define the needs of your Customers

Now that you have identified your customers, the next step is to understand and specify their needs and challenges. This is the step where you need to go hands-on with your research. Getting to know your customers’ needs helps you determine whether or not your product or service hits the mark.

To understand the needs of your customers, you can adopt the following approaches:

1. Engage directly with potential Customers

A very reliable way to get to know your customers is to simply ask them, either in person or on a call. You can reach out to your customers, conduct one-on-one interviews, create focus groups, and invite buyers to test your new products. You can collect an ample amount of data through these techniques.

However, we recommend prioritizing accuracy over the quantity of data.

A technique that can help you get a deeper insight into your customer’s needs and opinions is the five whys technique . While practicing so, be mindful of the way you conduct the interview. It is essential to keep the customers in a comfortable and conversational environment to attain accurate answers.

2. Collect data from your Customer support

Customer support is the place where you can find feedback and criticism given by your customers. Analyzing this data helps you understand the pain points of your customers. You can further elaborate on this data by interacting with the customers who had issues with your products.

3. Run surveys and mention statistics

Talking to your customers helps you get qualitative information that you can use to alter your product or services according to your customers. The next part is to attain quantitative information, in other words, presenting numbers to support the previous data.

Conducting surveys is one of the commonly used methods for quantifying information. You can conduct in-app surveys, post-purchase surveys, or link surveys in email and apps, etc.

The second method is by collecting statistical data to support your conclusions from the interviews. These include stating studies related to customer choices, results from popular surveys, etc.

Step 3: Create a Customer Persona

Now, it’s time you present the information using a customer persona. A customer persona is a representation of a segment of customers with similar traits. Creating customer personas helps you process the data more efficiently.

You can use customer persona templates that are available online. To help get you started, we have created a customer persona example.

Customer Persona Example

Customer profile example of an internet service provider:

• About: A lot of customers remain at home and have a minimal and easy-going lifestyle. They need high-speed, interruption-free internet access.
• Demographics: Age is between 30 and 40, has a laid-back lifestyle, lives in suburban areas, and the income range is between $10,000 to $40,000.
• Professional role: Shop owners, employees, freelancers, etc.
• Identifiers/Personality traits: Introverts, like routines, makes schedules prefer online shopping, and stick with the companies they trust.
• Goals: Wants easily available service, and 24×7 customer support, prefers self-service technologies and chatbots over interacting with representatives.
• Challenges: Fluctuating internet connection while working or consuming media. Not enough signal coverage.

Step 4: Explain the product alignment to the Customer’s Needs

You’ve gathered info and created customer personas. The final step is to explain how your product or service caters to the needs of your customers. Here, you specify the solution you offer to your customers to tackle the challenges they face.

Mention the USPs of your product and its features, and they benefit the customer. Here, you also mention how your offerings make your customers’ lives better.

Create Better Solutions with Customer Analysis

Understanding your customers inside out helps you assist them better in solving their problems while also achieving success. Analyze your customers as often as required to stay updated about their ever-changing needs.

This helps you create better offerings to consistently fulfill their expectations. As a result, this builds up loyalty over time with each success.

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About the Author

Ayush is a writer with an academic background in business and marketing. Being a tech-enthusiast, he likes to keep a sharp eye on the latest tech gadgets and innovations. When he's not working, you can find him writing poetry, gaming, playing the ukulele, catching up with friends, and indulging in creative philosophies.

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Customer Analysis

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Customer analysis is a way of analyzing and using customer data while strategizing an organization’s business plan. Customer analysis is a key element of customer acquisition strategies of marketing and advertising campaigns. It provides valuable insights about your prospective customers (like their purchasing behavior, preferences, demographics, decision making , etc), so you can segment customers while running marketing campaigns of a product or service to optimize sales growth and customer retention

You can only fulfill the customer’s requirements if you know what the customer wants. When you understand your potential clients, you determine who your target customers are and decide how you will attract them to buy your products. The key objective of customer analysis is to gain insight and have a deeper understanding of buyer persona to optimize the company ’s business plan and brand positioning in a personalized and result-driven manner.

Table of Contents

What is Customer Analysis?

Definition: Customer analysis is defined as the inclusion of qualitative and quantitative research methodologies used for understanding customer’s journey and their buying behavior to optimize the decision making process to make a successful marketing plan .

It tells the customer needs of your active customers, current customers, or new focus groups to offer products and services in a customized manner for enjoying increased sales and optimized brand loyalty .

Customer analysis helps in-

• Identifying who your customers are
• Finding out their needs and their pain points
• Making profitable segments of customers as per similar traits and customers behavior
• Creating a profile or buyer personas of your ideal customer(s)
• A better understanding of purchasing decisions patterns of new customers
• Designing personalized content and marketing strategy on best-suited marketing channels
• Extended decision making by suggesting significant modifications so product or service meets customer needs

Importance of Analysis of Customer Base

Customer Analysis is described as a way to comprehend clients, customer base , or target market of a business to optimize marketing efforts and customer acquisition. It is an essential function of marketing analysis.

Customer analysis takes place when a company researches its potential customers and their requirements. Customer analytics, as well as customer surveys, are the focused studies of the customer behaviors that come into play while addressing the pain points of target audiences and boosting total sales.

Before the customer starts to buy, businesses examine certain factors to find out the buying criteria of their target audiences. Total market capacity would be among the most critical estimations. Other factors considered are the marketplace, choices, economic situation, cost elasticity, etc.

The aim is to know the preferences of the significant number of target customers and why would they choose your product. What would motivate them to purchase your product?

Through buying behavior of the customers, businesses pay attention to the ways customers focus on the products or services, and accordingly, they decide their segmentation criteria to run personalized campaigns to target audiences of specific segments like geographic location, age range, inclinations, etc.

Customers tend to select products or services that have a good brand image and are positively reviewed by other customers. Customer analysis helps in guiding businesses in fulfilling the specific needs of the target audiences.

Behavioral Analysis of Customer Base – Analyzing Purchase Process and Patterns

Customer Analysis at times needs a detailed understanding of how exactly a customer decides to buy a product. It is not as easy as it seems. It requires a thorough study of the customer’s buying patterns.

Through this analysis, a firm tries to understand the customer by determining certain elements, such as –

• The steps that the customer takes in determining the product
• The information sources are considered by the customer while deciding the product to buy
• The amount of time the customer takes to determine their purchase
• Would the customer consult other people before purchasing the product
• The person who makes the final decision
• Does the target customer seek multiple bids
• Do some customer segments need modification in product or service

Behavior analysis can additionally concentrate on activities, like the kinds of products that were bought. It identifies the number of times the products were purchased and the time interval in which they were purchased.

To distinguish the shopping patterns and preferences of the customers, one must have an answer to the following questions-

• The purpose/event for shopping?
• How many times will the customer buy the product?
• The frequency of the purchase and the time interval between two purchases?
• The total number of goods acquired?
• How much time did the customer take to decide what to buy?
• Where did the customer buy the product, and where do they use it?

Steps to Conduct Customer Analysis for Optimizing Marketing Efforts

Customer Analysis is a time-consuming task and requires a proper execution strategy . Below are the steps of customer analysis –

1. Examine your current customer database and segment customers

The first step would be to understand the data of your existing customers. The preferences of your existing customers can help you understand their likes and dislikes. They can be segregated into different sections based on their age, gender, location, choices, income, frequency of buying, etc.

Dividing the customers into different groups will help to focus on one group at a time. That would help gain the attention of the potential customers and will help in boosting the business.

Different categories in which your target customers can be segmented are-

• Demographic such as age (range), income, gender, etc
• Geographic segmentation which is location-specific
• Psychographic segments based upon values, interests, beliefs, personality
• Technographic segments that depend upon the device/platform a customer is using
• Behavioral segmentation methods associated with the habits and frequent actions
• Needs-based segmentation criteria related to the needs that a product satisfies
• Value-based segmentation generally measured by Customer Lifetime Value (CLV)
• Industry based segmentation related to the industry the customer belongs to
• Business size segments based upon the number of employees or the revenue size or the average sale size

2. Recognize your potential target market segment & valuable customers

The second step is to identify your important customers. The customers that are most likely to get attracted to your products should be your focus. Through analyzing customer behavior patterns, you can find your target customers.

As the Pareto Principle suggests, for a company, around 80% of the business comes from 20% of customers, so it is crucial to find out your valuable customers first. Some of the customer metrics that will help you in checking the patterns in your customer database to measure customer retention are-

• Customer Lifetime Value (CLV)
• Repeat Purchase Rate
• Customer Retention Rate
• Redemption Rate

You may also try some of the key methods to measure customer loyalty towards your brand. You can do this by using the following methods-

• Repurchase Ratio
• Customer Loyalty Index (CLI)
• Upselling Ratio
• Customer engagement numbers

Customer database can also be used to find out valuable insights about the personal information of target audiences to know their age and background, so you can use the right ways of communicating with those audiences.

3. Communicating with new and existing customer

 It is essential to create a bond with your customers that they do not forget. A customer stays loyal to a brand, through which they have received excellent products and their requirements were acknowledged by the brand efficiently.

You need to know what the customer wants precisely to target them. It would not be possible without talking to the customer themselves about their experience. Customers tend to trust brands that constantly improve according to the customer’s suggestions.

Some of the methods you might try to communicate with your customers during a brand meet are-

a. One-to-one interviews

It lets customers share their side of the story behind their purchase decisions as well as their issues or pain points that impact their buying behavior.

b. Customer focus groups

Focus groups incorporate a selection of customers in a room with whom businesses discuss specific topics.

c. Usability study

It is used to observe how the use of a product or service by a few customers can uncover the issues associated with a product or service of a brand.

4. Collect voices/opinions of existing and new customers from the field

Interviews may have some sort of selection biases, therefore, businesses might try to collect customer voices or opinions directly from the field. Some of the ways you can try for this are-

a. Service conversations

The customer services or support departments are perfect for channelizing service conversations with customers in the field. Here, pain points, pain points, improvement/modification suggestions can be directly taken from customers. Use of live chat or support emails can also assist in this.

b. Social media – sentiment analysis

Businesses can also use sentiment analysis on social media channels. The use of unrestricted surveys on social media forums can be quite useful in this. Different tools like Brandwatch, Lexanalytics, Critical Mention, etc can be helpful in this.

c. Review sites

Different review sites like Capterra and G2 can also provide more concrete insights in comparison to the social media forums.

d. Conduct survey

Since talking to customers as well as collecting customer voices from the field are qualitative ways of gaining customer insights , running surveys is considered a quantitative way of finding customer sentiments and inclinations. methods. Different ways of conducting surveys are In-app surveys, Long email surveys, Post-service surveys, etc.

e. Conjoint analysis

This one is known as a choice-based survey method that asks target audiences to give a value to a product or service feature by using some hypothetical questions. Some of the resources that can help you in this are QuestionPro, Conjointly, Qualtrics, etc.

5. Identify how your brand can satisfy the customer’s needs

After understanding the customer demands, the subsequent step would be to implement measures to meet the customer’s needs. Finally, resolve the issues the customer might be facing. The goal is to enhance the customer experience as much as possible.

The next step after segmenting your customers is to use all the insights from the above steps to create customer personas of your target customer(s). These buyer personas include the details about your customers’ demographics, behavior patterns, motivations , goals, pain points, etc.

Making a customer journey map as per the change in the behavior of customers at different stages of their journey is another important step businesses need to take here. You can understand the customer journey map as a visual representation of different touchpoints and interactions customers go through to make their purchases.

For creating customer journey maps, you should list all the touchpoints and all possible actions along with the charting of emotions for every action and identifying pain points for each of the customer personas.

Role of Customer Profiles in Customer Analysis

Considering several factors like age, gender, location, background, income, choices, hobbies, language, and education , businesses can create customer profiles .

Every customer will be placed in separate sections to which they belong. What these customer profiles would do is, it would help the brand to focus on a specific group at a time, recognize their needs, identify their problems and solve them.

In addition, it helps in creating a clear picture of the requirements of the customer, hence it will alleviate the tasks of creating buyer personas.

Benefits of Customer Analysis

1. boosts marketing performance via personalized content.

Customer analysis helps in determining the proper steps to be taken to build up marketing strategies. Knowing about a customer will result in understanding what would attract the customers and what wouldn’t.

2. Customer loyalty and customer retention

It is required to understand what can cause the customers to move away from the brand. Customer Analysis enables us to understand what the buyers prefer. Having loyal customers help in increasing profits.

3. Revenue Increase

Meeting the customer demands would result in the overall growth of the company. In addition, it would lead to a boost in sales which means the revenue would increase.

Conclusion!

Applying principles of customer analysis to your business will optimize your business performance and make it more result-driven in the following ways-

• Through detailed Customer Analysis, you can get to understand customer behavior and their shopping patterns, as it helps you learn about your target customer to the core.
• Dividing the customers into separate groups is beneficial since it will help in strategizing a marketing plan for a specific group at once.
• The customer data is divided based on several factors like age, location, gender, language, etc., and then their requirements are identified.
• Personalized marketing campaigns will lead to customer loyalty that will help in prospering the business.

Liked this post? Check out the complete series on Customer Management

Related posts:

• What is Customer Equity? Components of Customer Equity
• What is Customer Feedback? 7 Types of Customer Feedback
• What are Customer Insights? Importance of Customer Insights
• How to get Customer Testimonials? 9 Customer Testimonial Examples
• Customer Care: Concept, Examples, & How to have Good Customer Care
• What is Customer Relations -Ways to Build Better Customer Relations
• Customer Persona – Meaning & Best Tips Develop a Customer Persona
• What is Customer Lifecycle? Stages and Analysis
• 6 Types Of Customer Segments to Target
• What is Customer Satisfaction?

About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.

In-person on-demand session

2024 Power Couple: Your Customer Analysis Stack and Customer Journey Analytics - S114

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Director, Product Marketing, Adobe

Katie Cipollone

Principal Expert Solution Consultant, Adobe

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Adobe Customer Journey Analytics

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ABOUT THE SESSION

Using person-level insights to improve customer experiences has become overwhelming for analytics teams. A first-party customer data strategy is now a must, and new use cases, data types, and customer analysis integrations require committed resources to implement and maintain. You need a partner to help unlock your customer data for real-time decision-making and activation — a partner that can reduce workloads and drive cost efficiencies so that your team can work on projects that are strategic for your brand. 

In this session:

• Discover how the shift to a customer-led paradigm in analytics and why shifting your mindset and investing in a modernized customer analysis approach is crucial to staying ahead
• Learn how true insight democratization is a force-multiplier for productivity and profitable revenue generation across marketing and IT teams
• See what best-in-class privacy, security, governance, consent, and readiness look like in a modernized analytics stack and how you can ensure the customer remains central

Track: Analytics

Presentation Style: Thought leadership

Audience Type: IT executive, Marketing executive, Business decision maker

Technical Level: General audience

This content is copyrighted by Adobe Inc. Any recording and posting of this content is strictly prohibited.

By accessing resources linked on this page ("Session Resources"), you agree that 1. Resources are Sample Files per our Terms of Use and 2. you will use Session Resources solely as directed by the applicable speaker.

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Director, Product Marketing

Your Customer Analysis Tech Stack and Customer Journey Analytics - VS117

Nate Smith is the Director of Product Marketing for the Adobe suite of Analytics products. In his role, Nate oversees strategic marketing for Adobe Analytics and works with several of the top brands in the world regarding their analytics use cases, challenges, and business opportunities. He has been involved in digital marketing for the past fifteen years and holds a BS in Information Systems and an MBA from Brigham Young University.

Principal Expert Solution Consultant

Katie Cipollone is a Principal Expert Solutions Consultant for Data & Insights at Adobe with over 25 years of experience working in Digital Experience and Business Intelligence. Katie’s area of expertise is in Adobe Analytics and Customer Journey Analytics, solutions she’s supported since their inception. Katie works closely with Adobe customers across all verticals to uncover opportunities to drive business value through democratized, customer-centric data analysis. In her role, she routinely collaborates with Product Management, Product Marketing, and Sales Specialists on strategies for customer success. Katie was also part of Adobe’s first Value Engineering practice, and is a SME for the Retail industry, where she worked prior to joining Adobe.

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How To Increase Conversions With This 6-Step Customer Journey Analysis

Learn how to improve conversion rates for all of your marketing campaigns by tying attribution to each part of the customer journey.

This post was sponsored by DAC Group. The opinions expressed in this article are the sponsor’s own.

To create websites and marketing campaigns that will increase conversions, you must understand what your customers need and when they need it.

What inspires a prospect to connect with you or buy your product from start to finish?

How do you track it?

What should you double down on to increase your profit and ROI?

In digital marketing, one of the most potent keys is understanding the entire customer journey to make informed strategic and resourcing decisions.

But, as any SEO practitioner will tell you, it can be exceedingly difficult to gauge the impact of any one digital tactic, never mind a complex web of nonlinear user touchpoints.

So, where to begin?

In this article, you will learn how first-touch and last-touch attribution can be deployed together to reveal the contributions of each channel – including organic search – and transform your understanding of the customer journey from the initial spark of awareness to the decisive moment of conversion.

To get started, let’s travel through the foundation of multi-touch attribution: the customer journey and how to attribute each touchpoint to a sale.

The first stop is understanding the entire customer journey.

What Is A Customer Journey?

A customer journey is a marketing term that helps explain how each prospect transforms from being introduced to your brand to becoming a customer.

In essence, the customer journey is a vital tool for any website that needs to convert visitors because it provides a blueprint for creating an effective, user-centered design that directly addresses users’ needs and behaviors.

This strategic approach improves the user experience and drives higher conversion rates, ultimately contributing to your business’s bottom line.

At a granular level, every touchpoint a prospect has with your brand’s content is another step on that journey – another piece that convinces a shopper to become a buyer.

As you can imagine, each customer journey is entirely different. Every user has unique and distinct needs that are met by multiple touchpoints with your brand. These touchpoints help your prospect make the final decision to convert.

In an ideal world, your brand has hundreds of pieces of content, web pages, articles, blogs, ads, emails, social media posts, and traditional marketing assets that influence a shopper or visitor to interact with your services or buy your product.

While it’s crucial to your overall marketing strategy, individual customer journeys are too numerous to study at such a granular level.

So, to help you track and optimize what’s working within your library of marketing assets, it helps to categorize each journey into a high-level picture known as a marketing funnel. From there, you’ll gain the ability to better identify which marketing tactics transform the most visitors into customers.

What Is A Marketing Funnel?

A traditional marketing funnel consists of five layers representing a group of potential customers along their high-level customer journey.

The layers of a marketing funnel typically include:

• Consideration.
• Conversion.

Most of your new customers will start in the largest and highest part of the “funnel,” the Awareness stage. This is where they learn about your brand and is often the first touchpoint a new prospect has with your products.

From there, they may become interested in what your brand has to say or what you offer. They then consider taking steps towards purchasing from you and are ready to be nurtured into a conversion, which usually consists of a sale.

To make the most of your marketing funnel, it’s essential to know what draws the highest percentage of potential customers into your brand and what pieces convince the highest percentage of potential buyers to become buyers.

Once you have that information, you can optimize your additional content to match your top-performing pieces and earn more sales.

But how do we know precisely what content in the entire funnel caused a sale for each customer? Attribution.

What Is Attribution?

Marketing attribution is the process of tying a conversion to a piece of marketing material.

Attribution lets you see exactly which pieces of content were the most effective at convincing a visitor to become a customer.

This can be done by utilizing tracking codes, such as UTMs and pixels, phone call tracking, and dedicated URLs created with services like Bitly.

To understand what types of tracking you should implement, we first need to learn about the different types of attribution.

Which Types Of Attribution You Should Focus On?

There are many types of marketing attribution you should focus on , but the most effective to build out your first, but powerful multi-touch campaign are:

• First-Touch Attribution.
• Last-Touch Attribution.

Traditionally, you may have looked to first-touch and last-touch attribution models to optimize your campaigns for maximum impact.

As you’ll see, each approach has its benefits, but when you combine them, you create something far greater than the sum of its parts: multi-touch attribution.

Today, we’ll focus on increasing your conversions by studying a user’s first- and last-touch attribution with your brand’s marketing materials.

First-Touch Attribution: Capturing Initial Engagement

First-touch attribution places emphasis on the very first interaction a user has with your brand.

This initial touch point can occur on various channels, such as social media, organic search, or via referral links.  Common web analytics tools like Google Analytics 4 and Adobe Analytics offer customizable attribution models in their Explore and Workspace reports to track initial touch points effectively. Utilize the ‘Model Comparison’ tool in Google Analytics 4 to optimize channel valuation and inform strategic decisions.

Although it’s a long way removed from the final conversion, there are some key benefits to incorporating first-touch attribution into your digital marketing strategy:

• Understanding awareness : First-touch attribution allows marketers to gauge the effectiveness of their top-of-funnel marketing efforts. By identifying the channels that attract users initially, brands can optimize their awareness-building strategies to better “feed the funnel”.
• Budget allocation : Knowing which channels are responsible for the first interaction can help you allocate marketing budgets more efficiently. By investing in the channels that drive initial engagement, marketers can optimize their spending and improve ROI.
• Tailoring content : Analyzing the first touch point provides insights into the type of content that resonates with the audience. Marketers can then tailor their content strategies to create more engaging and relevant material for their target audiences.

Last-Touch Attribution: Closing The Conversion Loop

On the flip side, last-touch attribution identifies only the final interaction before a conversion event.

Though it’s a relatively one-dimensional metric, last-touch attribution offers various benefits:

• Conversion optimization : By focusing on the last touch point, marketers can identify the specific channels or campaigns that directly contribute to conversions. This information is crucial for refining strategies and optimizing campaigns for higher conversion rates.
• Efficient resource allocation : Just as with first-touch attribution, understanding the last touch point can help you allocate resources more effectively. Marketers can invest more in the channels or campaigns that have a direct impact on closing deals and generating revenue.
• Campaign effectiveness : Last-touch attribution provides a clear picture of which marketing effort is directly responsible for driving users to take the desired action. This insight is invaluable for measuring the success of specific campaigns and making data-driven decisions.

Best Of Both Worlds: Layering First- And Last-Touch For Holistic Insights

While each attribution model offers unique insights , combining first-touch and last-touch attribution provides a much more detailed and nuanced view of the customer journey.

The result: multi-touch attribution, allows marketers to understand the entire conversion path and make informed decisions at every stage. In other words, multi-touch attribution will supercharge your insights in various different ways.

These are six of the most common.

1. Analyze How Users Are Exposed To Your Site

When using first-touch attribution, clicks are counted based on the first channel that drove a user to the site.

Using this methodology empowers marketers to better understand the value of each channel in generating initial site exposure.

When using a typical last-touch attribution model, clicks are counted only for the last channel a user engaged with. This can often over-value contributions from channels that are heavily skewed towards the end of the conversion funnel.

Adopting multi-touch attribution blends the first-touch and last-touch perspectives, distributing credit across the customer’s journey.

This approach provides a more detailed view of each channel’s role, from initial interest to final conversion, and mitigates the risk of over-valuing the last interaction or undervaluing the first.

2. Assess The Likelihood Of A Session Converting

In typical last-touch attribution, conversion rates are based on the channel a user last interacted with.

This will tend to value late-journey channels and tactics while ignoring the impact of early-journey efforts.

By also analyzing first-touch conversion rates, marketers can better understand the value of early-journey marketing tactics and channels.

3. Determine The Impact Of Your First Entry Point On Last-Touch Conversion Rates

In this analysis, we look at the impact of early-journey marketing on last-touch conversion rates.

To do so, we compare the typical channel conversion rate to that of when organic search was the first entry point. This allows us to measure the value of deploying early-journey tactics and evaluate how they impact late-journey performance.

In this particular instance, we found that almost every channel had a higher conversion rate when organic search was the first entry point:

4. Determine The Impact Of Your First Entry Point On Last-Touch Average Order Values

Like analyzing the impact of the first-touch channel on conversion rates, analysis can be conducted to determine the impact of a user’s first entry point on their later order value.

In this instance, we found that when organic search was the first touch point, almost all last-touch channels saw an increase in average order value.

5. Evaluate The Contribution Of Early-Journey Marketing On Other Channel Revenue

By comparing revenue attribution across first-touch and last-touch, marketers can better understand how much generated revenue is attributed to another channel.

This is particularly useful in understanding the role of each channel when guiding users throughout the conversion journey.

In the below example, DAC discovered that 38% of revenue driven by first-touch organic search was later attributed to other channels:

6. Measure The Impact Of Early-Journey Content (Blog)

Oftentimes, early-journey research and informational content is under-valued when measured via last-touch attribution.

This type of content tends to have fewer immediate conversions. As such, measuring via last-touch will attribute downstream conversions to the final channel. When viewed on a longer timeline, we found that blog visitors tend to return to the site within five weeks to make purchases.

Using last-touch attribution alone, this trend would not have been identified.

Beyond analyzing whether or not blog users convert, we can also look at the impact on conversions.

In analyzing, we found that users who visit the blog convert at a higher rate, have higher page views per visit, and a much lower bounce rate.

In the ever-evolving landscape of digital marketing, understanding how customers interact with your brand is essential for success.

First-touch and Last-touch attribution models offer distinct advantages, from optimizing awareness-building strategies to refining conversion-focused campaigns. By leveraging the insights provided by these attribution models and layering them together, marketers can create more effective and targeted campaigns, ultimately driving better results and maximizing return on investment.

Image Credits

Featured Image: Image by DAC Group. Used with permission.

A results-driven organic search marketer, Bill has been in the industry for 18 years. Bill specializes in developing organic search ...

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CStore Decisions

Taking a Leveraged Approach for Consumer Analytics

By Elie Y. Katz | May 16, 2024

For convenience store owners, the ability to deliver a unified and personalized customer experience is front and center. Many chains are using scan data to complete a thorough consumer analysis, which can bring strategic advantages, and most chains understand the finer points of collecting scan data, but that’s only half the battle.

Knowing how to maximize your data analysis to drive sales, best the competition and improve your bottom line is the ultimate goal.

Scan data analysis can be effective in better targeting customers. As organizations increasingly integrate their systems, omni-channel opportunities to gather and utilize data in interesting ways to make smarter merchandising decisions.

Real-time analytics are a key part of identifying and understanding the customer across retail channels and creating the right assortment at the right time for that customer. By investing in systems that handle and interpret customer and sales data, c-stores can position themselves for greater sales opportunities while offering personalized shopping experiences to their customers.

This is where a loyalty program can have its greatest benefits. Tracking your consumers’ shopping habits allows you to target them with offers that better resonate with their preferences, which means it’s more likely to spur new sales opportunities.

Capturing consumer data, in conjunction with a loyalty program, which rewards consumers with discounts on foodservice, gasoline and other key categories throughout the store, gives companies a real-time understanding of who is shopping their stores and when. It allows chains to predict, with high accuracy, what customers are looking for throughout the day and ensures each store is ready to meet their needs.

Scan data helps identify, target and reward consumers with offers that influence future purchasing decisions. Furthermore, loyalty program integration improves your understanding of customers so you meet their needs with improved merchandise assortment, timely and seasonal promotions and precise targeting at the right time of the day. This data can even lead to updating planograms so high-impulse, high-margin items are easily accessible and more toward the front of the store.

With this in mind, Ignite Retail Technology, which specializes in implementing loyalty programs for convenience store chains of all sizes, outlined its Five Benefits of Loyalty Programs for Convenience Stores that organizations can expect the following benefits from their loyalty programs. They are:

• More Customer Data
• Use the Customer’s Voice
• Community Building
• Improved Customer Retention and Referrals
• Operating Cost Savings

More Customer Data. Once a customer joins a loyalty program, marketing teams can track their preferences, buying habits and interests. In fact, most Americans are willing to trade personal information for loyalty program perks, according to Forrester Research. Organizations can use this data to personalize outreach and customize monthly offers to different customer personas.

Use the Customer’s Voice. Rather than develop many new marketing campaigns, marketers can rely on their rewards program members to drive more business.

Organizations can ask customers to review the company or program on third-party sites. Successful loyalty programs often delight customers so much that they feel compelled to share their experiences on social media, which can bring in new business and loyalty members.

Community Building. Many people, especially loyal convenience store customers, want to feel like they’re a part of something larger than themselves, and loyalty programs can offer that. Virtual events, call center/customer service lines and online forums can foster communities and discussions from which marketing teams can receive firsthand customer feedback.

Improved Customer Retention and Referrals. Loyalty programs reward repeat customers with exclusive offers and personalized treatment, such as coupons and birthday gift cards, which drive customer retention. Loyal customers often tell their friends and family about the program’s special offers, which brings in new referrals.

Operating Cost Savings. Although companies must spend money on the loyalty program benefits, management and marketing, it costs less than acquiring new customers to replace those who went to a competitor — with a loyalty program.

As retail evolves into an omnichannel experience for the customer’s time, attention and hard-earned money, the winners in the competition for sales dollars will succeed by unifying the entire shopping experience through alignment of the processes and technology to support customer expectations.

Understanding scan data, targeted offerings for existing and potentially new customers and removing friction from the sales process will go a long way toward growing your. business. Take advantage of the opportunities technology affords your company.

Elie Y. Katz is the CEO and president of National Retail Solutions (NRS).

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From start-up to scale-up: Accelerating growth in construction technology

Construction sites in 2023 might in many ways resemble those in 1923, with manual bricklaying, paper blueprints, and scaffold towers. At $12 trillion, 1 Oxford Economics, March 2023. architecture, engineering, and construction (AEC) is one of the biggest industries in the world, but historically it has been among the slowest to digitize and innovate.

This, however, is changing fast: strong demand for infrastructure, a shortage of skilled labor, and increased stakeholder pressure for data transparency and integration are all accelerating digital adoption. As a result, the AEC tech ecosystem has experienced an explosion of investment and a wave of start-up launches. An estimated $50 billion was invested in AEC tech between 2020 to 2022, 85 percent higher than the previous three years. During the same period, the number of deals in the industry increased 30 percent to 1,229 (Exhibit 1).

Although the AEC tech industry is maturing, it is not yet at the scale and sophistication of more established software markets like logistics, manufacturing, and agriculture. The industry boasts fewer scale-ups and unicorns relative to its size. And it is hard for AEC tech companies to grow efficiently due to several dynamics among AEC customers, including fragmentation, low IT spend (relative to other industries), and entrenched analog ways of working.

In this environment, how can AEC tech companies accelerate adoption and sales and achieve scale? To answer this question, we surveyed approximately 100 investors and AEC tech players in 2022 and interviewed founders, investors, and large software companies in the industry. Using primary research and publicly available data, we also mapped and analyzed more than 3,000 AEC tech companies. 2 PitchBook, November 15, 2022. In this article, we review the findings of that research. We outline the investment trends that are accelerating the digitization of the industry, and we suggest how tech businesses, and their investors, can address challenges to get on a path of efficient growth.

TABLE OF CONTENTS

Trends accelerating the digitization of aec, hurdles to scale aec tech investments remain, strategies for scaling aec tech businesses.

Digitization of the AEC industry started gathering steam a decade ago, but the pace has accelerated over the past three years—and a number of trends suggest it will continue to do so (see sidebar, “What do we mean by architecture, engineering, and construction tech?”).

Economic factors and regulation are prompting investment

What do we mean by architecture, engineering, and construction tech.

A variety of software and tech is used across the architecture, engineering, and construction (AEC) industry. It includes design software, robotics, and tools for the planning, scheduling, budgeting, and performance management of projects (exhibit). Companies in the AEC tech industry range from multibillion-dollar software giants to one-person start-ups.

A combination of supply-and-demand factors are prompting investment in AEC tech. On one hand, global demand for long-term construction is strong, in part because of increased stimulus by governments, such as the $1.2 trillion Bipartisan Infrastructure Law in the United States and the €800 billion NextGenerationEU fund in Europe. More asset owners are also investing sizeable capital to decarbonize their portfolios to make them climate resilient. On the other hand, there is a shortage of skilled workers as more retire or transition to other industries. The United States has 440,000 vacancies in AEC, compared with around 300,000 in 2019, whereas the United Kingdom’s vacancies have nearly doubled since 2019. 3 “Construction: NAICS 23,” US Bureau of Labor Statistics, 2023; “UK job vacancies (thousand): Construction,” UK Office for National Statistics, March 2023. The industry is deploying digital technology to help increase productivity and bridge this gap between supply and demand.

Meanwhile, regulatory changes aimed at creating a more connected industry are reinforcing this wave of digitization. For example, the United Kingdom’s Building Safety Act requires a digital ledger of all building data for new residential buildings, and Sweden’s ID06 requires digital records of all the construction workers on a construction site.

Investor optimism is high

Investment in AEC tech has grown multifold and, based on our research, more and more investors are recognizing AEC tech’s potential to fundamentally change the structure of the construction industry and redistribute value pools at scale. This momentum is likely to continue. Seventy-seven percent of the respondents to our survey expect to invest in AEC tech at similar or higher levels in 2023, and 64 percent see it generating higher returns versus other verticals.

Seventy-seven percent of the respondents to our survey expect to invest in AEC tech at similar or higher levels in 2023.

The tech scene is maturing

The proportion of late-stage venture capital in total AEC tech investment totaled $11.5 billion between 2020 and 2022, more than triple that of the previous three years (Exhibit 2). Meanwhile, M&A continues to be the largest source of funding for AEC tech ventures, accounting for 48 percent of all investments and 68 percent of all exits. The growth of the industry is further reflected in the fact that the median deal size and post-money valuation 4 Post-money valuation is a measure of a company’s valuation that includes all external investments. in the industry has more than doubled since 2017 (Exhibit 3).

Companies and customers are still seeking interoperability

In 2020, we observed  that AEC tech players were targeting multiple use cases to address customer pain points. 5 “ Rise of the platform era: The next chapter in construction technology ,” McKinsey, October 30, 2020. This trend has continued, led by customer demand for interoperability—either through virtual platforms built using open standards and workflows, such as openBIM, or with one-stop-shop platforms such as those developed by some of the largest AEC tech companies. Indeed, nearly half of the companies we analyzed offer customers solutions that address three or more use cases.

AEC technology and property technology are converging

Until now, AEC tech and property technology (proptech) have evolved as separate ecosystems. AEC tech has focused on the design and construction of assets, while proptech has focused on the financing, planning, operation, and maintenance aspects of assets. This is starting to change, as customers and technology players see value in connecting the two. Our analysis shows that 20 percent of AEC tech companies also address at least one proptech use case: for example, linking the design and operation of building management systems using a digital twin.

While the trends above have helped expand the ecosystem of AEC-focused tech businesses and start-ups, investors and founders still wonder how best to pursue efficient growth—defined as the ability to grow annual recurring revenues (ARR) and to generate free cash flow (FCF) from those revenues. 6 Annual recurring revenue is the revenue that a company (often businesses that operate on a subscription-based model) expects to receive from customers on an annual basis. Free cash flow is the cash generated by a company after paying operating expenses and capital expenditures. Our analysis across industries shows that as software companies expand, efficient growth increasingly correlates strongly with valuations (Exhibit 4).

Within the AEC technology industry, however, our research also indicates that efficient growth is particularly tough to achieve for four reasons:

• Customer fragmentation. The average construction company employs fewer than ten people. The average project involves more than 100 different suppliers and subcontractors. So achieving scale requires selling to a large number of companies. This means that sales growth can be labor intensive and slow. As one investor noted, “This is a risk-averse and fragmented sector at its core, so growth is slow, but it is extremely sticky.”
• Multiple customer personas. Founders frequently tell us that identifying the real customer is tough because they lack a clear understanding of user versus buyer personas. Depending on the project, for example, the customer could be the project manager, IT manager, or procurement manager. And often, purchase decisions are made at the project level, not the enterprise level. As a result, companies need to resell the product again to the next project, which drives down net retention and raises acquisition costs. As one investor said, “The most successful companies have a plan to sell to the enterprise, not just the project.”
• Low margins and economic headwinds. Making the case for spending on software can be tough for AEC companies when there is limited capacity for investment. The industry has low margins and increasing economic headwinds, including materials cost inflation. Moreover, the typical IT spend for AEC companies is 1 to 2 percent of the revenue, compared with the 3 to 5 percent average across industries. 7 “Gartner top strategic technology trends for 2022,” Gartner, October 2021. Against this backdrop, solutions must come with a business case. Although ROI can be high, until recently players have not been effective at quantifying benefits. As one investor said, “In a low-margin industry, and in this market environment in particular, it is important that companies can clearly demonstrate and measure the cost-saving benefits of their product.”
• Adoption and scaling challenges. Driving tech adoption in a projects business like construction poses several challenges: users often switch products among different projects—sometimes they need to adopt different tools depending on client preferences, and staff come and go. Furthermore, the industry has traditionally had limited digital capabilities, although this is changing as workers become accustomed to using digital technology in their everyday lives. And as one AEC company executive said, “The pandemic forced us to accelerate adoption from the office to the site overnight.”

For companies that can overcome these barriers, there is a big prize up for grabs: a customer base that is larger than most other industries. So what does it take? Our analysis of tech companies in AEC, as well as other industries like manufacturing, travel, and logistics, shows five common growth characteristics.

Pursue a big total addressable market and a bold vision

As one investor told us, “If the extent of your vision is to sell tools to solve a niche problem, then we’re not excited. We are looking for founders with vision and mission to improve outcomes for big swathes of the market.” Having a bold vision—and being able to effectively articulate how it benefits the user and the broader industry—helps attract talent, investors, and customers, and allows companies to move faster as they continually course-correct toward a North Star. For example, one AEC tech company focuses on improving predictability of project outcomes and uses that simple vision to expand the total addressable market (TAM) beyond contractors and planners to cover a far broader customer set, including project owners, banks, and insurance companies.

A bold vision usually means founders are thinking about the entire AEC tech ecosystem and figuring out ways in which their company can work with other providers to create a seamless user experience and unlock newfound value for a broader set of customers. For example, one AEC design platform expanded its core offering beyond architects and engineers to connect to product suppliers, and thus monetize transactions for building products used in designs.

Achieve a great product market fit

Finding the right product market fit is a key part of the investment decision-making process for investors in most industries, but AEC tech companies often do not get it right. In fact, as our survey indicates, the most common issues observed by AEC tech investors are an overfocus on engineering (rather than product and market fit) and product fragmentation (Exhibit 5).

As one AEC tech player noted, “Niche, technical design tools are often built by self-taught developers and construction professionals who built the tool to solve a specific problem or fill a gap in their workflow. As such, the very nature of those tools focuses on the tech and not the user experience.” In our discussions with start-ups and investors, three common themes emerged that can help create a better product market fit. All three elements require strong product management capabilities .

First is focus. Since customer needs differ across segments, companies would do well to focus on one or a few specific segments, whether they are targeting architects or subcontractors or distributors. As one founder put it, “I have potential customers in manufacturing, retail, construction, and facilities management across more than ten geographies, but we have to focus, or we will achieve nothing.”

Second is feedback. As one investor told us, “Many contech [construction technology] firms are founded by industry professionals who launched their business to solve a problem, so they have huge product focus. We need to see more founders with a balanced product and market/customer focus.” One way to sharpen market focus is to build a network of customers and collaborators. Most successful players do this through their investors’ networks and beta customers, who benefit from low-cost early releases in return for investment in testing and development feedback. And a side benefit is that they can provide access to a critical mass of other customers (Exhibit 6).

Third is flexibility. Nearly every start-up and scale-up we have spoken to has seen a big shift in their product proposition because they responded to market views and kept evolving to optimize the product market fit. For example, one start-up developed an app to measure material waste from construction sites but eventually evolved it to measure embodied carbon in materials.

Build a customer acquisition engine with a scalable revenue and distribution model

Valuations for start-ups are tied strongly with the ARR growth metric. In a fragmented market like AEC, customer acquisition is difficult and expensive. Based on our research, leading players differentiate themselves with three moves to maximize the ARR bang for each buck spent on marketing and R&D:

• Deliver a scalable revenue model. As one investor said, “Some products require so much customization that the software company becomes a consultancy.” Successful businesses have a product that can be deployed with minimal customization and training (and that usually means software rather than hardware). And where customization or training is required, they invest time only in high-potential customers and often partner with independent software vendors to deliver at scale.
• Find creative routes to market. You’re never going to crack the market one customer at a time. Successful players use their investors and existing customers to open new routes to market. They also lock in users early. For example, one design software player gave away free copies of its software to architecture students, who then took it to their new employers. Moreover, these players have a channel strategy aligned with customer tiers, and that includes not only value-added resellers (VARs) and distributors but also low-cost remote channels (including multilingual remote inside-sales centers) and self-serve, web shop, and e-commerce.
• Supercharge the sales team. Successful software companies incentivize their direct-sales teams to cross-sell and upsell and drive key account management capabilities. One leading player with multiple brands centralized its go to market across brands to accelerate cross-sell and upsell and capped bonuses on some established products to incentivize sales of new products. The best sales organizations are underpinned by data that allows them to see the relationship between specific, often siloed, sales and marketing activities and overall growth outcomes.

Improve net retention with customer success

Our analysis shows that as software companies grow, the most important driver of valuation shifts from pure growth, often measured by ARR, to include the ability to generate FCF from ARR. In short, it’s not enough to just have customers; you need to earn money from them. In what is commonly referred  to as the “rule of 40,” the sum of percentage growth and the FCF rate should equal 40 percent or higher. 8 Paul Roche and Sid Tandon, “ SaaS and the Rule of 40: Keys to the critical value creation metric ,” McKinsey, August 3, 2021.

Achieving strong FCF is in large part about optimizing the payback period—that is, how long does it take to recover your customer acquisition costs. This means acquiring new customers efficiently, retaining customers, and upselling and cross-selling to them. This is measured by net retention rate (NRR), 9 Net retention rate is a metric that shows how effective a company is at driving growth in its existing customer base while keeping the churn low. which requires a laser focus on customer success. Across sectors, companies with high NRRs demonstrate three common characteristics:

• They know their numbers. At the heart of customer success is a data-driven understanding of how customers obtain value from a specific product. Maximizing NRR is a game of inches, so leaders analyze the many drivers of growth and churn (upsell, contract cancellation, additional licenses, and so on) at a customer level and respond with targeted interventions (for example, offering bundles for additional “seats” as usage reaches contract limits).
• They set up a dedicated customer success function. A team that can work with customers to get maximum value from the product is particularly important in AEC, where customers are less digitally mature and solutions are less well established. For example, the largest AEC technology companies have customer success teams and run conferences and training for their users. One software company hired a retired construction contractor for its customer success function to better understand customer needs.
• They deliver customer success at low cost. Customer success does not have to mean dedicated (and expensive) customer support. It can often be delivered at lower cost by cultivating user communities and promoting the use of online tutorials, for example. One AEC tech company gained thousands of users on zero-marketing spend by leveraging its community forums and industry networks—effectively putting its own customers to work.

Build functional maturity as you scale

As software companies grow beyond the start-up and scale-up stages, growth rates slow, and FCF (and hence, valuation) is increasingly driven by operational efficiency. This typically comes down to optimizing NRR as well as marketing and sales spend (which can be 50 percent or more of the revenues of typical software companies). At-scale software companies in the top quartile for valuation typically exhibit the following characteristics 10 “SaaS and the Rule of 40,” 2021. :

As software companies grow beyond the start-up and scale-up stages, growth rates slow, and free cash flow (and hence, valuation) is increasingly driven by operational efficiency.

• Optimize marketing and sales spend. Leading software players allocate marketing and sales spend against future, not past, revenue opportunities to give high-growth accounts the biggest coverage. They also continuously segment customers, targeting lower-potential customers through web sales/e-commerce and inside sales while increasing spend on the highest-potential customers.
• Continuously optimize pricing and track impact. Leading players build customer business cases to link pricing to the value generated for customers. They also track the impact of pricing changes in near real time and optimize accordingly. Companies would also do well to make sure their payment terms are right. As one investor explained, AEC tech players often price based on a project or milestone. “This is not ARR, even though some may call it that. And because construction is often subject to delays, this means the risk attached to these revenue streams is very high, which puts off potential investors.”
• Lean on data and automate processes. Successful software companies leverage data, AI, and automated processes  across the business in a variety of ways, including identifying leads and proactively targeting cross-sell and upsell opportunities, leveraging usage information in pricing and product decisions, and assessing developer velocity .
• Strengthen the business-building muscle. Tech companies of every size often reach the tip of a growth curve without a market-ready venture or offering that can pick up the slack, so their growth dips. Leading players maintain momentum by launching net-new businesses more quickly. They incubate new businesses thoughtfully, with dedicated resourcing for product development and go to market.

Several tailwinds are powering growth in the AEC tech industry despite the near-term challenges of the economic slowdown. To capitalize on the investment opportunities and achieve efficient growth, investors and tech companies can learn from the most successful AEC tech companies and catch the wave in this exciting industry.

The authors wish to thank Daniele Di Mattia, Julien Gagnon, Josh Johnson, and Adam Singer for their contributions to this article.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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