What is Customer Analytics: Types, Best Practices & Tools

What is Customer Analytics: Types, Best Practices & Tools cover

At a time of heightened customer information, customer analytics helps you understand complex customer behavior, track performance, and drive product growth.

In this article, we cover:

  • What customer analytics is, and why it’s important.
  • The different types of customer analytics.
  • The best product analytics tools for SaaS companies.


  • Customer analytics is the process of collecting and analyzing customer data to help businesses make better business decisions.
  • It helps businesses to understand their customers better and improve their customer relationships, customer experiences, and retention rates.
  • There are different types of customer analytics, including customer journey analytics, customer experience analytics, customer engagement analytics, customer lifetime analytics, customer behavior analytics, and customer retention analytics.
  • To get started, you’ll need to define your objectives, set SMART goals, and decide on the type of data you need to collect.
  • Once you’ve collected your data, you’ll need to identify the right tools to collect and analyze your data.
  • Some best practices for performing customer data analysis include pairing analytics insights with product feedback surveys, building hypotheses and testing solutions, and taking an omnichannel approach to user interactions.
  • Userpilot is one of the most robust analytics tools available. It helps you track and analyze user interaction, collect feedback data, segment users, analyze funnels and flows, etc.
  • To learn more about how Userpilot can help you increase sales and decrease customer churn, book a Userpilot demo.

What is customer analytics?

Customer analytics is the process of collecting customer data, analyzing it, and contextualizing it across several devices, channels, and interactions to help you identify, attract, and retain customers.

The importance of customer analytics

At its core, customer analytics helps businesses understand who their customers are, how they think, and how they interact with your business.

It provides actionable insights that help companies make better business decisions regarding their pricing, product, and service experience. This leads to many benefits, including:

  • An increase in customer lifetime value and customer retention by better predicting customer behavior and, as a result, maintaining better and healthier relationships with them.
  • Improved customer experience as it helps businesses understand complex customer behavior and high touchpoint areas.
  • Customer analytics provides more accurate insights into customer pain points and roadblocks that help you better engage with customers.
  • Examine key customer touchpoints and identify opportunities to create personalized experiences and content to foster customer loyalty.
  • It boosts the overall profitability of your business by empowering you to create timely and targeted marketing campaigns.

Different types of customer data analytics

Customer analytics is a broad term for the different kinds of customer analysis that help businesses understand customer needs and how to satisfy them.

There are different categories of customer analytics, including:

Customer journey analytics

Customer journey analytics involves tracking and analyzing customer interactions with your brand and product.

It follows the customer’s journey across all touchpoints, from initial research and information-gathering to actual purchase and beyond, helping you to understand the impact of every customer interaction with your business.

Customer journey analysis, thus, analyses data from conversations with support, user ratings, product usage, etc. It leverages this data to connect the dots between customer behavior and important business KPIs.

Customer experience analytics

Customer experience analytics is the process of gathering and analyzing customer data to help you understand (and improve!) the product experience.

It helps you understand user behavior, improve your product, increase customer retention, boost customer satisfaction, and improve the customer lifetime value.

For instance, you can track feature usage using feature tags, heat maps, and session recordings. Or, you can use a CSAT, NPS, or CES survey to collect customer feedback regarding their experience.

Customer engagement analytics

Customer engagement encompasses every interaction a customer has with your brand across your different communication channels. This engagement may be with your product/service or with your brand.

With customer engagement analytics, you can examine all of these interactions to determine the health of the consumer, identify preferences, and predict future behavior.

Your customer success teams, for instance, may track feature usage data to understand how users engage with your product.

On the other hand, the marketing team may track website visitors to see where they come from, how they interact with your content and CTAs, their navigation paths, etc.

Customer lifetime analytics

Customer lifetime analytics enables businesses to identify their best customers and the expected benefits that the customer might bring during their lifetime with your product.

This is similar to the customer experience and journey analytics, but it’s differentiated by a key metric – the customer lifetime value (CLTV). This metric helps you determine how much revenue a single customer will bring through their time with your business.

With this in mind, you can segment customers to determine and target more valuable customer groups accordingly. It can also serve as a good measure of your business health.

For instance, if CLTV declines over a period, it indicates an issue with your repeat customers. Similarly, if your CLTV is lower than your customer acquisition cost, it indicates an overspending on customer acquisition.

Customer behavior analytics

Customer behavior analytics collects, examines, and interprets data about customers’ interactions with your business to help you understand and predict their behavior.

Because customer behavior has a very broad scope, what you choose to analyze will depend on your business needs.

For instance, you can examine your customers’ buying habits, including social trends, frequency patterns, and the background factors influencing their buying decisions.

Or you can segment product users to identify behavioral trends and analyze feature usage, heatmaps, survey feedback, etc., to determine customer satisfaction levels as well as friction and drop-off points.

Customer retention analytics

Also known as customer loyalty analytics, customer retention analytics measures how loyal your customers are. Its major goal is to identify the rates and reasons for churn among your customers and customer segments.

Since retaining customers always costs less than winning new customers, analyzing retention patterns is a must. Retention analytics, thus, answers questions like:

  • How many of our users are repeat customers?
  • What percentage of our customers churn?
  • Why do users churn?

Note that there are two kinds of customer retention analytics – periodic and retrospective.

Periodic analytics measures user activity over a set period and shows customer engagement with your product. You can think of this as predictive analytics designed to help you identify churn before it happens.

Retrospective retention analytics, on the other hand, focuses more on identifying when customers churn and revealing the survival rates within different customer segments.

How to get started with customer analytics to drive product growth

It might feel intimidating to dive into the complexities of customer analytics because of the size and variance of the data. Thankfully, it doesn’t have to be. Let’s now look at the process behind it and how you can get started.

1. Define your objectives before the customer analytics process

Defining your goals early enough will help set the tone for your process. Before getting into any form of analytics, you need a target. Essentially, you need to answer the question:

“What do I want to achieve by analyzing customer data?”

For example, instead of setting a goal to grow your revenue, you can set a SMART goal to grow your revenue by 15% over the next six months. This goal is specific and measurable (15%). It’s also time-bound (6 months). Thanks to the timeframe, it’s also achievable and realistic.

On the other hand, a goal to grow your revenue by 70% in 3 months is specific, measurable, and time-bound, but it isn’t realistic (relevant) as it leaves too little time for it to be achievable.

2. Decide on the collected data and analytics type

With your goals sorted, it’s now time to determine the type of analytics needed to achieve those goals. Similarly, the type of analytics needed will inform the data you need to collect.

For instance, if you want to improve your customer retention rate by 20% in six months, then customer engagement analytics and customer experience analytics should be your tools of choice.

Both of these forms of customer analytics give you an insight into the customer experience. What do customers like about your brand? What challenges do they face with your product? And, what do they need?

For this, you’ll need feature usage data, user flow data, and even direct customer feedback. The data you collect should help you determine what customers love and identify friction points in your product.

By understanding how customers engage with your brand and what challenges they face, you’ll become equipped to improve their experience, which will also improve customer retention.

3. Use tools to collect relevant customer data

Next, it’s time to get started with data collection. Once again, the data you need will determine the tools you need for data collection.

In our example above, you can use feature tags, heat maps, event tracking, and session recordings to understand customer behaviors and interactions within your product.

You’ll also need to measure customer satisfaction with in-app surveys. By adding follow-up questions to your NPS, CSAT, and CES surveys, you’ll get direct responses about what customers like/dislike about the product.

This combination of qualitative and quantitative data collection will make it easier to identify common pain points and areas of improvement within the user experience.

4. Analyze customer data using tools

With all your data collected at different customer touchpoints, you’re finally ready to analyze your data and glean relevant insights. For this, you need the right customer analytics tools.

To select the right analytics tool, you should know what it measures, how it measures it, and the data it collects/needs. These should all align with your already-stated goals and KPIs.

Carefully select a tool and get started with your analysis. Watch specific trend reports, build funnels with your data, check page activities, and track user engagement activities.

Userpilot analytics
Visualize customer data easily with Userpilot.

You’re also now equipped to perform predictive customer analytics and identify at-risk customers in advance.

Best practices when performing customer data analysis

You can ensure consistent success in your data collection and analysis methods by observing the following best practices:

Pair analytics insights with product feedback surveys

In-app analytics insights and numbers only reveal one side of the story – the problem (or success). To identify the driving forces behind any failure or success, you need to peel back the numbers and look deeper.

Product feedback surveys help you do just that, giving you a fuller view of your data. They help you understand user needs and identify improvement opportunities.

For example, when you notice a decrease in feature usage, collect customer feedback for that feature to identify why it has happened. Pair quantitative and qualitative questions to understand the reason behind the sentiment around the feature.

customer satisfaction survey
Use a feedback survey to understand the ‘why’ behind the numbers.

Build hypotheses and test solutions

Traditional customer analytics always begins at the diagnostic analytics phase. This involves using data to understand what has happened and how you can improve things.

Sometimes, though, it may not be immediately clear what you can do to remedy a situation. When that happens, move from diagnostic to predictive analytics.

This involves forming different hypotheses and testing out solutions until you find what works. This is especially important when diagnosing customer engagement; you may need to evaluate various options to find out what sticks.

Look at customer interactions from an omnichannel point of view

An omnichannel customer engagement strategy unifies customer interactions and messaging across different channels.

You’ll need to collect data from across the customer journey to approach customer analytics in this manner. This means collecting data from every interaction channel, including your product, website, email, and social media engagement data.

Next, unify all of this data into your analysis. This will enable you to better understand your customers, deliver a personalized customer experience, and exceed customer expectations.

Robust customer analytics tools

Finally, let’s now identify some of the most robust customer analytics tools for collecting and analyzing a wide variety of data.


First on the list and a very robust platform, Userpilot is a complete product growth tool. It enables you to harness user data to drive customer engagement and boost product adoption. Here’s how:

  • Trend analysis: Track key changes in important product metrics over time. Trend analysis helps you visualize event data at both user and company levels over a period. You can filter the data for more granular analysis and select your preferred graph/chart style.
Userpilot trend analysis
Uncover usage trends with Userpilot.
  • Feature tagging and heat maps: Feature tagging enables you to track user interactions with specific UI elements. You can track clicks, text inputs, and hovers. Similarly, heatmaps present user engagement with features in an easy-to-grasp manner using ‘cold’ and ‘hot’ colors.
Userpilot feature tagging
View user engagement data in the Userpilot Features & Events dashboard.
  • Funnel analysis: Monitor and optimize conversion rates at key stages of the customer journey. Funnel analysis enables you to view the average conversion time and easily identify drop-offs and friction points.
Userpilot funnel analysis
Userpilot funnel analysis.
  • Flow analysis: Track user progress along the user journey in detail. Extract granular user interaction insights at every step.
Userpilot flow analytics
Flow analytics in Userpilot.
  • A/B testing and Multivariate testing: Conduct in-app experiments on your in-app flows to improve the customer experience. This may involve testing the effectiveness of a single flow or comparing different flows.
Experiment with your in-app flows.
  • In-app surveys: Create in-app surveys to collect both quantitative and qualitative data from your customers and immediately analyze response trends.
The Userpilot survey template library
The Userpilot survey template library.


Amplitude is one of the popular user analytics tools, with a focus on customer journey and event analytics. Thanks to its two-way integration options with other industry-leading tools, it makes the product analytics process easier.

Designed for product teams, Amplitude helps you analyze patterns in user behavior and interactions, and make predictions accordingly. Unfortunately, it can get very difficult to use without an in-house specialist.

Amplitude analytics
Segment users and track events with Amplitude.

Google Analytics

Google Analytics is a must-have tool for web user analytics. It enables you to track key metrics, from acquisition and marketing campaigns to behavioral data collection, making it easy for you to gauge your website performance.

You can track traffic sources (to identify your most important acquisition channels), conversion rates and sources, and more.

Note, though, that Google Analytics isn’t a product tool. To access any product-specific data, you’ll need a highly technical team.


Customer analytics provides you with invaluable insight into customer motivations, interactions, and needs. It helps businesses of all sizes make better decisions that will boost customer engagement and retention.

Ready to get started collecting and analyzing user data? Book a Userpilot demo and learn how it can help you make better business decisions.

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