What Is Behavioral Analytics? The Complete Guide

Understanding what drives action—or inaction—along a user’s journey is key to making better-informed decisions about the digital experience you provide. This guide offers an introduction to behavioral analytics, as well as an overview of the benefits it brings to your business.

Behavioural Analytics

A comprehensive guide to behavioral analytics

Key takeaways

  • Behavioral analytics helps you to measure and understand how people behave on their digital journeys

  • It examines things like what pages they visit, how long they stay, what buttons they click, and even their mouse (or finger) movements

  • The insights gained answer key questions about the actions, interests and struggles of users

What is behavioral analytics?

Behavioral analytics is a way of tracking and analyzing how people behave on websites, apps and other digital experiences. It involves monitoring a number of metrics, from how long a user stays on a page or screen to the individual elements they interact with and the specific actions they take.

While many standard analytics tools can tell you what happens along the user journey, only a digital experience intelligence (DXI) solution can tell you why. By helping you to understand the underlying pattern behind user actions, behavioral analytics reveals customers’ likes, dislikes, intentions and perceptions. With this knowledge, you can eliminate guesswork and make better decisions that drive specific business outcomes, such as:

  • Improved user experiences

  • Optimized marketing strategies

  • Personalized customer offers and experiences

The benefits of behavioral data analytics for your business

While limited on their own, behavioral analytics tools offer a variety of transformative benefits when used within a digital experience intelligence (DXI) platform.

The greatest benefit behavioral analytics brings your business is customer understanding. With a clear view of what causes specific user actions, it’s possible to develop a much greater understanding of your customer’s preferences, interests and needs. These insights can help you unlock new, more effective approaches to tailoring your products, services and marketing to better meet customer expectations.

Another significant benefit is improved decision-making. Behavioral analytics provides objective data that will help your business to make more informed choices. While eliminating guesswork, behavioral analytics enables data-driven decisions about product development, pricing strategies, marketing channels and more. With a complete picture of where your business should be heading, it becomes easier to optimize resources, increase efficiency and maximize return on investment.

Behavioral analytics provides another major benefit whose importance cannot be overstated: an enhanced user experience. By identifying the root cause of pain points, bottlenecks and areas of confusion along the customer’s digital journey, these tools help you to make improvements that enhance their experience, leading to higher customer retention and loyalty and increased sales.

Types of digital behavioral analytics tools

Many kinds of behavioral analytics tools exist to help businesses gain insights from their customer behavior. While successful businesses will generally make use of all available analytical methods, which tool is used will differ depending on the question you are trying to answer. Below you’ll find an overview of these tools and how they work.

A/B testing tools

A/B testing tools allow you to experiment with different variations of your website, app or digital experience. Offering these variants enables you to analyze how differences impact user behavior. By comparing the effectiveness of different elements, layouts, designs, calls-to-action and more, you can optimize digital experiences and maximize conversion rates. To learn more, see our Complete Guide to A/B Testing.

Session replay tools

Session replay tools show you video-like reconstructions of an individual user’s journey through your website or app. With session replay you can see interactions such as mouse movements, clicks, scrolls and form inputs–or for mobile users, every tap, scroll and pinch. Reviewing and analyzing these interactions, both on the individual level and in the aggregate, provides a detailed understanding of how users are navigating and interacting with your digital platform. Session replays are particularly effective for identifying usability issues, uncovering user frustrations and providing insights that help optimize the user experience. For a more in-depth explanation, check out our Comprehensive Guide to Session Replay.

Heatmap tools

Heatmap tools (or interaction maps, which are the next generation of heatmap tools) offer a visual overview of user behavior and actions using color-coded overlays. These overlays represent aggregated data on interactions such as clicks, scrolls and cursor movements–or taps, scrolls and pinches for mobile users. The visual nature of heatmaps makes them a helpful method of identifying areas of interest, friction, engagement and attention along the customer journey. To see examples in action, reference our Complete Guide to Heatmaps.

Voice of customer (VoC) and survey tools

Voice of customer (VoC) tools capture and analyze feedback directly from customers to help you understand their needs, preferences and expectations. These tools collect data through a number of channels, including surveys, feedback forms, online reviews, social media and customer support interactions. VoC tools are particularly useful for gaining an in-depth understanding of customer sentiment, satisfaction levels, pain points and suggestions for improvement. For a full list of benefits and limitations, visit our Complete Guide to Voice of the Customer.

🔥Hot tip: A digital experience intelligence solution, like Glassbox, combines all these tools into one platform so you can access advanced insights across the company in one place. These advanced insights give you the complete picture of the digital customer experience, not just bits and pieces.

3 types of behavior analysis

Several analyses can be conducted using behavior analytics tools. By choosing the method of analysis that aligns with your specific needs and goals, you can gain actionable insights that drive overall business performance. Below are some examples of the kinds of behavioral analysis your business can undertake using these tools.

A/B test analysis

An A/B test analysis involves dividing a sample audience into groups and exposing each group to a different version of the element being tested.

For example, let’s say you want to know which version of a CTA button leads to a higher conversion rate. The conversion goal could be signing up for a newsletter, making a purchase or any other desired action. An A/B test can be conducted by:

  1. Creating variations: In this example, let’s say we have two versions of the CTA button, A and B. Version A is the current version of the button. It’s green, says “Sign up now,” and is placed at the bottom of the webpage. Version B, a new design, is orange, says “Get started,” and is placed at the top of the webpage.

  2. Defining a sample audience: The next step is to randomly divide a sample audience into two groups. Each group will be shown only one version of the CTA button.

  3. Data collection: Next, we’ll run the test for a sufficient duration to gather data. Throughout the test, relevant data is collected, including the number of button clicks and the conversion rate for each variation.

  4. Insights: After the test concludes, the results can be reviewed. If version B shows a significantly higher conversion rate compared to version A with a high level of statistical confidence, it can be concluded that the changes made to the CTA button drove better results. This learning can now be taken forward to make informed decisions about CTA buttons in the future.

Funnel analysis

A funnel analysis involves tracking and analyzing the stages users go through during the conversion process on their digital journey. The results help you to identify and understand points of drop-off and inefficiencies within the conversion funnel.

Ready for an example? Let’s say the funnel being analyzed is an e-commerce purchase consisting of the following steps:

  1. Homepage visit

  2. Product view

  3. Add to cart

  4. Checkout

  5. Payment

  6. Order confirmation

As part of the funnel analysis, user interactions and events are tracked and recorded at each stage of the funnel. The collected data is then used to create a visual representation of the funnel, typically shown as a series of connected blocks. By examining how users flow through the steps of the funnel, it’s possible to identify patterns, bottlenecks, drop-off points and potential areas for improvement. For example, if a large number of users abandon the cart before checkout, there may be usability or friction issues in the checkout process. In a similar vein, reviewing the product pages which lead to the highest number of cart additions can help you to optimize other pages to increase conversion.

Want to learn more about funnel analysis? Check out the blog Funnel analysis: The ultimate guide to unlocking insights and maximizing conversions.

Audience segmentation

Audience segmentation is a method of dividing a target audience into distinct groups based on specific characteristics, behaviors or demographics. Grouping users in this way can help you to create a more tailored experience with messaging and prompts that engage each group effectively.

Let’s imagine an online fashion retailer who wants to better personalize its marketing efforts. They decided to segment their audience based on shopping behavior:

  • Repeat customers (people who have made multiple purchases in the past)

  • New customers (people who recently made their first purchase)

  • Abandoned carts (customers who added items to their cart but did not complete the purchase)

Using behavioral analytics tools, the retailer collects data from sources including customer profiles, purchase history and engagement metrics to identify customers belonging to each segment. Once it’s known which segment a customer belongs to, it’s possible to tailor their experience. Examples include:

  • Providing repeat customers with personalized discount codes, exclusive offers or loyalty program benefits to encourage continued engagement

  • Welcoming new customers with introductory offers and product recommendations to incentivize future purchases

  • Reminding customers who abandoned their cart of the option to recover their cart and encouraging purchase completion

Teams that can benefit from behavioral analytics

Departments across your business can benefit from the insights gained through behavioral analytics. Below is an overview of how teams can utilize deeper customer understanding to drive specific business improvements.

Product management

Behavioral analysis allows product managers to understand the actions users take and make informed, data-driven product decisions. These insights enable product managers to prioritize product features and enhancements based on how they impact both the user experience and business goals. Product managers can also use behavioral analysis tools to validate hypotheses about user behavior and investigate potential product improvements before they are developed and rolled out to all customers.


Marketing teams can use behavioral analytics to leverage deeper customer understanding, creating more targeted and effective marketing strategies. The ability to segment audiences based on behaviors, interests and actions opens the way to personalized, highly relevant campaigns that deliver a greater return on investment.

Data analysts

Behavior analytics supports data analysts in their exploration of engagement data, helping them to uncover patterns and trends in user behavior. This enables them to identify insights, anomalies and areas of interest for further investigation. Tools, such as heatmaps and funnel maps, support data analysts in communicating complex behavioral data effectively with other teams and stakeholders, facilitating data-driven decision-making processes across your business.

Customer service

The valuable insights offered by behavioral analysis empower customer service teams to deliver personalized support, anticipate customer needs, and resolve issues efficiently. Using learning taken from behavioral data, agents can deliver exceptional experiences and build strong relationships with customers.

User experience (UX)

Behavioral analysis keeps UX teams focused on the needs, preferences, and behaviors of users. With a greater understanding of how and why users navigate, interact, and respond to different design elements, UX teams can create user-centric designs that align with expectations and enhance the overall experience.

Engineering and DevOps

Digital behavior analytics help engineering and DevOps teams understand system performance and identify opportunities for optimization. For example, teams can review response times, resource utilization, and error rates to spot performance bottlenecks, detect anomalies and troubleshoot deviations before taking proactive measures to resolve them.

6 behavioral analytics best practices

To get the most benefits out of behavioral analysis, it needs to be done on a continuous basis. We’ve outlined six best practices to get you started.

1. Define your goals. Before you think about what you want to measure, you need to define what your goals and objectives are. This will ensure that what you’re tracking maps to your overall company goals and KPIs.

2. Map out customer journeys. Typically, a person doesn’t become a customer the first time they visit your website or app. In fact, it can take many touchpoints until they do. It’s important to map out and understand the customer journey so you can understand all customer touchpoints where there is engagement—from an interest in the product to making contact with the brand to any pain points or struggles faced.

Once you understand your customers’ journeys, you’ll have a better sense of any pain points or frustrations they face and can optimize the customer experience.

3. Collect the data. As we mentioned earlier in the guide, there are many user events you can track. As a refresher, some of these events can be:

  • Creating an account

  • Filling out a form

  • Submitting a form

  • Adding an item to a shopping cart

  • Abandoning a shopping cart

  • Signing up for a newsletter

  • Purchasing an item or subscription

You’ll also need to determine how to track these behaviors. A digital experience intelligence solution like Glassbox can track all of this for you and will go beyond telling you a user behaved a certain way–it’ll tell you why they behaved the way they did. For example, if a customer abandons their cart, you can see exactly what led them to do so. These advanced insights can help you optimize the customer experience and increase conversions.

4. Analyze the data. Now that you have your behavioral data, you can begin analyzing it. When looking at the data, consider asking the following questions:

  • Are there successes that can be replicated elsewhere on your website or app?

  • Are there areas where people struggle or any friction points that can be identified?

  • What commonalities do repeat customers have?

During this step, you should compare qualitative and quantitative insights. Doing so will help you identify areas for improvement, uncover friction points and show you successes that can be applied to other areas.

5. Implement necessary changes. Now that you have your behavioral data, it’s time to use it to your advantage to make improvements or replicate successes.

For example, if you notice users are rage-clicking what appears to be a button on a web page, but it isn’t actually a clickable button, you can change the UX design to make it obvious that it’s not or you can change it to be.

You can also run A/B tests to see which version performs better once you implement any changes.

6. Measure the results and repeat. Once the changes have been implemented and enough time has passed, it’s time to take a look to see if they’re having a positive impact. Evaluate the successes of the changes you made to see if they had the desired outcome. Improving the customer experience and listening to your audience is an ongoing process. To remain competitive you need to analyze behavioral insights on a regular basis.

Ready to try a complete digital experience intelligence solution?

On their own, behavioral analytics tools can provide a powerful view of your customers’ behavior, but that view is limited to how users act. Without the why, it can be difficult to derive clear, actionable insights.

When you use behavior analytics as part of a complete digital experience intelligence platform, you can truly understand the behaviors, struggles and decisions customers encounter throughout their entire digital journey. With that greater understanding, you can make changes and additions that positively impact your business objectives while enhancing the user experience.

Start making decisions based on the complete story—check out Glassbox today.


Frequently Asked Questions about Behavioral Analytics.

What is behavioral analytics?

Behavioral analytics is a way of tracking and analyzing how people behave on websites, apps and other digital experiences. It involves monitoring a number of metrics, from how long a user stays on a page or screen to the individual elements they interact with and the specific actions they take.

What are examples of user behaviors related to behavioral analytics?
  • Creating an account

  • Filling out a form

  • Submitting a form

  • Adding an item to a shopping cart

  • Abandoning a shopping cart

  • Signing up for a newsletter

  • Purchasing an item or subscription

What are the different types of digital behavioral analytics tools?
  1. A/B testing tools

  2. Session replay tools

  3. Heatmap tools

  4. Voice of customer (VoC) tools

  5. Digital experience intelligence solutions

What are six behavioral analytics best practices?
  1. Define your goals

  2. Map out customer journeys

  3. Collect the data

  4. Analyze the data

  5. Implement necessary changes

  6. Measure the results and repeat