What is Behavioral Analytics and How to Do it [+ Examples & Tools]

In SaaS, behavioral analytics can help you improve your product, increase your user engagement and retention, and grow your revenue.

It shows every click a user makes, every engagement they have, what they ignore, and it can trace the whole user journey for you.

So if you want to pull it off effectively, let’s go over the basics, the different methods to use behavioral analytics, and the best tools in the market for this.

TL;DR

• Behavioral analytics is a process of collecting, analyzing, and interpreting data related to user activities. It provides you with knowledge of how users navigate, engage, and interact with your product.
• Let’s explore five types of behavioral data you can collect:

1. Engagement data. It shows how users behave with your app (clicks, hovers, text input, etc.). It can be collected using feature tags or event tracking.
2. User journey data. It outlines the entire journey your users have with a product. You can collect it using custom events and goal-based tracking.
3. User navigation data. It tracks the sequence of steps a user performs in order to complete a particular task.
4. Session recordings. Which are video replays of users interacting with your product, capturing everything from mouse movements, clicks, and keystrokes, to scrolling behavior and page navigation.
5. Survey data. It involves relational and transactional surveys for collecting quantitative and qualitative data to understand user behavior.

• There are eight types of behavioral analysis you can leverage, and they include:

1. Path analysis. It’s used to map out every step that your users make through their journey. It can identify the path your most successful users follow to achieve goals with minimal obstruction.
2. Funnel analysis. Which lets you watch how users move through the funnel and find opportunities for improvement.
3. Trends analysis. Which involves tracking specific metrics over a particular time frame and spot trending changes in behavior.
4. Cohort analysis. To spot behavioral patterns among a group of users (e.g. features that drive engagement) and find ways to enhance the product experience.
5. Survey analysis. Which involves observing survey responses to find patterns, categorize feedback, and spot improvement opportunities by tagging qualitative survey responses.

• Additionally, we brought three use cases for using behavioral analytics to drive growth:

1. Combining cohort and trend analysis to understand trends happening in different user groups. Then adjust your retention strategy if there’s a decreasing trend.
2. Integrating funnel and path analysis to determine the happy path, understand the key steps in the conversion funnel, and find ways to improve customer success by leading other users to the right path.
3. Segmenting survey data to pinpoint common pain points that those users share and offer personalized, proactive help.

• The best behavior analytics tools in the market are:

1. Userpilot to collect and analyze product data such as events, feature usage, and survey responses.
2. Amplitude for top-tier funnel analysis and cross-platform analytics.
3. Mixpanel for tracking trends in real-time.

• Since you need the right tool to unlock valuable insights. Why not book a Userpilot demo to see how you can unlock new growth opportunities?

What is behavioral analytics?

Behavioral analytics tracks and analyzes your user’s activity by using both quantitative and qualitative data.

It reveals behavior patterns, user preferences, and pain points, which you can use to create personalized onboarding flows, improve feature adoption, and increase customer lifetime value.

Types of user behavior data to collect

There are different types of behavioral data you need to collect before you can perform any analysis.

That said, let’s explore and explain five of them.

In-app engagement data

Engagement data shows you how users interact with your app. And it can be collected using:

• Feature tags. It tracks client-side inputs that happen while the user engages with specific parts of your app such as clicks, hovers, and keystrokes.
• Event tracking. Which tracks server-side events that go beyond simple clicks (such as time spent, revenue generated, payments, and so on). It’s used for filling the gaps that feature tagging can’t track.

With the right combination of both, you’ll be able to understand your users’ work habits and reactions.

User journey behavioral data

User journey data, as the name suggests, outlines the entire journey your users walk with a product—from the moment they create an account until they become power users.

It shows how users reach their milestones with your product. This means that, in order to track it, you need to define what actions indicate that they have achieved a milestone first. And then use a tool (like Userpilot) with custom events and goal-based tracking to have a bird’s-eye view of the entire user journey.

User navigation behavioral data

User navigation data refers to the sequence of steps a user performs in order to complete a particular task.

This data can be collected with event tracking, which is like a key that lets you see the attributes and user activity on a specific process inside your product. Plus, they can be combined to create “custom events” to track them as a group.

These tracked events can trace the whole activity of a user throughout your app, which can help you determine which events and actions are critical for achieving a specific goal.

Session recordings

Session recordings are video replays of users interacting with your product, capturing everything from mouse movements, clicks, and keystrokes, to scrolling behavior and page navigation. Essentially, they allow you to watch over your user’s shoulders and study everything they do.

The value of recordings is that it not only shows what the users do, but why they’re doing it. For instance, you can find out why users are not using a feature properly or reveal how a UI element is causing a dead click.

This way, you can uncover friction points, understand user behavior better, and optimize the product experience accordingly.

User survey data

Only users can tell you how they feel when performing a specific action.

That’s why survey data is extra valuable for understanding user behavior. It provides quantitative and qualitative data that will make you grasp your users’ sentiments and pain points behind every action.

Now, there are two types of surveys that you need to use to get a complete perspective on your customer’s sentiments and needs:

• Relational surveys. Which are meant to understand the relationship your customers share with your product. They’re best used as quarterly or annual check-ins to track changes over time and understand trends (examples: NPS and PMF surveys).
• Transactional surveys. These are surveys that are automatically triggered after a specific interaction between the customer and the product (e.g. after purchasing an upgrade). They’re ideal for gathering immediate feedback to, for instance, understand if users are engaging with a new feature (examples: CSAT and CES surveys).

Types of behavioral analyses to conduct

Now, what can you do with the behavioral data you gather?

That’s what these types of behavioral analysis are for. So, let’s go over five of them:

Path analysis to see how users navigate

Path analysis is particularly useful for identifying the happy path—the ideal journey where users can achieve success with minimal obstruction.

To detail this process, you need to set starting and ending actions through tracked events. For example, a path that starts when a user lands on your pricing page and ends when the user proceeds to upgrade their plan.

Then, the goal is to trace the most common path among your most successful users and see what steps lead users from point A (start) to point B (end) most effectively (you can use any product analytics tool for this).

With a repeatable process to achieve success you can, for example, create and trigger in-app guidance to pull other users into the happy path and increase the number of successful customers.

Funnel analysis to identify drop-offs and friction

Funnel analysis allows you to watch how users move through the stages of the user funnel.

For this, you first need to define the steps within the funnel. Where does the awareness stage begin? What steps come before the purchase?

With a well-designed funnel (and systems in place to track it), you can then understand where users leave before getting to the other end. These drop-off points indicate areas of friction that deviate users from the desired path—which you can often fix.

For example, if you’re having difficulty upselling higher tier plans to your users, maybe because they haven’t realized the “need” to upgrade. So, you can try promoting educational webinars or some sort of special content where you can educate users on how to use advanced products—and incentivize them to move toward the “revenue” stage of the funnel.

Trends analysis to gauge how users interact

Trend analysis involves tracking specific metrics over a particular time frame, painting a clear picture of how trends happen and fluctuate over time

Suppose there’s a spike in feature usage from users with a standard plan. Watching trends lets you identify if a specific campaign resonated with this segment or if a change in the product led to this spike.

At the same time, trend analysis can also identify sudden drops in user interaction. So if there’s a sudden decrease in daily active users from 6-month-old customers, maybe your retention efforts are not making any effects, and you should check it out before more users start churning.

Cohort analysis to track behavior changes among user segments

Cohort analysis looks at how different segments of customers behave over a certain period of time. You can identify cohorts based on their plans, use cases, or in-app behavior, and then analyze them using a behavioral analytics tool.

This analysis can hold tremendous value in understanding the activity of a segment. For instance, if you want to analyze the conversion rate of users on different plans, cohort analysis can help you compare their conversions in a period against the previous period.

This way, you can identify if these changes are happening due to specific changes in your product or because of a campaign. And then double down on what’s working and fix what’s causing unnecessary friction.

Survey data analysis to find improvement opportunities

Survey data analysis can open up a plethora of opportunities for improvement if you know how to read user feedback.

How do they feel about your product? What’s frustrating them? What do they value the most? How do they want to feel?

For example, you can identify ‘passives’ and ‘detractors’ using a NPS survey. But if you add a qualitative follow-up question to the survey, you can uncover reasons why they’re choosing a lower score.

This brings an opportunity to find recurrent issues that could be fixed (like improving the usability of the app). Plus, if you use a tool that allows you to tag NPS responses, you can quickly filter responses by keywords and address issues on “customer service” or “performance” (or even reach out to detractors to see how you can help them).

User behavior analytics examples for driving growth

Knowing very well how to use the different types of behavioral analytics, let’s look into some examples where you can apply these for continuous improvement.

Use cohort and trend analysis to identify at-risk customers and prevent churn

Combining cohort and trend analysis allows you to understand trends happening in different user groups. You can find out if there’s a segment that’s decreasing its usage of a core feature over time and presenting symptoms of churning.

To do this:

1. Identify and segment your cohorts based on when the user signed up, their role, demographic data, etc.
2. Monitor the data to spot trends within each cohort such as their time spent on your product or the features they’ve been using (or ignoring).
3. Compare the data of every cohort to identify which metrics are experiencing uncommon decreases and understand what values are normal.

Let’s say there’s a segment of users who signed up in Q1 of the year and have decreased interaction with a key feature in the last month. This could mean a failed retention strategy (and therefore, a risk of churn).

Then, you can execute a customer retention strategy—like sending targeted help—to increase user engagement with said feature and prevent potential waves of churn.

Use funnel and path analytics to boost conversion

Integrating funnel and path analysis is like a cheat code to pave the way toward success. It helps you determine the happy path, understand the key steps in the conversion funnel, and find ways to improve customer success.

To put a thorough example: Suppose you use path analysis to find out that users who convert the most often follow this path:

1. Read a blog.
2. Visit your pricing page.
3. Take a free trial.
4. Go through the onboarding process.
5. Engage with core features regularly.
6. Upgrade to a paid plan.

However, it turns out that many users deviate from this path during onboarding because they dismiss the guides entirely, just to later get stuck.

Then, your mission becomes to find ways to get users back to the onboarding process. You might choose to implement an in-app knowledge base so stuck users can find answers quickly, or trigger additional in-app help to those users who skipped the onboarding and are now stuck.

Leverage survey data and segmentation to offer targeted help

Segmenting survey data serves as a powerful tool to offer targeted assistance to your user base. It can pinpoint common pain points that those users share and where you can provide proactive help.

For example, if you segment passive users or users with low Customer Effort Score (CES), you can deliver tailored support to address their issues effectively and cultivate positive relationships at scale.

For this:

1. Identify the survey answers that indicate a need for assistance.
2. Segment users based on those answers (like in the screenshot below).
3. Offer personalized help based on the identified needs of each segment.

Used wisely, it can turn your product experience from a struggle into a streamlined journey.

Best behavioral analytics tools to gain actionable insights

That said, which products in the market that aren’t called Google Analytics can fit your needs? Let’s explore three of the best behavioral analytics tools for SaaS business that we know:

Userpilot

Userpilot is a product management tool with robust product analytics features.

This means, it not only helps you watch over behavioral data, it also provides multiple ways to collect data and user feedback.

For collecting data, Userpilot offers feature tags to measure any user input. It brings built-in tracking features to watch over the performance of in-app experiences. And, it has goal-based tracking to monitor how many users are achieving specific milestones (influenced by your in-app flows).

Userpilot also lets you create and trigger in-app surveys inside your app, such as NPS, CES, and CSAT surveys (so you can collect feedback automatically).

As for analyzing data, Userpilot has easy-to-set-up dashboards where you can:

• Watch over segmented audiences to find common patterns and responses on specific groups of customers.
• Use feature analytics for tracking the performance of your product and how features are used by customers.
• Check product usage data to keep track of how many unique users are using your product, how often they use your features and their performance.
• Combine funnel and path analysis to have a bird’s-eye view of how users move through the funnel, how they deviate from it, or how they achieve success with your product.
• Analyze survey data and measure metrics such as NPS and CES. It also allows you to tag qualitative responses to find common keywords among detractors or promoters.

Amplitude

Amplitude is one of the most advanced analytics tools available to product managers. It offers all the analytics functionality that you would expect from a top-of-the-class tool, like advanced user segmentation, funnel analysis, and retention analysis.

It excels particularly at behavioral journey analytics, as it has cross-platform analytics which allows you to track how users move between your native apps, mobile apps, and web pages. But as with many advanced tools, its learning curve can be quite steep for non-technical users.

Mixpanel

Mixpanel is a powerful real-time analytics platform that can keep track of your product usage and user behavior while supporting both mobile and web apps.

It’s particularly good at tracking trends (like in the screenshot below), as it shows data in real time and visualizes how your retention, usage, and growth metrics change over time.

But despite having a free plan, Mixpanel enterprise offers can get expensive and require an engineering team to set it up—just like many advanced tools in the market.

Conclusion

Behavioral analytics is an indispensable tool for understanding how your users interact with your product, as well as finding ways to add more value to their experience.

With user behavior data and actionable insights, you can improve your product’s usability, efficiency, and performance.

But remember, you need the right tool to unlock these valuable insights. So why not book a Userpilot demo to see how you can upgrade your product experience?