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Behavioral Analytics

Introduction to behavioral analytics

Understanding our customers and their problems is one of the most important roles of a product manager. After we ship a feature, the most pressing question is, “Are people using it, and how?

We should know if the right assumptions and decisions are made according to users’ expectations.

Understanding the impact of decisions on the users of the product requires a deeper layer of analysis – a behavioral analysis.

Customer-centric teams are aware of user behavior. Customer-centric teams have:

  • A complete understanding of the user’s challenges.
  • Insights into features that customers use and what they do not use.
  • Knowledge of how their customers get the most value from their products 


What is behavioral data?

Behavioral data is the raw data that is generated when users click, swipe, and navigate through a site or app. You can view the data in aggregates to understand different types of behaviors and reactions that are most common among the users, this is also called user flows, which shows the order in which users have taken the actions. Behavioral analysis relies on behavioral data. 

What is behavioral analytics and why is it so important?

Behavioral analytics is the data that shows you how your customers behave on mobile applications or on websites. It goes beyond basic metrics like just monthly active users. Behavioral data reveals how engagement with your product impacts retention, conversion, revenue, and the other evaluative outcomes you care about.

Understanding user behavior is necessary to increase engagement, retention, lifetime value, conversion rates, and most importantly, revenue.

The building blocks of behavioral analysis is termed as events. Events represent any activity your user can perform in your product (like opening the app, creating an account, viewing a video), or any activity associated with the user (like making a purchase).

Sending optimal event data to your analytics platform is the crucial step towards understanding your user's engagement with your product. If you are too hasty in instrumenting your analytics you may never get the complete value of your data.

Why should companies use behavioral analytics? 

For most products, marketing, and analytics teams live in constant pursuit of the question, “How are customers using the product, if at all?”

Behavioral analytics software provides concrete answers with a visual interface where teams can segment the users, run reports, and note customers’ needs and interests. 

Without behavioral analytics, teams are stuck using insufficient demographic data and so-called vanity metrics.

Behavioral analytics can provide answers to questions like these:

  • What do users click on within the product? 
  • Where do users get stuck?
  • How do users react to feature changes?
  • How long do users take from the first click to a conversion?
  • How do users react to marketing messages?
  • Which ads are the most effective?
  • Can the team nudge users to be more successful?

Conducting behavioral analysis is more complicated than just running simple reports in the analytics tool.

Teams must first clearly identify what they want to achieve from the analysis and write down the paths they expect users to take instead. Only with the preset expectations can teams correctly identify whether users are deviating from the ideal path and help redirect them.

What are the 10 steps to analyze user behavior?


Step 1: Define your business and analytics goals. 

What is your team working towards? What is your goal? Think of all the business objectives you want to achieve. Once you have your overarching goal established, think of how can you work towards this goal? Establish the Key Performance Indicators (KPIs) that you are focused on improving to achieve your goal. 

Let us say your business objective is to increase income. Your KPIs may be:

  • Increase onboarding conversion.
  • Increase retention for paying customers.
  • Increase checkout funnel conversion.

It is essential to define these important points before you start thinking about your data classification so that you can ensure you are sending the right events to the right project in order to track your KPI’s.

Step 2: Mapping out the critical paths that align with your goals 

Critical paths are a sequence of actions a user takes that aligns with the product’s purpose.

An example of an e-commerce product would be: 

Search > Browse Products > Add to Cart > Checkout > Order Confirmation 

For a gaming product, a critical path can begin when a user opens the app and is prompted to register and later taken through a game tutorial.  

Suppose you have a gaming app. A critical path could be broken down into four distinct events:

Opens app > Registers > Verifies account > Completes tutorial 

Make sure to track only the events that are essential to boost your business and analytical goals.

If needed you can always add more events later.

Step 3: Organize your event classification  

Behind every great user behavior analytics is a great event classification i.e. the way you organize this collection of events and properties that you are using to define the actions people can take within your product. Think of the event classification as the foundation for all future analysis.

It is crucial to get right! 

If you are not using a product analytics platform to organize your event classification, you will be likely to use a spreadsheet to keep track of all the names of events, event properties, and user properties observed from the analytics.

Step 4: Understand how to identify users. 

Most analytic platforms require you to configure identifiers like a username or email. 

For example, in their mobile SDK or HTTP API for keeping track of unique users. This lets you collect data from multiple devices and sessions to one user. Another important thing to note -

Most analytics platforms count unique users when they catch a new device or a new user ID (only if the user is signed in). A challenge arises when the device anonymously logs in to the event that was performed by a user already recorded in the system.  


Step 5: Decide if you need cross-platform behavior analytics. 

If your product exists on different platforms, like mobile and web, should you accumulate all your data together or keep it separate? 

Well, it depends on the product. If you are guessing that user behavior is going to be different across platforms, you will want to know how each platform performs separately from the others so cross-platform will not be a priority. But, if you want to understand user behavior across the entire user’s history, you should make sure that your analytics solution can do cross-platform instrumentation of the data. 

Grocery shopping apps are a perfect example of a product that utilizes cross-platform tracking because they want to know how people are using their product across mobile and web.

Step 6: Establish the ‘Minimum Viable Instrumentation’. 

Once you have spent some time thinking about how to set up your analytics and organizing your events i.e. steps 1 through 5, it is time to start accessing some basic app metrics.

Step 7: Track your events. 

Start tracking the events and the critical paths you brainstormed in Step 2.

You do not necessarily have to track every action possible in your app, but do make sure to evaluate events that are key steps in onboarding, conversion, and retention.

Step 8: Setting user properties and event properties. 

Assigning user properties and event properties can give you deeper insights into the behaviors that your customers are exhibiting as they engage with the app. 

A user property describes various attributes of an individual person using the app (e.g. age, gender, location).

An event property describes a property of an event like, how long someone performs the event.

Step 9: Verifying user behavior events if they are being tracked properly. 

How do you know if you have instrumented everything correctly?

Use a test device to go through the app. If you can view your analytics in real-time, you should be able to see your device shooting events at each step. 


Step 10: Start analyzing user behavior. 

Instrumenting your user behavior analytics events is a huge accomplishment. It is a meaningful investment in giving your team access to the data they can use to measure the impact of product decisions.

When you are fully instrumented, it is time to start implementing this data in - 

  • Creating behavioral cohorts.
  • View the critical paths and increase conversion with funnel reports.
  • Calculate user retention over time.
  • Run experiments.
  • Run campaigns.
  • Measure the impact of new feature releases.
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