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Behavioral Analytics 101: What is Behavioral Analytics?

8 min read

Behavioral Analytics 101: What is Behavioral Analytics?

What is behavioral analytics?

What is Behavioral Analytics? Behavioral analytics are tools that provide a clear understanding of how users engage with digital products, beyond simple metrics like clicks and pageviews. Teams rely on behavioral analytics to make better business decisions around growth, acquisition, engagement, and retention.

Behavioral analytics is a type of business intelligence that uses advanced tools to analyze users’ behavioral patterns. The goal is gleaning insights and predicting preferences in a world where consumers experience more choice (and display less loyalty) than ever before. A good behavioral analytics tool can help you identify and analyze the many factors that affect your company’s ability to acquire customers, and then to retain them.

Why does my business need behavioral analytics?

Jerry Seinfeld observed this about satisfying audiences: “The rules are constantly changing. a liquid, and that’s the jumping on the ocean surf and trying to swim.” We think this is a great metaphor for the market your business is trying to thrive in. It’s fluid—shifting subtly, sometimes dangerously. And the only way to keep an accurate eye on this moving mass of metrics is to employ data analytics, because by the time you can confirm the existence of a new market trend, you may have missed the chance to capitalize on it. (Or survive past it!)

A recent Heap survey Forbes article found that while 43% of users think that the majority of websites were not designed around the needs of the user, a full 95% of product teams believe that it’s “somewhat” or “very” easy to navigate or use their site! What causes this discrepancy?

One answer is that many teams rely on outdated analytics to get information about their customers. Most vendors require organizations to manually hardcode event data in their customer journey touchpoints for analysis. This approach often requires extensive labor, causes slow development, and doesn’t allow you to segment user behavior in useful ways as you develop your app or product. Complete insight into users’ activity patterns lets businesses zero in on how to turn clicks into conversions.

Enter behavioral analytics.

If you're ready to move on to a more thorough evaluation of behavioral analytics tools, read our complete guide.

How do behavioral analytics tools work?

Simply put: first, they collect the reams of raw data created when users interact with websites, mobile apps, and other digital products. Then they provide ways to comprehend what the data is saying. Teams can then use this information to make business decisions around growth, acquisition, and retention.

Learn more about conversion in our CRO guide, featuring CRO calculator!

What's the difference between web analytics, product analytics, and behavioral analytics?

  • Web analytics aka outdated) tools, like Google Analytics, were built to assess page traffic and marketing spend. While they’re still pretty good at that, these tools were never designed to provide transparency into a modern customer journey, let alone handle sensitive data securely. It’s simply not possible to use these tools to see what kind of value users get from the digital experience you provide, since they can never show you what users are doing: what they prefer, where they get stuck, where they drop off, etc.
  • Product analytics refers to a more robust set of tools that help product teams monitor and optimize their digital experiences. Product Analytics provides detailed insight into the user's behavior. Rather than having to guess or rely on customer interviews to address problems and identify opportunities, teams can see in real time how well their decisions meet user needs. (Some product analytics solutions still rely on manual implementation, which in our opinion is not much more practical than Google Analytics.)
  • Behavioral analytics is essentially the process of using your product analytics tools to gain the deepest understanding of the users you have right now. When Amazon or Netflix make recommendations for you, they are basing those suggestions on calculations derived from activity you’ve taken on their sites. Simply having demographic data, like a zip code and income bracket, doesn’t clue them in that you like Jim Carrey movies or roast your own coffee.

The best user behavior analytics tools collect this kind of data automatically, and have near-infinite flexibility in what they allow you to do with the data once it’s collected.

How can behavioral analytics help teams get to product-market fit?

The goal of any analytics initiative is to investigate user activity closely to make informed decisions that enhance your product and improve your business. A great behavior analytics tool will help you discover the "unknown unknowns" within your product and your audience. These include the situations, circumstances, pitfalls, and possibilities that aren’t immediately apparent. Now you can develop strategies that fit unmet needs, as well as gain insights towards improving the overall user experience—increasing the potential for conversion and sales.

By taking full advantage of behavioral analytics, you can gain the deepest insight into the users you have right now, study your highest-quality customers, and develop methods to attract more just like them.

How do teams across the business benefit from behavioral analysis?

Marketers can use metrics on how their best customers behave to attract more users just like them. They can develop strategies, improve website content, publish relevant white papers and articles, and develop email campaigns that entice new users to get the most out of the product. Teams can learn:

  • Which users are opening (and reading) our emails?
  • Who is reacting favorably to our marketing methods?
  • Which of our ads are most effective?

Learn about the best metrics for analysis in our complete guide!

Product and eCommerce teams can use behavioral data to find out which activities correlate best with adoption and retention, then spend their time developing functionalities and building integrations that encourage the behaviors associated with high conversion rates. They can ask:

  • Where do users click most frequently?
  • How do users react to new features and updates?
  • Where are users getting stuck or abandoning the app?

Growth teams can study user behavior, to map out user journeys, develop KPIs, and optimize funnels to get a deeper understanding of customer relationships with the product. Teams can explore questions like:

  • How can we increase product usage exponentially?
  • How can we make it easier for users to accomplish their goals?
  • What are some new ways to generate revenue?

Customer Success teams can use behavioral analytics to improve retention and create more opportunities for expansion, as well as to prioritize their limited time by focusing on the customers that most need their attention. Teams can learn:

  • Which customer behavior indicates likely churn?
  • Which customers are using the features that suggest they’re ready to expand?
  • Which customers are using the features we’ve created for them, and which aren’t?

What are some key features to look for in a behavioral analytics platform?

Ease of use is paramount! Some tools need detailed event tracking and require a lengthy set up, which will delay the time to value. Teams also need analytics specifically built to mine the captured dataset for hidden opportunities and points of friction.

Autocapture is a critical feature that makes implementation easier, and allows anyone access to the data they need to analyze user behavior and make critical business decisions. Autocapture is what makes retroactive inquiries possible—you’ll never forfeit insights from data you neglected to track. But just having 100% of the data isn’t enough.

Proactive Insights do the data science work in advance, sifting through data to show teams what they should be looking at. While many analytics tools require you to dig through data yourself, an analytics tool built to ingest raw data uses machine learning to direct you to key moments of friction and opportunity in your product, even if you haven’t been paying attention to those moments.

Step Suggestions are tremendously useful features enabled by machine learning that automatically flag and surface site activities that warrant a closer look. These moments of unseen friction and opportunity can be tremendously valuable. For example:

  • Are there events exhibiting significant dropoff that aren’t currently being tracked?
  • Are there problematic steps in a funnel that you should remove?
  • Or would adding MORE steps make a given process easier for users?
  • Which user segments have the greatest impact on conversion and engagement?

Effort Analysis is another first from Heap—the ability to quantitatively measure friction. Exactly how much difficulty (time spent, number of site visits, interactions between steps) do your users face when moving through every step of every user flow in your digital experience? When you can make friction tangible, it helps you prioritize the fixes that will have the biggest impact on your users’ experience. (Learn more in our video!)

Virtual events are simulated events you define in your analytics tool. You can apply labels to raw data without modifying that data or affecting your codebase. Teams can assign multiple labels to the same data, even retroactively. This keeps both your dataset and your analyses clean and trustworthy, so it’s organized and easy to modify or merge as your business evolves.

Cohort Analysis lets you group users to discern actionable discrepancies between them. With this segmentation, you can isolate low-performing groups, find out why they churn, and focus development efforts on customers least likely to churn in the first place.

Learn more about cohort analysis in our complete guide!

Data governance features make sure your data stays protected with controlled access, so you can maintain a trusted, up-to-date, and accurate dataset. Categorized events, segments, reports, personal sandboxes, and staging environments keep your accounts clean and organized.

Learn how to govern data at scale.

Profiles let you establish user categories around the criteria of your choice, as well as create custom user models. All kinds of conversion funnels can be created to explore different paths to conversions, and anomalous behavior, from a “learn more” click to a purchase.

Journey Maps are something new and unique from Heap—the first analytics tool ever that can compare multiple paths leading to the same goal, as well as measure the impact of optional steps in a funnel. By surfacing the myriad journeys users take through your digital experience, you can quickly and accurately test which behaviors affect conversion.

  • Which steps are users skipping...and which users are skipping them?
  • Are users who complete a given step in the funnel more likely to convert?
  • Are users taking alternative paths to their goals that you haven’t anticipated?
  • Are customized features confusing your users more than helping them?

When you understand your customers' real journeys, that’s real power.

For a more thorough guide to behavioral analytics platforms, download our evaluation guide.

What integrations should behavioral analytics work with?

Connecting to external sources like your warehouse and third-party tools (such as your CRM, email marketing, testing, social media, accounting, and payment systems) lets you blend that information with behavioral data to answer more complex questions. Some popular API integrations are Stripe, Shopify, Salesforce, Marketo, and Optimizely. The more integrations your product analytics solution can accommodate, the better.

What makes Heap a good choice as my behavioral analytics platform?

We believe the twin pillars of quality behavioral analytics are automatic data capture and data-science-driven insights. Autocapture is a feature unique to Heap that requires only a single snippet be installed in order to capture all of your user data, all the time. Eliminating manual tracking efforts saves massive amounts of time, since you don’t need to plan all your inquiries in advance.

Then, data science algorithms sort through that data for you to point you in the most important directions. It’s a one-two punch that lets your teams experiment with the data anytime, and in any way they see fit.

Our latest knockout blow is the unveiling of our Illuminate features, the next generation of proactive analytics tools. Heap Illuminate makes it easy for digital builders to find the most valuable insights from user behavior and prioritize the fixes that lead to the biggest business impact. We believe the path to success is obvious, once you know where to look.

Heap helps you quickly turn insights into action for informed decision-making geared to give customers what they want most. Everything we do is designed to help you understand your business better. Reach out and learn how we can help you accelerate your company’s goals.

Getting started is easy

Interested in a demo of Heap’s Product Analytics platform? We’d love to chat with you!