Behavioral Analytics 101 Guide to Improving Your Product

Behavioral analytics can be incredibly powerful for product, marketing, and data teams alike. Analyzing how customers move through your app, for example, can help you build a better product—one that works seamlessly for your users and improves your business metrics.

If you’re new to the world of user behavior analytics, we know it can be hard to know where to start. That’s why we created this beginner’s 101 guide to using behavioral analytics to build better products.

Below, we cover how you can use user behavior analytics to:

• Understand how frequently users are returning to your site or product
• Flag issues in your customer journey to quickly mitigate negative effects 
• Optimize your customer journey by analyzing A/B test results

The first step in putting behavioral analytics to work for you is in measuring your product’s stickiness. Product stickiness is a leading indicator for all kinds of other beneficial business outcomes, so understanding your baseline stickiness is a great first step.

What Is Product Stickiness and Why Does It Matter?

Product stickiness is how well your product creates value that encourages users to come back repeatedly and “stick” around, and it’s a little different than many of the other things behavioral analytics tools measure. It doesn’t correlate with a precise metric you can point to. Instead, you have to define what stickiness looks like for your product.

But defining and measuring product stickiness is crucial for product teams because it helps you gauge how much value your product provides for users—the more value you provide, the stickier your product—and how engaged those users are with the product. The more engaged users are, the more likely they are to stick around.

It’s different from customer loyalty because stickiness is more transactional. Customer loyalty refers to the customer’s willingness to continue making purchases, subscribing, or otherwise giving you money based on a feeling of goodwill or other emotional relationship with the brand.

Product stickiness is about transactional value, whether that’s value created by your pricing, customer experience, product quality, customer support, or something else.

As Help Scout put it, “Sticky customers are customers who continue paying because of convenience or value. Loyal customers are customers who love your product so much that they don’t bother considering alternatives.”

SaaS stickiness, in particular, usually involves usage of the software or app on a regular basis (more on this in a minute). Take a company like Google for example: many users actively use at least one Google SaaS product every day. But products like Gmail and Chrome are much stickier than Google Translate, for example.

What Are the Benefits of Product Stickiness?

Increased stickiness means a few things, including more frequent usage, higher engagement, and deeper product adoption. From a business perspective, that increase can yield a lot of benefits.

• Increased customer retention and reduced churn: Engaged customers see the value in your product, so they’re more likely to stick around.
• Improved customer experience: Sticky products just work for users and do what customers need them to, improving the overall experience.
• Higher customer lifetime value (LTV): With higher retention, more customers stick around for the long-term, increasing overall lifetime value.

Not to mention, the more customers who regularly use your app, the more behavioral data you have at your fingertips to help further improve the product and develop a continuous improvement loop.

How Is Product Stickiness Measured?

As we mentioned above, stickiness isn’t a specific metric by itself—it’s more of a concept. But for many SaaS product teams, in particular, stickiness means active usage. The specific metric you use—usually monthly, weekly, or daily active users (MAU, WAU, or DAU)—depends on your unique product.

For example: a fitness and nutrition app may want to measure DAU or WAU while an ecommerce app measures MAU.

How to Measure Product Stickiness Using Indicative

1. In Cohort tool, start by selecting an initial event that represents a customer using your site or product. The first event of a cohort is required; a user must complete this event to enter a cohort. In this case, let’s use Site Visit. 

2. Now let’s select a target behavior event in Row B. Whereas the first event defines the cohort, the second event defines the target behavior that you would like to observe. Often this is an event that is repeated multiple times, such as a purchase. This is the event that represents the subsequent user behavior that you wish to analyze. In the query builder, let’s select Site Visit once more.  

3. Hit the Run Query button, and let’s review the results.

4. Be sure to Save to Dashboard by clicking in the top right of your screen, so you can reference fresh numbers at a glance at any time.

How Do You Increase Product Stickiness?

Once you have a baseline understanding of your product’s stickiness, then you can set about improving it and measuring the impact of your efforts. As we said, you can drive stickiness via a number of factors, including pricing, product quality, and customer support. For product teams, in particular, it’s all about product quality and customer experience. Here’s how you can increase product stickiness:

Build a Distinctly Unique Value Proposition (UVP)

Every company needs a UVP, and the stronger (and more unique) yours is, the stickier your product will be. Think of your UVP as the moat around your product that gives it a clear transactional value over and above what your competitors offer.

For example: Indicative is the only Product Analytics solution that connects directly to your data warehouse. That direct connection creates a ton of value and convenience for our customers, which helps make our product stickier.

Invest in Onboarding

If product quality, ease of use, or similar attributes are what make your product sticky, onboarding is a powerful tool here. You need to invest in helping customers realize that quality as quickly as possible. That means increasing your adoption rate and funneling users toward their “Aha!” moment as quickly as possible.

For example: If you offer a specific feature your competitors don’t—and it drives your stickiness—your onboarding flow should proactively encourage users to start using that feature.

Improve the Overall Customer Experience (CX)

Convenience and customer experience can both be powerful drivers of product stickiness, so product teams should do all they can to create a positive, seamless experience for users.

That can mean a lot of things—from adding whole new features to the roadmap to removing points of friction from the user journey (more on that next!) and the onboarding we talked about already.

The key is to ensure, at a minimum, that your customer experience doesn’t detract from the transactional value that makes your product sticky.

User friction is anything that slows down or gets in the way of users accomplishing whatever it is they came to your product to do. User friction comes in 3 flavors:

• Interaction friction happens when your product’s actual user interface (UI) and functionality get in the way. That can include both the product design itself and the interaction design.
• Cognitive friction refers to how much mental effort is required by users. For example, analysis paralysis is a common example of cognitive friction. One way product managers and designers can mitigate cognitive friction is by minimizing the number of decisions they ask users to make.
• Emotional friction happens when a user’s own emotions are what create friction. Procrastination, for example, is a type of emotional friction. It’s the job of product teams to understand the types of emotional friction that impact their product and to find ways to mitigate it.

As you can see, it’s a broad topic—there are practically infinite things that can create friction, and your actual product is only part of the equation. But it’s the part you have control over, which is why identifying and fixing friction within your user journey is a key part of behavioral analytics.

What Is Friction in Design?

Both interaction and cognitive friction play a big role in product and design. UX design, in particular, is essentially a user friction crash course. In fact, the primary role of a UX designer is to control friction.

We say “control” instead of “remove” because some kinds of friction can actually be beneficial for both users and companies.

For example: multi-factor authentication is a form of user interaction friction—it slows down the process of logging in for users. But it also makes an app more secure.

Another common use of intentional friction is for certain nonreversible user actions, to confirm the user’s intent before the action is taken.

What Happens When There Is Too Much Friction?

While some intentional user friction can be beneficial, too much friction is a big problem for product teams. When users face too much friction…

• It harms the overall user experience
• The user journey comes to a halt and conversions drop
• Customer retention suffers

That’s largely because customers don’t like friction (for obvious reasons). Users want their digital products to be easy and quick to use. They want to get in, do what they came to do, and move on with their lives.

When friction gets in the way of that, customers are likely to get frustrated and look elsewhere to solve their problem. While they’ll accept some friction in exchange for benefits like increased security, it’s a fine line—they need to get a clear value back that validates friction.

That’s why identifying and eliminating unnecessary friction within your product is a key part of product management and behavioral analytics.

How to Find Points of Friction Using Indicative

1. In the Funnel tool, search and select the events in the steps that are part of the customer flow for subscriber conversion. In this scenario, the steps involved in subscribing include: Email Clicked, Site Visit, Create Profile, and Subscribe.

2. Once you run the query (by clicking the play button), you will see a percentage between each of the steps in your funnel. The number between the first two circles on the left represents the conversion rate from Step 1 to Step 2.

3. Click the push pin icon on the Create Profile step of the funnel or in the query builder to unpin that step. This means you will be able to see the conversion rate when a user creates a profile versus when they do not.

4. Next, click Settings and under Path Exclusivity, ensure Exclusive is selected. This will enable you to measure the impact of the optional step on the conversion rate.

5. In this example, we can see that the conversion rate to Subscribe is higher when customers create a profile, compared to when they do not. We can interpret this to indicate that the create profile page is not a point of friction, and in fact may aid conversion, since more customers who create a profile tend to convert compared to those who do not.

Note: Do not worry if your numbers don’t match – the data shown is relative to when you personally run your own query

Once you’ve identified areas of friction within the app, it’s time to get rid of them and optimize the overall journey through your product. When you’re ready to start making changes, it’s crucial to use A/B testing to measure the impact of your changes and ensure you don’t roll out updates that actually hurt the user experience.

What Is A/B Testing?

A/B testing is a process of collecting real customer journeys and other behavioral analytics data across 2 different versions of a product, feature, or other digital asset.

A/B testing 101: The “A” and “B” in A/B testing represent the 2 versions.

By collecting and analyzing this data, you can understand the full impact of product changes—before you roll them out to your entire user base. You can see whether proposed changes actually improve the journey and reduce friction, for example, and you can ensure product updates don’t create additional friction elsewhere.

While A/B testing is the most common process for product, design, and marketing teams, you can also employ multivariate testing, which involves testing versions that differ based on multiple variables. Multivariate testing is more advanced, so we recommend getting started with straightforward A/B tests.

Why Should You A/B Test?

In short: A/B testing is one of your most powerful tools to put the data behavioral analytics tools offer to work to actually improve your product. It’s all about optimizing your product for the business outcomes and KPIs it drives and taking a data-driven approach to proving that specific changes will improve those metrics. Because without testing, you’re effectively just guessing when you make product changes.

For example: you can use A/B customer journey testing to…

• Increase conversion rates by testing things like the color, placement, and copy of your calls-to-action (CTAs) or entire landing pages
• Improve specific product metrics like bounce rate, engagement, churn, adoption rate, active usage, and more.

Beyond concrete metrics, A/B and similar user journey testing help your team develop a better understanding of user behavior and how you can influence it. By analyzing how small changes impact the way users move through and interact with your product, you can get to the heart of behavioral analytics: why users behave the way they do. 

Understanding that “why” is a really powerful tool that can make every team—from product and marketing to customer support—more effective.

An Example A/B Test

Here’s an example from one of our customers, a B2C company that sells wine subscriptions. When they first signed up for Indicative, one of the big questions they wanted to answer was around checkout conversions. They wanted to know which checkout CTA would yield the highest number of conversions.

This was a perfect use case for A/B testing. By using the A/B testing features within Indicative, they could test everything from CTA color to size to copy to placement. They could test several versions at once and see the impact of each on conversions—before rolling out a change across the board.

By doing so, they could quickly land on the highest converting version of their checkout CTA and start increasing their conversion rate in a hurry.

How to Perform an A/B Test Using Indicative

1. In the Funnel tool, let’s add 3-6 steps that are part of the product flow you want to analyze by searching and selecting different events. Let’s take our series of steps from last time — a paid subscriber conversion flow for an e-commerce business. Our events are: Email Clicked, Site Visit, Create Profile, and Subscribe.

2. Layer on the event property A/B Test by selecting AB Test after clicking group by in the first row of the query builder, similar to the image above. 

3. You will see each funnel step broken into all of the test variant groups. In this case, we have groups A and B, where group A had a higher conversion rate. Click on each slice of the funnel steps to see what percentage of each variant converted, or view this in the table on the bottom of the screen.

 

How to Analyze the A/B Test Results Data

Analyzing the results of your A/B tests is super easy using Indicative. Simply add “A/B Test” as an event property on your customer journey Funnels and our tool will automatically break out the conversion rate for each variant at each step of the funnel.

With other A/B testing tools, there are a few ways to analyze results. Most dedicated A/B testing tools will show you a report that summarizes results and shows you what the overall conversion rate for each version was and which version “won.”

To go beyond that, you’ll likely need to use a tool like Google Analytics. Once you integrate your A/B testing tool with Google Analytics, you can analyze results by building a custom report and pulling in any data you’re interested in. You may want to know, for example, which version had the highest average order value instead of the highest conversion rate.

The key here is to have a specific hypothesis at the beginning of your test—a clear reason for why you’re performing the test and what you’re trying to improve. With a clear goal, analyzing results can be really simple.

We recommend building a customer journey dashboard that can help your team keep a constant pulse on all the analyses we covered above. Your dashboard should include:

• Your unique metrics for product stickiness
• Friction indicators like low conversion and other key product metrics
• User journey funnels
• Active A/B test results

If you use a Product Analytics tool like Indicative, you can quickly and easily build dashboards and add any analyses you perform to it.

How to Set Up Your Dashboard in Indicative

Adding Queries to a Dashboard

In Segmentation, Funnel, and Cohorts, you can add a query to your customer journey dashboard by clicking the Save to Dashboard button in the top right-hand corner. In the Funnel tool, you will be given a few options for how your widget will display in the dashboard. Select as many as you would like displayed in your dashboard, and each will become a separate widget.

Rename and Delete Widgets

Select Settings to rename the dashboard. Click on the name of the widget to edit the individual widget.

Setting up Scheduled Reports

Once you have built your dashboard to your liking, now is when we suggest you schedule reports to be sent on a cadence of your choosing. 

Users can schedule their dashboard results to be sent to other Indicative users, executives, or external partners and stakeholders. Once a report is created, your dashboard will refresh at the selected date and time, and a PDF will be sent to the selected individuals. 

In addition to the PDF snapshot, Indicative users may access your dashboard to view results in real time. Any teammate with access to your Indicative project can create a Scheduled Report for an existing dashboard.

Now, let’s dive into how to create a Scheduled Report.

To create a scheduled report, you must first create a dashboard to send it from. If you already have a dashboard that you’d like to use, navigate to that dashboard using the View dropdown in the top navigation menu. In this case, we will use a previously built dashboard called “Test Dashboard” for the purposes of this demonstration.

Once you have selected your desired dashboard and changed your dashboard layout mode to Print Mode, click to open the Reports dropdown in Dashboards settings  in the top right of the dashboard. 

Here, you may create a new Scheduled Report or view your existing Scheduled Reports as shown below:

You are now ready to build a dashboard and share it with your team, congratulations! 

For more information on using advanced features, read our support documentation on Dashboards & Reporting. 

Analyzing customer and user behavior isn’t a one-and-done kind of thing—it’s an ongoing process that requires product teams to create a continuous improvement loop, constantly iterate, and make incremental improvements.

Ultimately, that’s the end goal of behavioral analytics—to build better products by understanding what about your product keeps users around, how they move through your app, and where you can improve the overall user journey.

With the step-by-step process above, and a capable Product Analytics tool like Indicative, you can build that feedback loop based on accurate, up-to-date product data.