What is behavioral analytics?
Behavioral analytics is a tool that reveals the actions users take within a digital product. It organizes raw event data such as clicks into a timeline of each user’s behavior, also known as a user journey. Teams use behavioral analytics to determine what users like and don’t like and, by inference, what adjustments can make the product more valuable.
What is behavioral data?
Behavioral data is the raw event data that’s generated when users click, swipe, and navigate a site or app. Teams can view the data in aggregate to understand which behaviors are most common, or look at journeys, also called user flows, which show the order in which users took the actions. Behavioral analysis relies on behavioral data.
Why should companies use behavioral analytics?
Most product, 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 users, run reports, and deduce customers’ needs and interests.
Without behavioral analytics, teams are stuck using insufficiently detailed demographic data and so-called vanity metrics. As Streaming, Sharing, Stealing co-author Michael D. Smith explained to The Signal, if a company wants to personalize its service to users, it needs their behavior data. A streaming movie platform can’t know that a user loves horror films, for instance, simply based on their age, gender, or nationality.
Behavioral analytics can provide user-level data so teams can answer questions like:
- What do users click within the product?
- Where do users get stuck?
- How do users react to feature changes?
- How long do users take from first click to 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 simply running reports in the analytics tool. “Analyzing generic data doesn’t magically produce answers to unidentified problems,” wrote Drew Hendricks, a technology writer for Inc. Teams must first identify what they want to achieve and write down the paths they expect users to take. Only with preset expectations can teams identify whether users are deviating from the ideal path and redirect them.
The tech-driven insurance provider Lemonade, for instance, adjusted its user paths to increase revenue. The team knew their goal was to convert more website and app visitors into paying customers and with Mixpanel analytics, they noticed a “staggering drop-off” in user flow right before the point of purchase. By analyzing the page where the drop-off occurred, the team realized it was due to a technical error and a weak call-to-action (CTA). They fixed the bug and reworded the CTA, which led to a 50 percent increase in the number of users who purchased additional coverage.
Why behavioral analytics is different
What sets behavioral analytics apart from other types of business analytics is that it combines two technologies: user segmentation and event tracking. While some analytics vendors only offer one or the other—user data or event data—behavioral analytics unites the two for a complete customer view. It ties users to the events they trigger to produce a map of their actions, also known as a user flow, or customer journey.
Viewed either alone or in aggregate, user journeys tell stories that teams can use to tweak and improve their product development, marketing, and launch strategies.
Steps to successful behavioral analysis
Customer behavioral analysis requires careful planning and each team’s success is a function of how carefully they implement the analytics tool and how seriously they take their tracking plan.
Behavioral analysis is not a race. The first half of the implementation process should be spent planning and all teams who will eventually benefit from user intelligence need to have a hand in selecting and deploying the tool.
Teams can prepare themselves to conduct user behavior analysis in five steps:
1. Select goals, KPIs, and metrics
To determine whether users are reaching the right goals, such as purchases or conversions, teams must select the KPIs and metrics that indicate progress toward those goals. A fitness app that makes money through monthly subscriptions, for instance, can track paid subscriber growth. An enterprise resource planning (ERP) software that relies on annual contracts, on the other hand, can track users that complete the onboarding sequence.
2. Define the most desirable user journeys
Based on the service or app’s design, what are the most common paths for users to reach their goals? If the product has already been launched, teams can use actual user data to answer this question. If the product is pre-launch, the team can use the design team’s wireframes of the suspected or intended flow.
All user journeys should end in some type of a desirable outcome for the customer or the business. An e-commerce website, for instance, can track a user from their first page visit to adding an item to their shopping cart to checkout because that flow leads to purchases. Alternatively, a streaming music app can track users as they move from its homepage to playing a song and, hopefully, purchasing that song.
3. Create a tracking plan
Based on the user flow, teams can decide which events they’ll need to track within the product. It can seem appealing to track everything but this is a mistake—too much data can clutter the analytics and make useful information more difficult to find. Track events and users based on whether the data is actually useful.
Some events contain, within them, multiple properties. The event for playing a song within a music app, for instance, could contain properties for the song title, genre, and artist.
To keep events and properties organized, companies typically create a tracking plan in a spreadsheet. This acts as a directory of all events and serves as a map for implementing the analytics tool. A tracking plan is a mutable document that should be revised and updated as the product, team, and goals change. To reduce the burden of trying to share and control access to the spreadsheet, Mixpanel offers a feature called Lexicon which stores the event name taxonomy for all to see.
Involve all teams—analytics, product, marketing, and engineering—in drafting the tracking plan. Members of each will need to understand how the users and events are named and organized if they’re going to run reports and understand the results.
4. Set a unique identifier for users
Most digital products today exist across multiple platforms and this makes it difficult to track unique users. One user can appear to be multiple people unless assigned a unique identifier—either an email or string of characters—that persists across platforms and devices and connects the touch points along their journey. Teams should ensure their behavioral analytics platform vendor provides a unique identifier that won’t change over time.
5. Implement analytics and begin event tracking
Once the tracking plan is complete, companies can deploy behavioral data analytics software and use its SDK or API to integrate it with their products. That’s when they assign a unique identifier for users and set up user and event properties as outlined in the tracking plan.
It’s not uncommon for teams to discover additional events they want to track during implementation. This isn’t an issue as long as they update both the tracking plan and the analytics service.
Before the tracking system goes live, teams should use test devices to verify the event and user tracking is firing properly. Once working, teams are ready to begin analyzing their users.
How to apply the results of behavioral analytics?
Most teams study their users with segmentation, which allows them to separate users based on characteristics and behaviors. An e-commerce app, for example, can create a segment for recent users who added items to a shopping cart but then abandoned. Or, they could filter for power shoppers who access the app multiple times a day.
Segmentation allows teams to learn about their users to build more complete customer profiles. They can save user segmentations, known as cohorts, and make adjustments to their product and marketing to make it more profitable with each segment.
Media and entertainment company STARZ PLAY, for instance, segmented users that signed up through its free trial offer and found that some users were gaming the system for multiple trials. By creating alerts for the negative behavior, the product team closed the loophole and saved 8x on its marketing spend.
Here are other industry applications for user behavior analytics:
- E-commerce sites can predict future trends and increase conversion rates
- Consumer messaging apps can increase usage
- Insurance companies can sell additional products
- Travel sites can increase bookings
- Online gaming platforms can attract more users
Teams can track users’ progress toward outcomes such as purchases or signups with funnel reports. Funnels display a series of stages in a user journey, as well as how many users are progressing from one stage to the next. A fitness app could use funnels to see how many users progress from download to signup and purchase. If one stage has a low conversion rate, it’s a signal that that stage needs attention.
Funnel data allows teams to A/B test different buttons, messages, and images, to see if small changes improve conversions. The peer-to-peer shopping app Grabr, for instance, noticed conversions for referred users was low. The team tested new variants of the landing page and increased referral conversions 2x. The data can also be used to personalize parts of an app or website, say, to greet returning users, or present prospects with content relevant to their industry.
Teams can also use funnel data to deduce which behaviors are correlated with high retention. A media site, for instance, could look at the cohort of users that continue to return to the site eight weeks after signup to see if they share certain behaviors, such as a propensity to leave comments.
With a view of what’s happening within the product, teams can run experiments and make alterations to improve the product and help users find more value.
How to choose the right behavioral analytics provider
The best behavioral data analytics solutions are compatible with many systems, offer quick results, and are versatile. They allow teams to integrate their full range of digital products, from desktop and mobile applications to internal services like CRM, social media platforms, and customer support system. They have friendly, intuitive interfaces that allow teams to quickly find answers, and they provide a breadth of functionality, including some that teams don’t yet need, but may in the future.
Swapping out a behavioral analysis platform can be a costly and time-consuming endeavor. Teams can save themselves time and money by evaluating vendors with great caution and investing in a platform that offers them room to grow into.
Look for user behavior analytics tools with:
- The ability to automatically capture user and event data points
- The ability to access access data in real-time and query it in a variety of ways
- Pre-built reports such as funnels, cohorts, and retention
- Guide rails, automated notifications, and recommendations
- A data visualization component