Why You Should Start Using Behavioral Segmentation
What is Behavorial Segmentation?
Behavioral segmentation is a type of analysis that sorts users according to their behavior in an app or website and looks for patterns among different groups. Depending on your site, behaviors may include purchases, clicks, likes, media plays, uploads, page views, data entry, or social shares.
Behavioral segmentation is a powerful way to group users. It helps marketing and product teams learn how different types of prospects and customers are likely to use their product, how engaged they’ll be, and how long they might remain customers. Before we dive into ways you can use behavioral segmentation to improve revenue, retention, and ROI, let’s take a closer look at what it is.
What is Behavioral Segmentation? Behavioral segmentation is a type of analysis that sorts users according to their behavior in an app or website and looks for patterns among different groups. Depending on your site, behaviors may include purchases, clicks, likes, media plays, uploads, page views, data entry, or social shares.
This approach is used by product teams to see which user behaviors correlate with better engagement and retention, so they can modify their product or strategy accordingly. As teams analyze the behaviors of their customers, they look for segments that clearly stand out as higher value — users of a specific feature that tend to stick around for longer, people who make frequent purchases that favor your mobile app over your website, or customers with a high annual contract value who all use the same pre-built integration.
In another article we dig into cohort analysis, a subset of market segmentation that examines users’ actions within a specific time period, and explain how it can be used to lower churn.
By segmenting users based on their behavior, you can pinpoint factors that contribute the most to your company’s goals, better target customers, and prioritize your efforts towards maximizing the behaviors (and product engagement metrics) you’re trying to produce.
Behavioral segmentation. Here you can see that users who did create a report had a better retention rate than those who didn’t.
Demographic Segmentation vs Behavioral Segmentation
Most of us are familiar with the idea of demographic segmentation, in which companies group users according to demographic data: location, age, gender, company, and so on.
So what’s the difference between demographic segmentation and behavioral segmentation?
Demographic segmentation is useful for figuring out which users you should be targeting
Behavioral segmentation is more useful for optimizing your product experience
Put differently: to drive measurable improvements on usage metrics — adoption, retention, conversion — brands are better served by digging into user behavior, not just user traits.
How Behavioral Segmentation Works
To do behavioral segmentation properly, you need a reliable source of data flowing in from the places where customers engages with your product: your website, platform, mobile app, and other marketing tools. Once this information is available, you can group data by different behaviors to find patterns and trends that warrant further analysis.
For example, let’s say your product team is trying to create a roadmap for a workflow automation platform. You want to prioritize growth, both in usership and revenue, and retain existing customers.
To do behavioral segmentation, you might start out by analyzing behavior patterns to find your most valuable user segments. Comparing the lifetime value of different groups based on how many workflows they create, whether they use a particular integration, how often they message and tag teammates, and countless other possible activities on the platform, you find that your most high-value customers are the ones that interact with teammates daily and integrate data to their CRM.
Once you know this, you can tailor the in-product experience to this user status. Your product team can prioritize the messaging feature, provide tooltips on how to get integrations working properly, spend time building similar integrations, and so on — actions that encourage the behaviors associated with high LTV.
Meanwhile, the marketing team can start focusing their marketing strategy on prospects that behave similarly to your high-value customers. They can improve your website content around integrations, publish white papers and articles about collaboration, and develop email marketing flows that teach new users how to get the most of messaging on the platform.
Of course, behavioral segmentation can look different between B2B and consumer companies. Below are a few examples of each:
Behavioral Segmentation Examples for Consumer Brands
B2C and D2C brands use different types of behavioral segmentation to improve their shopping experiences and add personalization that drives revenue. They look at customer behavior to influence the purchasing process, learning how to make their product easier to navigate, offer better recommendations, increase cart size, and build an intuitive user flow that matches the needs of their ideal, most loyal customers.
Fine-Tuning the User Experience
When you track user behavior in your product, you can create a more engaging customer journey that ultimately drives revenue.
Figs is a direct-to-consumer healthcare apparel and lifestyle brand that celebrates, empowers and serves current and future generations of healthcare professionals. Their goal is to help people feel and perform at their best 365 days a year. To further support their busy customers, they integrated Heap + Shopify so they could ensure their online shopping experience is as seamless as possible. By unlocking a wealth of customer behavior they’re able to optimize A/B testing and conversions based on their customers actual needs.
Behavioral segmentation allows the team at Figs to understand how different acquisition channels, product pages, and on-site features impact purchase behavior. Their integration between Heap + Shopify allows them to take the behavioral segmentation one step further by connecting order data from Shopify to onsite behavioral data in Heap. From there they can understand how different shopper behaviors, demographics, and marketing channels correlate with conversion, average order value, and repeat purchase rate.
These insights help the FIGS Data Team design A/B tests to measure the success of increased personalization in new shopping interfaces. While personalization is only one potential use for behavioral segmentation, it can be a powerful application of this type of analysis.
Optimizing Conversion Funnels
Behavioral segmentation can help you optimize the path to conversion for each user. As a potential customer uses your product, the messaging and offerings they see on each step of the conversion funnel can all be tailored based on their previous behaviors, or the behaviors of users with similar traits. Often a valuable output of behavioral segmentation is a better sense of what questions the team should be asking.
Sur La Table, one of the largest cookware retailers in the world, uses behavioral segmentation to optimize their conversion rate. Their segmentation strategy is based on the category or SKUs people view most often, whether they complete checkout flows, whether they book culinary classes or not, or any number of other behaviors that might lead to faster conversions or higher purchase values.
The team can dig into complex behavioral questions and find a clear delineation between converting or not converting — for example, they used behavioral segmentation to learn that the more products someone views, the more likely they are to convert (and the higher the revenue they are to produce). This led to a follow-up question: how should their email newsletter be optimized to drive more product page views, and consequently more revenue? Retargeting Shoppers on Other Sites
If a user adds an item to their cart and later abandons it, brands can use this behavioral data to display an offer for that product while they’re browsing a different website days later. They may also set up an automated campaign to email that user to get them to complete the transaction. This kind of retargeting drives revenue over time by recapturing what would have been a lost sale.
The Heap + Shopify integration provides immediate insight into online shopper behavior, making it easy to launch retargeting campaigns based on previous product interest and activity. Get the ebook on how the Heap + Shopify integration works here.
Increasing Customer Loyalty
Another application for behavioral segmentation: segmenting customers based on behavior to improve your customer loyalty program. Retailers can create special offers for their most active and loyal users, give them early access to sales, and build a rewards program with levels based on how much they purchase or interact with the brand.
To do this, you must identify your heavy users. For example, after analyzing purchase behavior, you might find that those who make three or more purchases a month remain customers for longer and spend more. By deep diving into what drives this group, you’ll uncover data that influences your roadmap. You can then test new features with your most dedicated customers first, prioritize releases based on which features they use the most, and add in-product messaging that encourages a target audience to behave more like the loyalest ones.
Testing and Optimizing New Features
Behavioral segmentation can be used to test new features among an appropriate subset of users before rolling them out to the rest. Redfin, a popular real estate platform, segments their user base in Heap to understand how new features will perform. “We like to see how new agents do things, compared to Redfin veterans,” says Nick Smith, Senior Product Manager. “For example, if something is successful for 10 people in Atlanta, we’ll do a full beta in Atlanta, and if things look good we’ll extend it to everyone across the US and in Canada.”
Once a feature or new product is launched to your entire customer user base, you can start tracking adoption among different groups. Regular users, occasional users, light users — comparing each segment’s behavior and level of product adoption can uncover interesting variations among them that help you understand what to build next.
Behavioral Segmentation Examples for SaaS Products
Companies selling software platforms and other B2B tools use behavioral data to make improvements to their product that will have the highest impact on acquisition, adoption, retention, and revenue.
Defining the highest-value customer
Product teams want to cater to customers who are most likely to come back often and spend more, whether that means repeatedly buying the same consumer product or remaining subscribed to a paid SaaS platform. To find out who these customers are and what drives them, you can compare purchasing metrics among different groups:
Purchasing trends among different groups. Here you can see that over time, people who did not view the blog had a higher average total price in checkout.
Once you group users by purchasing behavior, you can test different in-app messaging and offers to increase the value of the lowest-performing segments. If one segment continues to produce less revenue over time, it might make sense to decrease their allocation of sales and marketing spend.
Improving Engagement After a Signup or Trial Period
Many software companies struggle to get new users to fully take advantage of their product. A user testing out a project management tool, for example, might create a task or two for themselves but fail to bring the rest of their team on board. In this case, they’re likely to stop using it once the trial period is over.
To start, the product team might dig into behavioral data to find out which activity correlates with retention. They find that inviting a team member has a very high correlation with using the tool for over six months, whereas users who don’t invite team members churn more quickly.
Before the trial period is over, they can send automated emails to remind the user to add a team member. They can also analyze the segment of their user base that never added a team member to see what members of the group have in common. This may lead to changes in the roadmap that better accommodate the lower retention group, or might result in a reallocation of marketing spend to higher retention groups altogether.
Perfecting Workflows and Launching New Features
SaaS products are often built around common workflows. These workflows inform which features to create and how to structure the user experience. For example, an applicant tracking and recruiting platform that automates the hiring process might see a pattern in which some of their biggest customers often manually invite their team to review and rate candidates. The product team can act on this by automating the review process and sending alerts when a candidate has gone unrated for too long after the last interview.
Behavioral segmentation can also help you group people by whether they take the actions you expect or want them to. At Freshworks, the product team used Heap to discover that a feature they had just shipped, Table View, had a relatively low adoption rate. As they dug deeper they realized that many customers hadn’t even clicked on the feature, which made them wonder if users just weren’t interested. However, upon further analysis they realized it was a discoverability issue — the icon was hard for users to see. Armed with this insight, the team shipped changes that made the option easier to find with the help of a simple text dropdown, doubling the number of people discovering Table View.
Why Behavioral Segmentation Is So Important
Behavioral segmentation gives you insights about usage and guides decision-making like no other kind of analysis can. By identifying your most loyal and valuable customers, implementing more personalized experiences, and rolling out features users will love, you can improve your top growth and retention metrics. Here are a few examples of the benefits of behavioral segmentation:
Boost revenue by offering the right product or user experience to each unique user based on their behavioral patterns.
Avoid loss due to cart abandonment or confusion in the purchase process.
Improve customer retention by proactively fulfilling their needs and creating programs that reward them for staying.
Lower the cost of feature development by efficiently testing and focusing new functionality on your most valuable user base.
Win more business by predict how prospects will behave based on similarities to your current customer base.
According to a study by the customer data infrastructure company Segment, 44% of surveyed participants said they were more likely to become a repeat customer after a personalized shopping experience. Additionally, 39% said they’re likely to tell family or friends, 32% are likely to leave a good review, and 22% are likely to leave a positive social media comment. When customers have these experiences, the impact can be multi-dimensional.
How to Do It in Heap
One of the biggest blockers to successfully using behavioral segmentation is having to manually collect behavioral data. Many companies only invest in a basic product analytics tool that requires them to select each potential event to track (like clicks, purchases, or uploads) and ask the engineering team to write the tracking code.
This approach doesn’t provide the flexibility needed to segment behavior in different ways as you learn more about your user base. Because you won’t know right away which behaviors will correlate with metrics like engagement or retention, you should have a platform that captures all possible activity, connecting behavioral data from any applications users interact with. We built Heap’s autocapture feature to address this, making it easy to set up behavior tracking and allowing you to decide later which actions to segment on.
Once you’re set up with Heap, you’ll see what users are doing on the platform in real time. As you collect more and more of this data, you can start comparing different groups based on hundreds of actions and use your findings to improve the user experience for each.
Just install a single tracking code to begin visualizing activity in your product. Your Heap dashboard will start to populate with event data you can later use to create segments.
Autocapture vs Manual Tracking
Heap’s Segments feature allows you to create segments based on any combination of user-level properties and actions they’ve taken on your site. To do this, visit Define > Segments in your Heap account and click the ‘+ New Segment’ button. Then, enter the name of your new segment and select the appropriate category and filters.
To learn more about Heap’s Segments, watch our Heap University Segments video.
For example, you can create a “Big Spenders” segment for users who have made 10 or more purchases by selecting:
Behavioral property: Count of
Key event: Purchase
Value: greater than 10
A cohort made up of people who have purchased more than 10 items at once.
Or a more complex segment that captures multiple behaviors at once:
A complex user segment. This captures users who have received a Marketo email in the past week, have opened a graph in the past week, have run a query to completion, are in the US, and are paying customers. Once making this segment, you can compare it to other user segments to find key differences.
Never Stop Learning from Behavioral Data
Heap makes it easy to build out behavioral segments. Once you’ve done this, you’ll want to take an iterative approach, using new data from the platform to make decisions on where to change course. Because Heap automatically scales as you grow, you can further segment your customer base and analyze data based on new variables without ever slowing down the evolution of your business.
Interested in a demo of Heap’s Product Analytics platform? We’d love to chat with you!