How To Use Behavioral Segmentation in Streaming Media A/B Tests

By Mike Fradkin

January 15, 2025

Share

Streaming media platforms are typically designed to provide engaging content, capture attention, and attract and retain users—and they’re currently managing those priorities while facing intense competition from the rest of the industry. With countless streaming options available, standing out requires fully optimized user experiences.

For those already running an experimentation program, behavioral segmentation is a powerful mechanism that allows your organization to dig deeper into the results of every A/B test. How are different groups of users really interacting with your platform? While a variation might work well for some groups, it might not be ideal for others. Behavioral segmentation is the tool that gives you greater visibility into the finer details of the how and why behind your results.

A/B tests already help streaming media organizations offer better features for viewers. By mixing up and experimenting with new features and user options, you can see a noticeable impact on subscribers, renewal rates, or revenue. In fact, allowing household members to create personalized profiles has increased paid subscriptions by as much as 13%.

Why? It adapts recommendations to individuals instead of merging everyone in a household into a single entity. Though the result applies broadly and across demographics, it highlights the transformative potential of impactful user insights. With behavioral segmentation offering even more clarity on user preferences, your organization can drive higher engagement, retention, and long-term growth.

Let’s understand what behavioral segmentation is, explore its importance for streaming platforms, and reveal how examining behavior-related data in A/B testing can elevate user experiences and fuel growth.

Why Behavioral Segmentation Matters for Streaming Platforms

At its core, behavioral segmentation involves grouping users based on how they interact with your platform rather than on traditional demographic markers such as age or location.

In the context of streaming media, behavioral data provides richer and more usable insights into streaming media because it focuses on user habits and preferences while providing key clues about their motivations.

Why does this matter? Two viewers might be the same age and live in the same city, but their streaming behaviors could differ significantly. One might binge-watch late at night, while the other prefers occasional daytime viewing. Demographic data alone can’t capture these nuances or reveal overarching patterns in behavior among your specific set of users.

With behavioral segmentation, you can:

  • Identify drop-off points and opportunities for retention
  • Activate promotional strategies by targeting users with relevant upsell offers or premium upgrades
  • Deliver personalized content suggestions that engage users longer

Types of Behavioral Segmentation in Streaming Media

Wondering what behavioral segmentation actually looks like in streaming? We break it down into four key categories:

Usage Behavior

This category tracks how users engage with your platform over time. Are they occasional viewers or daily users? Do they consistently finish episodes or drop off partway through? These insights can inform decisions around content placement, interface design, or features like autoplay previews.

For example: If a subset of users tends to abandon content midway through, it might indicate the need for clearer episode summaries or content descriptions.

Content Preferences

Another user group could be based on specific content types. Some might love watching action-packed blockbusters while others typically watch soothing nature documentaries. Segmenting these users based on their preferences allows your organization to deliver more personalized experiences. Tracking preferred genres, directors, and viewing formats can help you build better algorithms and align content suggestions with individual tastes.

Time-Based Segmentation

When do your users watch? Behavioral data will be able to show your team the patterns associated with specific times or occasions. For example: Are weekends prime binge-watching periods? Do some users watch late at night while others stick to weekday evenings? This information can inform decisions about release schedules, promotional timing, and content libraries.

Loyalty-Based Segmentation

This approach focuses on behaviors that signal user loyalty. Some of your users might log in frequently, renew consistently, or engage with platform-exclusive content. Adding rewards or exclusive offers for loyal users can strengthen their connection to your platform, but you need data on their behavior to reach them.

Regardless of how you choose to further segment your users, each type of segmentation provides opportunities to design more specific A/B tests. Interested in optimizing your platform for subgroups? Trying to reach all of your users more effectively? Behavioral segmentation insights can help you achieve those goals.

How Behavioral Segmentation Advances A/B Testing

Behavioral segmentation allows for more precise, targeted A/B testing. Instead of treating all users as a single group, you can see the outcome of each test on specific segments.

For example: Testing a new homepage layout could yield different results for binge-watchers than for casual viewers. Similarly, a promotional banner might drive engagement among users who prefer action movies but could fail to capture the attention of documentary enthusiasts.

By exploring results across behavior segments, you can:

  • Refine your hypotheses to address specific user needs
  • Analyze results more effectively by accounting for differences across segments
  • Optimize changes based on what resonates most with distinct user groups

This approach improves your test results’ accuracy while ensuring your organization implements changes your audience wants and needs.

Examples of Behavioral Segmentation in A/B Tests

Example 1: Personalized Content Suggestions

An organization might run a test to compare two recommendation algorithms for users who frequently binge-watch. One algorithm serves up more trending shows, while the other highlights content similar to past viewing history. Segmenting users by behavior can help them determine which approach better satisfies binge-watchers.

Example 2: Cross-Device Interface Adjustments

Suppose you want to introduce a new navigation menu. Testing the feature on users who primarily stream on mobile devices could reveal usability issues specific to smaller screens. Conversely, desktop users might have a completely different experience, providing valuable feedback for further adjustments.

Example 3: Promotional Strategies

A platform offers a discounted premium subscription upgrade for a limited time. By targeting users who frequently stream on weekends, the promotion achieves higher conversions than a general campaign. In this scenario, behavioral segmentation allowed for more strategic targeting and helped the organization put promotional messages in front of the best possible audience to maximize ROI.

Tips for Mastering A/B Tests with Behavioral Segmentation

To make the most of behavioral segmentation, keep these best practices in mind:

1. Incorporate Dynamic Segmentation Tools

Advanced segmentation platforms can create highly granular audience groupings, such as “frequent viewers aged 25–34 using mobile devices to watch thrillers and comedies during weekday evenings, located in urban areas, and subscribed to the Pro plan.”

Take advantage of tools that can create these dynamic groupings. Some use AI to uncover patterns and adjust or recommend segments based on real-time data. These insights allow you to test meaningful micro-segments you may not have previously considered.

2. Monitor Long-Term Trends

User behaviors evolve. Regularly reviewing segmentation criteria ensures that your tests remain relevant and actionable.

3. Use the Right Tools

A/B testing solutions like SiteSpect simplify the process of testing against many granular segments without backend changes and with the ability to integrate into any other segmentation tools you use. For teams looking to reduce test implementation time and bring in accurate, scalable results, those features can significantly improve the capabilities of their experimentation program.

Final Thoughts

Behavioral segmentation is a strategy that can help you learn more from every experiment. By incorporating user segmentation into your A/B testing campaigns, you can cater more directly to your users, optimize your promotions, and strengthen user engagement.

Ready to see how SiteSpect can help you adopt more behavioral segmentation for better A/B test results? Request a demo today.

Share

Mike Fradkin

Mike Fradkin

Mike Fradkin is the Director of Product Marketing at SiteSpect. His experience ranges from smaller series-A startup companies to large multinational corporations such as AT&T and IBM. With a technology career that began with several customer-facing leadership roles, Mike never loses sight of the connection between technology value and the real people it can positively affect. He enjoys the challenge of identifying trends and market drivers, truly understanding the problems of customers within their specific industries, cultures, and reporting structures, and leveraging those insights to deliver more impactful results.

Suggested Posts

Subscribe to our blog:

[hubspot type=form portal=7289690 id=55721503-7d2c-4341-9c5f-cd34a928a0dd]