Running experiments without segmentation is like navigating with a compass but no map. You know the general direction, but you miss the critical details of the terrain.

As teams scale their growth efforts, they inevitably reach a point where broad experiments stop delivering results. That is when they start looking for platforms that offer advanced targeting and segmentation for experiments.

If you are evaluating who offers built-in segmentation in AB testing software, you need to understand why it matters, what the market offers, and what truly counts as "advanced" targeting.

Why segmenting experiments is important

Averages hide insights. When you look at the overall results of an AB test, a variation might show a flat or negative conversion rate. However, if you dig into the data, that same variation might have increased conversions significantly for a specific cohort.

Consider a real scenario from one of our customers. They offer a fixed 5% discount for instant payments and display these discounted prices across their website. The friction occurs at checkout: when users switch the payment method to a credit card, the total price increases, leading to frustration and cart abandonment.

To solve this, they ran an A/B test to determine the best pre-selected payment method at checkout:

• Option 1: Credit card (single payment)
• Option 2: Credit card (installments)
• Option 3: Instant payment

Looking at the overall results, option 3 won easily. The 5% discount is a compelling incentive for the average buyer. However, segmenting the data by order value revealed a different reality.

For users with high-value orders, option 2 was the clear winner. Buyers rarely keep large amounts of liquid cash available for instant transfers: they prefer installments for expensive purchases.

If you do not segment your experiments, you discard winning experiences simply because they did not appeal to the entire user base. Segmentation allows you to find these hidden winners, understand how different cohorts behave, and transition from generic AB testing to highly targeted personalization.

What types of platforms allow you to segment AB tests

The market approaches segmentation from a few distinct angles, each with its own limitations.

Client-side plugins

These tools run on top of your website via a JavaScript snippet. Because the targeting logic executes in the user's browser, they have access only to the immediate browser context, such as the user agent, IP address, and marketing campaign UTMs.

This severely limits how far you can go with behavioral segmentation.

Developer-centric feature flags

These platforms handle server-side targeting, allowing you to segment tests based on the data already available in your project and database.

However, this means your targeting is only as good as the data you manually pipe into the tool, and it usually requires engineering work to set up and manage.

Customer Data Platforms (CDPs)

When connected with your experimentation tools, CDPs let you build complex segments based on user history and past behavior across multiple channels.

The downside is that this data processing usually happens in batches, meaning you can target based on what a user did yesterday, but not on what they are doing in real time during their current session.

Basic vs. advanced segmentation

Not all targeting capabilities are created equal. Many platforms claim to offer advanced targeting, but only provide basic filters.

Examples of basic segmentation

Basic segmentation relies on static or easily accessible session data. Most standard AB testing tools handle this out of the box.

• Device and OS: Targeting mobile versus desktop users, or iOS versus Android.
• Traffic source: Segmenting users arriving from specific UTM parameters or referral domains.
• Visitor status: Distinguishing between new and returning visitors.
• Simple geolocation: Targeting users based on their country or state.

Examples of advanced segmentation

Advanced segmentation requires a real-time understanding of user behavior and intent. It connects historical data with live session context.

• Behavioral triggers: Targeting users who abandoned a cart worth over $100 in the last three days.
• Engagement depth: Segmenting users who have scrolled past 75% of a specific product page but have not clicked the CTA.
• Lifecycle stage: Delivering a specific test variation only to users who matched the LeadGenerated event but have not logged into the application in the last two weeks.

Why Croct is the best option

To achieve advanced segmentation, you need data. Most AB testing tools require you to integrate an external CDP to handle complex cohorts, adding latency and technical overhead.

Croct solves this by uniting the data layer, the decision engine, and the content delivery.

We built a real-time behavioral engine natively into our platform. Using our segmentation feature, your team can define complex, granular audiences, like "users who viewed pricing twice today", without writing a single line of backend logic.

Because Croct evaluates these segments server-side, you can run highly targeted experiments based on both historical and real-time session data without compromising website performance, causing visual flickers, or relying on developers for every new test.

If you want to move beyond average metrics and start optimizing for the individual, create your free account and explore Croct today.