Traffic Allocation and Audience Segmentation

What You’ll Learn

You’ll learn how to intelligently allocate traffic between your control and variations, and how to target specific audience segments to run faster, more relevant experiments. Smart traffic allocation ensures you don’t waste traffic on losing variations while proper segmentation lets A/B Test Starters test multiple hypotheses simultaneously without interference.

Key Concepts

Traffic allocation determines what percentage of visitors see each variation—a 50/50 split is common, but you can run 70/30 or 90/10 tests when validating low-risk changes or when you’re confident a variation is superior. Audience segmentation lets you run different experiments for desktop vs. mobile users, new vs. returning visitors, or users from different geographic regions. For A/B Test Starters, segmentation is powerful because it prevents diluting results—testing a mobile checkout improvement only on desktop users wastes data when you could isolate mobile traffic and reach significance faster.

• Traffic Split Allocation Strategy: Use 50/50 splits for most experiments to maximize statistical power, but use 80/20 or 90/10 when rolling out changes you’re highly confident about or when protecting user experience is paramount. Document your allocation rationale so team members understand why you chose a particular split rather than defaulting to 50/50.
• Audience Targeting Rules: Configure targeting based on device type, user geography, traffic source (organic, paid, direct), browser type, or custom attributes from your CRM like user tier or customer lifetime value. A/B Test Starters should start with simple rules (mobile vs. desktop) before advancing to complex multi-rule logic that requires engineering support to maintain.
• Cohort Consistency and User Bucketing: Ensure the same user always sees the same variation across visits using persistent user IDs or cookies that identify users consistently. If bucketing fails, a user might see variation A on day one and variation B on day three, corrupting your data since you can’t attribute outcomes to a stable experience.
• Traffic Holdout and Control Groups: Consider excluding a small percentage of users from the test entirely—a true control group that sees your default experience—so you can measure baseline conversion rates independent of test variations. This practice prevents accidentally making your control the weaker option when analyzing results.

Practical Application

Design a traffic allocation plan for your first experiment specifying the percentage split between control and variation, and list all audience segments that will run this test. Set up targeting rules in your A/B testing platform’s audience builder and verify that the correct users are being bucketed by checking your platform’s traffic preview or traffic calculator before launching.