Episode 28
Matt and Iqbal dive into the technical details and importance of test targeting conditions in A/B testing with guest Jonas Alves. The episode highlights the complexities of segmenting users for experiments to ensure accurate and reliable results. They focus on effective test targeting conditions for A/B experiments, emphasizing the importance of precisely defining which users should be included to maintain data integrity. Experiments often capture users who aren’t exposed to the tested changes, introducing unnecessary noise and reducing test sensitivity. Proper targeting helps address this by only counting users who interact with the specific elements being tested, such as scrolling to a certain section or clicking a particular button. Tools often automate some targeting, but nuanced scenarios—like page scrolls or feature visibility—require custom configurations.
Jonas provides some advanced targeting strategies he used during his time at Booking.com, including multi-stage triggers that track user exposure at multiple points throughout the experiment. This method allows for separating the impact of different changes, like server-side queries versus frontend features, providing more granular insights. A sophisticated approach to triggering, which may involve balancing noise reduction and implementation complexity, is essential for faster, more accurate experimentation. Although most off-the-shelf tools don’t support this level of detail, understanding when and how to use triggers effectively can significantly improve experiment outcomes.
Key Takeaways:
- Targeting reduces noise: By including only users who truly interact with tested changes, you minimize irrelevant data and shorten test durations.
- Avoid sample ratio mismatch (SRM): Proper targeting reduces the risk of uneven group sizes, which can distort test results.
- Custom triggers are often necessary: Built-in tools might not handle complex interactions like scroll depth or pop-ups, requiring manual configurations.
- Multi-layer triggering offers deeper insights: Breaking down changes into multiple stages isolates different impacts, improving reporting and decision-making.
- Complexity versus simplicity: Balance noise reduction with the risk of errors when implementing complex triggers. Sometimes, a simpler approach is preferable if the noise is minimal.