Avoid These 26 Costly A/B Testing Mistakes and Maximize Your Revenue

Did you know that A/B testing mistakes can lead to significant revenue loss? Take Booking.com, for example, which experienced a 2% annual revenue loss due to unsuccessful experiments. But what if you have a less experienced team? The potential revenue loss could be even greater.

When it comes to costly experimentation errors, it’s not just the obvious bugs like a malfunctioning “Buy” button that can cause harm. The most dangerous mistake is deploying a feature based on false positive experiment data, which resulted in a staggering 42% annual revenue drop in one unfortunate case.

At Conversionrate.store, we have conducted over 7200 A/B tests for 231 clients, including Microsoft. Surprisingly, 72% of our first 100 experiments had mistakes that we only discovered 8 months later.

To help you avoid these detrimental errors, here are four common problems that can significantly impact your revenue and hinder growth:

• Implementing false-positive results
• Neglecting critical changes without A/B testing
• Experiencing direct revenue loss from underperforming variations
• Failing to maximize the volume and velocity of experiments

These issues are interconnected, and we often come across them repeatedly. Let’s take a closer look at the 26 typical A/B testing mistakes that can undermine your results:

1. Hypothesis is not focused on the main bottleneck – Failing to identify and address the primary issue affecting conversions.
2. Guessing reasons behind the main bottleneck – Relying on assumptions rather than data-driven insights to determine the cause of performance issues.
3. Guessing how to fix the cause of the drop-off – Implementing changes without a clear understanding of how they will impact user behavior.
4. Holding the wrong metric like conversion-to-purchase as a goal – Setting goals that don’t align with the desired user actions and outcomes.
5. Data tracking not at least 90-97% accurate – Inaccurate or incomplete data collection, compromising the reliability of experiment results.
6. No event mapping for all elements on A and B – Neglecting to track and compare specific user interactions on both variations.
7. Testing more than one hypothesis per experiment – Introducing multiple variables that make it difficult to determine the true impact of each change.
8. Stopping the experiment only based on statistical significance – Prematurely concluding an experiment without considering practical significance and real-world impact.
9. No MDE and pre-test sample size planning – Insufficient consideration of Minimum Detectable Effect and necessary sample sizes for reliable results.
10. No QA of alternative versions after the experiment is launched and no monitoring of experiment session recordings – Neglecting to ensure the proper functioning of alternative versions during the live experiment and lack of monitoring.
11. No regression QA of the control version during an experiment – Failing to test the control version for potential issues or changes during the experiment.
12. No QA of experiment data tracking – Overlooking the verification of accurate tracking and measurement of experiment data.
13. Not eliminating the “novelty effect” – Allowing temporary user behavior changes due to the novelty of new features to skew experiment results.
14. Implementation of false positive results – Incorrectly implementing changes based on statistically significant but misleading data.
15. No anomaly detection – Neglecting to identify and address anomalies or outliers that may affect experiment outcomes.
16. Outliers not cleaned up – Failing to remove statistical outliers that can distort the interpretation of results.
17. No preliminary A/A or A/A/B tests – Skipping the essential step of conducting baseline tests to validate the stability of the testing platform.

18. No analytics or tracking of the long-term impact of implemented winning versions – Lacking follow-up analysis to evaluate the sustained impact of successful experiments.
19. No in-depth post-test research and documentation of results – Failing to thoroughly analyze and document the outcomes, insights, and learnings from experiments./li>
20. Targeting irrelevant traffic segments together in one experiment – Grouping unrelated user segments together, making it challenging to derive meaningful insights./li>
21. Not checking for sample-ratio mismatch (SRM) for 100% of experiment traffic or all meaningful segments you want to compare – Overlooking discrepancies in the traffic distribution among variations, leading to biased results./li>
22. Experiment data set not visualized – Not utilizing visualizations to gain a clearer understanding of the data and results./li>
23. Deploying winning versions to a different audience than in the experiment – Implementing successful variations on a different audience or traffic source, potentially leading to inconsistent results./li>
24. Low experimentation velocity due to lack of in-house resources or absence of 100% dedicated experimentation teams – Limited resources or inadequate focus on experimentation, hindering the frequency and scale of tests./li>
25. Not leveraging parallel experiments when there is enough traffic – Missing opportunities to run simultaneous experiments to maximize learning and efficiency./li>
26. Not speeding up experiment time with CUPED or similar techniques that leverage historical data on the sensitivity of metrics – Failing to optimize experiment duration

If any of these alarm bells are ringing, it’s crucial to address them to ensure your experimentation process is optimized for success. We offer a free A/B testing consultation where we can assess your current process, identify bottlenecks, and provide guidance on how to maximize volume, velocity, and uplift.

Don’t let these mistakes hinder your growth potential. Schedule a free consultation with us today and unlock the full power of A/B testing for your business.

 

Schedule a free A/B testing consultation where we can go through your experimentation process, discover its bottlenecks and consult on ways to maximize it’s volume, velocity and uplift.