When conducting A/B testing on app store assets, how do you accurately account for seasonal fluctuations or external events that might impact results?

To accurately account for seasonal fluctuations or external events that might impact results when A/B testing app store assets, you should implement these key strategies: first, run tests for a sufficiently long duration, ideally spanning at least two seasonal cycles or periods affected by relevant external events. This helps to average out the impact of these fluctuations. Second, implement a control group or a holdout group that is not exposed to any of the test variations. This provides a baseline against which to compare the performance of the test variations, helping to isolate the impact of the asset changes from the impact of seasonal or external factors. Third, meticulously document any external events or seasonal changes that occur during the testing period. This allows you to analyze the data with these factors in mind and adjust your interpretation of the results accordingly. Fourth, use statistical methods, such as time series analysis or regression analysis, to model the impact of seasonal or external factors on app performance and remove their influence from the test results. This provides a more accurate estimate of the true impact of the asset changes. Also, use a Bayesian A/B testing approach, which allows for incorporating prior knowledge about seasonal effects and external events into the analysis, leading to more robust conclusions. Finally, segment your data to isolate the impact of the asset changes on different user segments. This can help you identify if the asset changes have a different impact on users who are more or less affected by seasonal or external factors.