In one project, we noticed that a product feature looked extremely successful based on overall engagement metrics. At first glance, the numbers were solid, but something felt off when we broke the data down further. Usage was heavily concentrated in a specific age group and region that had recently been targeted by a marketing campaign. Because this group was overrepresented in the dataset, the feature appeared universally successful, when in reality it wasn’t resonating with a large portion of the broader user base.

We detected the bias by slicing the data across demographics, time periods, and acquisition channels, then comparing trends instead of relying on aggregates. Once we validated that the skew was driving most of the uplift, we adjusted our analysis by normalizing samples and reporting segmented insights rather than a single headline metric. This changed the business conversation completely from this feature works for everyone to this feature works well for a specific audience, and here’s where it needs improvement.