In one project, the goal was to predict customer churn using historical interaction data. The model performed exceptionally well in testing over 90% accuracy. But once deployed, its performance dropped drastically.

The issue turned out to be hidden in the data itself. Many “active” users in the training data hadn’t actually engaged meaningfully; they were just generating background activity. The model had learned to associate these false signals with retention.

After reworking the feature definitions and cleaning the labels, the accuracy stabilized  not as high as before, but far more reliable in production.

It was a good reminder that real-world data rarely behaves like curated research datasets. The focus shouldn’t just be on performance metrics, but on how well the model understands reality.