In the last decade, the cost of storing data on disk has fallen by something like a factor of 10. As a result, the traditional approach of storing pre-defined fields in a database is being supplanted by a “record everything” model.
However, the ability to collect data on such a large scale is a double-edged sword.
A/B tests are an excellent way to learn what works and doesn’t work for your website. But after running a test and measuring an effect, a common question is, “why did that happen?”. Maybe a test that sounded like a great idea actually created a downlift and you want to know what went wrong.
At this point many people reach for “post-test segmentation”, taking the test data and splitting it into different user segments, to see if the test had an unexpected effect on some group of users.