Adam Davison

Position: Lead Data Scientist As Lead Data Scientist, Adam spends his time working with the Data Science team to analyse the huge amounts of data Qubit collects on behalf of clients. Previously a Physicist, he switched from high energy particles to studying human beings when he joined the company in 2013. You can contact him at Key facts Started at Qubit: July 2013 Loves: Chicken and a good lasagne Hates: Weird snacks

More from Adam Davison

Painting by numbers: how to piece together a more comprehensive picture of your customer using data

Thought Leadership

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.

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Why segmenting your A/B test results could be less fact and more fiction... and what you can do about it.


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.

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