Your First A/B Tests: A Step by Step Guide

Your A/B testing system is hot off the press, you've integrated with OptimizelyGoogle's Content Experiments, or maybe deployed some code of your own. You open your eyes, look around, and see a whole new world of experimental opportunities.

But suddenly, you start asking more questions and forming more hypotheses than one could possibly answer in a lifetime. Should you start testing 41 shades of background hues to see what's most attention grabbing? Maybe start by testing a rewrite of every piece of copy on your site? Or maybe you should start A/B/C/D testing your web fonts?

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A Mixpanel Data Exporter

Mixpanel is a great analytics tool for small to medium sized web and mobile shops. And not surprisingly, their analytics product has pretty good adoption (over 1,400 companies using it, according to their homepage).

One things I've noticed, however, is that as some of these shops grow in size, they slowly start to ask more than Mixpanel can answer. Their data science team may want to do some in-depth analysis over customer lifetime value. Their product team wants to do some deeper funnel analysis comparing variants in a recent A/B test. Or their search team wants to do some click-depth inference on long-tailed queries.

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Eight Ways You've Misconfigured Your A/B Test

You've read about the virtues of A/B testing feature releases. You love iterating quickly, testing quickly, and continually learning in a data-driven fashion. You appreciate the importance of keeping an eye on the statistics behind your testing, and perhaps you even use a tool or two to make sure your results are statistically valid.

But, you ran a test last week, the results have been coming in for some time now, but, the data just doesn't look quite right.

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