CXL - All Things Data-Driven. Conversion Optimization Blog

Beyond "One Size Fits All" A/B Tests

If you’re invested in improving your A/B testing game, you’ve probably read dozens of articles and discussions on how to plan and run A/B tests.

In reading advice about how long to run a test or what statistical significance threshold to use, you probably saw claims like “Always aim for XX% significance” or “Don’t stop a test until it reaches YYY conversions” – where XX% is usually a number higher than 95%, and YYY is usually a number higher than 100.

You might also have heard it’s best to come up with many variants to test against the control to improve your chance of finding the best option.

No matter what rule is offered, such advice seems to rest on the assumption that there is a one-size-fits-all solution that works in most situations.

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How to Kickstart Your Growth Process

At TelTech, it took our product-marketing organization more than a year to get to something that resembled a true growth team, running high tempo testing.

So, if you are struggling to implement the growth hacking methodology, I get it. We assembled a team, achieved product-market fit, and identified our growth levers, but got stuck when we tried to put process behind our testing.

If you’re at a similar stage in your development, you’ll probably get stuck there too. Eventually, we found some practical methods to help us succeed.

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How to Get Better Results with Online Learning

Learning stuff online is the new standard for learning. It’s not just “how to tie a tie” how-to videos, it’s also learning hard skills. In fact, according to a recent report 59% of employed data scientists learned skills on their own or via a MOOC.

But how can we take better advantage of online learning? What’s the most efficient way to learn as much info as quickly as you can? Keep reading »

How to Segment A/B Test Results to Find Gold

You run an A/B test, and it’s a winner. Or maybe it’s flat (no difference in performance between variations). Does it mean that the treatments that you tested didn’t resonate with anyone? Probably not.

If you target all visitors with the A/B test, it merely reports overall results – and ignores what happens in a portion of your traffic, in segments.

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