If you ever ran a highly trustworthy and positive a/b test, chances are that you’ll remember it with an inclination to try it again in the future – rightfully so. Testing is hard work with many experiments failing or ending up insignificant. It’s optimal to try and exploit any existing knowledge for more successes and fewer failures. In our own practice we started doing just that. Keep reading »
If you’re invested in improving your A/B testing game, you’ve probably read dozens of articles and discussions on how to do a/b testing.
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.
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.
As conversion optimization continues to mature and become adopted by more organizations, it’s always interesting to get an a/b testing tutorial from people in your network to see how they’re approaching growth and optimization. Especially, for me, in the tech startup space, as these companies often live and die by data, and tend to build their organizations around experimentation.
LawnStarter is one such company, so we sat down with their CTO, Jonas Weigert, to learn how they experiment across their product and communication and how they deal with optimization as a company.
Nothing works all the time on all sites. That’s why we test in the first place; to let the data tell us what is actually working.
That said, we have done quite a bit of user experience on ecommerce sites and have seen some trends in terms of what generates positive experiences from a customer perspective.
This post will outline 16 A/B test ideas based on that data.