Optimizing for Smaller Audiences

Hello folks, I've had a crazy last 6 weeks but have been capturing some stories I marked to blog when I finally carved the time to catch up and get back to my usual rhythm.  Here's the first of several stories.

I've often thought about the practice of A/B testing, or split testing (the practice of providing part of your audience with one design and the rest with another to compare and contrast).  It is especially difficult when your audience is small.  As a statistically driven person I think in terms of scientific sample size when determining the accuracy of results.

Thank about it.  Is your A/B testing useful when you have 5-10 visitors regularly?  100?  10,000?  When your numbers are in the tens of millions or more then ... definitely, but what about the smaller fish in the sea?

Here you go:

https://getuplift.co/how-to-optimize-a-low-traffic-site-without-ab-testing-step-by-step/

Here is a snippet from the piece:

Testing is not a matter of opening your testing tool and waiting for that little “Statistically Significant” marker. You need to reach your required sample size before concluding anything or else the insights will be invalid.

That’s where it gets difficult for low traffic sites.

Unless you’re detecting an incredibly large effect, you’ll need to run the test for months. The longer a test is running, the more vulnerable it is to sample pollution, which can rear its head in many ways.

Happy Reading,

J.W. Gant