What is A/B testing?
A-B testing is an experiment that compares two versions of a webpage/app against one another. It can also be referred to as split-run testing or bucket testing. A/B testing is done to determine which of the two versions is performing better.
What eCommerce stores usually test
Every site has a goal, and that goal is to move visitors through the sales funnel to do something. This can be a
- like/share of a page
A/b testing in Fera.ai focuses on the purchase part of the customer journey and tests
- Revenue per shopper
- Conversion rate
How A/B testing works in Fera.ai
Statistical analysis is performed after the A/B testing is done to mathematically determine which of the variations performs better for revenue per shopper and conversion rate.
Why A/B testing is useful for your store
By measuring the performances of A versus B, you can measure the rate at which it converts your visitors to the goal you’re trying to reach.
Comparing variations lets allows you to
- see what works better for your customers
- collect data about the impact of your change
- analyze whether a change will be beneficial or detrimental to your site/app.
Overall, this method of introducing changes to a user experience also allows the experience to be optimized for the desired outcome.
The importance of running an A-B test
A/B Testing allows you to utilize your traffic. It lets you collect data from your visitors and harness it to create a better user experience. You’ll be able to meet your goals and drive better conversions from your traffic.
The cost of trying to acquire paid traffic is huge, but to increase the cost of conversions is minimal. The return on investment (ROI) of A/B testing can be huge because even minor little changes like the color of a button can result in significant increases in leads and conversions.
Running an A/B test allows your team to make careful changes to the experiences their users have and collect data on those.
Those results can allow for the construction of hypotheses on what impacts the user experience. You’re able to better understand why certain elements of the experience impact your users’ behavior (s). You can prove yourself right or wrong and see what changes are helping you out
By testing one change at a time you’re able to pinpoint exactly which one had an effect on the behavior of your visitors. Over time, these changes can combine to improve the experience overall and improve your conversions.