The most common errors of A/B testing

A/B testing is a life saver for organizations. The testing process comprises critical aspects failing to those can lead to a loss of time and reputation.

Tech companies perform A/B testing to check the user's reaction to the webpage versions. If it goes wrong, the whole project may go for a toss and it will be a wastage of time, energy and money. Here's what you need to make sure you know before you agree to an A/B testing.

A/B testing: what are the common mistakes

Perform A/B testing on the wrong page

In most cases, performing A/B testing on the wrong business's webpages costs a lot of time, money and resources. Webpages which are critical to conversions are the ones to hit first because these are the key to generating more leads and revenue.

Depending on the type of business, the webpage testing priority should change. Commonly, for any website, the visited pages are: 

  • Home page
  • About page
  • Contact page

Whether for e-commerce businesses, it is the product pages. And therefore, it is essential to perform the usability test for these pages. Altogether, A/B testing or usability testing should be performed on the webpages, which are the most important revenue generation.


Performing multiple A/B testing at once

A/B testing needs a sample to perform the test. In case of a large variation testing, the sample size becomes huge, and it may result in errors while testing. It is recommended not to run more than four usability tests at once, or you won’t know what variation is actually making a difference.

Using a faulty testing tool

The pervasiveness of A/B testing has flooded the market with numerous low-cost software. Some of them have fantastic features, but other critical functionalities must be checked before the website's split and UX testing. Some testing software can significantly reduce the webpage's loading speed, and responsiveness hampers SEO and conversion.

Forget to test a hypothesis

An A/B testing hypothesis is a theory to establish the root cause of the results and how to fix those results. Before performing the test, a detailed outline of the validity or disapproval can save a lot of ambiguities. Set some Key Performance indicators (KPIs) based on the hypothesis and measure the test results accordingly. 

Performing tests with low traffic

Assessing an optimum sample size is essential to get an accurate testing result. A high-traffic test can be performed quicker, whereas it takes longer if the traffic is low. 

How to Conduct Efficient A/B Testing?

To ensure that the A/B testing is effective, the following steps are vital to follow.

Select one variable to test

Multiple variables can create confusion in determining the primary impactor. Therefore, select a single variable and perform the test to get a better result. For example, to personalize the emails, test the email subject lines, then sender names etc.

Split sample audience equally and randomly

An equally and randomly divided sample group can provide more realistic and conclusive results. Though it is not applicable for every test, this strategy is highly effective in cases where there is more control over the audience, like email.

Perform concurrent tests of the versions

As time is a significant factor, A/B testing of the two versions must be done simultaneously. Testing performed on different time schedules may cause second-guessing of the results.

Provide sufficient test time to generate valuable results

Depending on the company and the execution process, the testing run time may take hours, days, or months. It depends on the website traffic to generate statistically significant results.

Ask for user feedback

An exit survey or a poll from the actual users during the UX testing helps to generate quantitative data. This type of information provides an insight into the user's behavior.

Measure the results

There are several parameters for the performance of the testing. For example, Bounce rate, engagement, open and exit rate, and conversion rate are some of the critical parameters to gauge.

Take actions accordingly

Mark whether the test result is significant or inconclusive. In case of a total inconclusive result, it's better to stick to the original version and perform another test. The failed data can help to figure out a new iteration for the new test.

Key Takeaway

A/B testing is a savior for tech organizations. The testing process comprises critical aspects failing to those can lead to a loss of time and reputation, which is not at all desirable. Therefore, considering a reliable company like UNGUESS to perform high-quality A/B testing, is surely a smart step.

download the case study

Similar posts