A/B Testing is an experimental marketing program where the website shows two web pages to different website visitors simultaneously to see which one holds more impact. We have found 3 common Pitfalls of A/B Testing and how to avoid them.
Since the past few years, A/B testing has become a standard practice within digital marketing. It is one of the easiest and convenient ways of improving conversion rates, but everything has a drawback too. Not all businesses can get the correct results. This is due to the fact that small mistakes during A/B testing can have a drastic effect on your test results.
Common Email A/B Testing Pitfalls
A/B testing is one of the best ways for conversion rate optimization testing, for marketers. If not used correctly, it can harm your conversion rates. Some of the common pitfalls that arise during A/B testing are as follows:
- Don’t stop when you get the first significant result:
While performing A/B testing, when you stop testing as soon as you get a significant result, you immediately start getting invalid results. Therefore, if you want to get good results from your emails, you need to stop practicing this habit.
The major cause behind these invalid results is the presence of false positives, which are very likely to appear if you keep on checking off and on. Consider it as a direct relation. The more likely you are to find a result that has been measured incorrectly, the more you check and stop your testing. Now, how should one avoid this?
According to an article published on LinkedIn, the practice of A/B testing should be a continuous and persistent effort. It’s unwise to halt testing merely because a positive result was obtained in a single test. Email marketing is a dynamic field, with people’s behaviors, needs, interests and motivations constantly evolving.
Tip to avoid this pitfall – Keep a minimum sample size in mind and fight the urge to check:
First of all, don’t stop testing as soon as you get a significant result, this could potentially cause problems for you. Also, keep a low sample size. Some of the tools that you can use are, VYO and Optimizely, in order to stick to a specific size and keep it at a considerable amount. However, it is recommended by GetResponse to use a sample size of 25%.
- Try segmenting your test based on new or old customers:
You might think that changing the slightest of thing in the design of your email might get you great results but that’s probably not the case. Each type of user has a specific requirement. Some might not like sudden changes, while others would appreciate them.
According to a HubSpot Blog article, it’s important to recognize that new customers often have distinct needs and expectations compared to long-standing customers. For instance, new customers may tend to prioritize price sensitivity, whereas established customers may place a higher emphasis on features and benefits. When conducting tests that involve all your customers as a single group, it becomes challenging to discern whether the alterations you’ve implemented are positively or negatively affecting each of these customer segments.
Tip to avoid this pitfall – Look for radical changes:
In order to focus on improving this factor, learn to segment your test based on the users. You may keep the same layout for usual users but change it for the new ones.
- Stop considering conversions only:
Most people neglect long-term business results while performing A/B testing. You might think that getting more converted users will prove beneficial for you, but that is not the case. Sometimes, these converted users are of low-quality and harm your business. These types of metrics will distract you from authentic revenue-driving results.
Based on a report published on Towards Data Science, it is recommended to avoid participating in conversations during A/B testing since doing so can introduce bias into the findings. Engaging in discussions with users may result in responses that are influenced by their perception of your preferences or your own biases, which could potentially undermine the precision of A/B test results.
Therefore, you should focus on the leads produced on the page rather than the conversions.
Tip to avoid this pitfall – Outline a hypothesis:
To get rid of all other distractions, you need to set up a hypothesis on KPI based on your email marketing. This will help you drive authentic business results.
