The sample size required for the test to yield conclusive results depends on the set confidence level for the A/B test. Reaching the required sample size can be a big challenge if the website receives a lower traffic. This, in turn, will increase the duration of the test to collect the sample sizes for the test.
Moreover, business owners and marketers are not statisticians, and they want results too quickly. So there is the possibility that the test may be concluded early without achieving a sufficient sample size. There is a need to learn about sample sizes and how larger sample sizes lead to more precise results.
Your hypothesis will depend upon the accuracy of the collected data. Since there is so much ground to cover, it can be challenging to assimilate all the information into structured data. One way to overcome this is by first dissecting the analytical data to identify the possible target pages. Then, move to the feedback and customer journey data to investigate these pages’ elements to find the areas for improvement.
You must find out where exactly the problem lies within your website/app and then develop a hypothesis. It is a proposed statement formed on the basis of limited data that can be further used for testing the right things, or else you will end up trying random things and achieve nothing.
One of the most critical challenges of A/B testing is narrowing down the correct elements for running the test. How would you identify the correct one to produce the most desired effect and prevent yourself from testing the non-influential ones?
That is where the analytical and behavioral data points will come into play. Organize the data about various elements on your landing page, such as conversion rate, traffic volume, bounce rate, total conversion value, etc.
Then, try to find a correlation between the elements and your test goals, as explained in the previous sections. It will give you a quantitative approach to look at the data points and select the right element for the test.
When a test fails, don't quit. Keep on trying and make it better. By doing this, you will land on the right page with the right setting and get the desired results. It may take several follow-up tests to spot actual changes in conversions, but eventually, you will be able to boost traffic and attract more visitors.
Data should trump gut feeling.
Sometimes emotions or unintentional biases can creep up while preparing the parameters for A/B testing. There are instances where you may not like the winner of the A/B testing because you were rooting for the control, or someone in your team may want to make a change based on their intuition.
The challenge is to keep these feelings at bay and understand that you want to make the change that produces the most impact, whatever your feelings may be towards it.
One of the ways to remain unbiased is to follow the data points. If your hypothesis is based on quantifiable data, you can make better decisions.
Another way is to improve team collaboration. Run brainstorming sessions to provide and take constructive criticism and keep the data interpretations free of inherent biases.
Another grave challenge is to find the balance between making those changes that affect the quality of experience and those which affect your goals.
Not everything on your website makes the needle jump towards conversions but produces a delightful effect that attracts customers and promotes retention. These changes may not reflect in your A/B test, whose duration is a few weeks but are essential to produce long-term effects.
For example, adding an overlay lead popup may increase lead generation during the A/B test but have an overall negative effect in the long run, like higher bounce rates.
That’s why it is important to keep your customer experience in mind while deciding on the hypothesis for A/B testing.
One way to sidestep this hurdle is to perform A/B testing regularly. It will help measure the changes in the trends and customer behavior. You can compare the past and current test results to see whether the changes are performing as expected or not.
You can also add additional measures to evaluate the customer experience post implementing the A/B test results.
For example, add a survey to the page to collect feedback about the changes and gauge customer experience.
The flicker effect in A/B testing is when the original page appears before the user for an instant before seeing the variation.
It usually occurs due to page loading speed issues, improper script installation on the webpage, or how the A/B testing tool interacts with your page.
The flicker effect can lead to a poor user experience and influence the test results.
You can minimize this by using the synchronous script version and optimizing the page speed. Most of the A/B testing tools like Optimizely provide provisions to eliminate flicker effects and keep your A/B testing parameters identical throughout the test.
The novelty effect is the tendency to be attracted to something because it is new. In A/B testing, novelty effects can skew your test results and give a false positive towards a variation.
Let me explain. Let’s say you are testing a color variation for a CTA or a newly added feature on the page. It is possible people might interact with the variation more because it is new. This behavior will show as a positive result in the test.
In the long run, the change may prove less effective than the original design, but since the duration of your test is small, it may lead to incorrect conclusions.
There are a few ways to reduce novelty effects in A/B testing.
The Beginner’s Guide to Conversion Rate Optimization (CRO) is an in-depth tutorial designed to help you convert more passive website visitors into active users that engage with your content or purchase your products.
With a 30% or higher response rate, every product owner should be asking their customers these questions.
Whether you are developing a new product or have been selling the same one for years, you need user feedback.