A/B testing is an experiment of comparing two or more versions of a customer journey against each other to understand which version performs better against a specific goal. You can conduct an A/B test by splitting the traffic between different versions of customer journeys and using statistical analysis, determine which version performs better for a given conversion goal.

Why do you need to run an A/B test?

A/B testing allows you to make careful changes to your customer journeys while collecting valuable data on the test results. You could construct hypotheses and learn better how certain elements of your experiences impact user behavior. You could even be proven wrong - an A/B test could help you understand if your opinion about the best experience for a given goal holds true. AB testing can consistently improve a given journey, improving on a single goal like conversion rate over time.

Let's assume you are an eCommerce manager at a fashion store who wants to identify the right recommendation module that aids conversions. In order to do that, you can try A/B testing two different types of recommendation modules and see which module works the best, by exposing two different user groups to journeys with these two modules with everything else kept constant. This will help gauge the impact of the modules and compare the performance of one against the other. 

The potential benefits of A/B testing are:

  • Improved conversion rates
  • Better User Experience
  • Optimized User Journey

There is no single journey that can fit all customers. Testing multiple customer journeys allows you to gain key insights into customer behaviour and identify the best-suited journey for the audience. The optimum journey varies based on a range of factors such as user demographics, products available, time spent on the website, and so on. The idea is to continuously experiment with different combinations of experiences with highly customized content across different audiences to maximise your business goal. supports a frequentist approach to A/B testing where the user is completely in control of executing/monitoring and concluding A/B tests.


Before you begin A/B test