A/B tests, as they are called, are quite common in the pharma industry. Large clinical trials are fundamentally split tests, where one group receives a novelty treatment for a condition while the other group receives the standard prevalent treatment (or a placebo). Results are used to compare if the novel treatment is significantly better at treating the medical condition.
But the popularity of these tests soared when product and marketing managers started using them to understand questions like, “How do I ensure people click on my ad campaign? Or how do I increase the number of people that open my promotion email?”
A/B tests integral to software product development
From launching a new product, tweaking the existing product, identifying a new target customer segment, or solving existing customers’ needs better – product managers are always looking for data-driven tests to complement their decisions. In an A/B Testing environment, the performance of in-app messages, understanding product feature adoption, or designing a killer user experience – all such decisions are increasingly becoming more scientific than gut-based. Top content creators on YouTube today are not just creating content; they are also optimizing video titles, thumbnails, and descriptions based on multiple A/B test results and ensuring that their content never underperforms.
How A/B testing shapes data-driven software product decisions
Let’s look at a hypothetical scenario – The product managers of a popular homestay booking app are trying to figure out why their customers drop out on the payments page. During focused group discussions, they find that people have certain inhibitions to pay prior to their actual stay. As one of the controlled tests, they came up with two variations of the payment method – “Pay Now” – the control version, and “Reserve Now, Pay Later” – the test version.
