The world population continues to grow, and so does the number of house pets. While we all hope most of them have a good quality of life, some don't have a home. To combat this, we can make an animal care app. In this blog post, the app's objective is to increase the pet adoption rate. We will change the color of our call-to-action button and measure the click-through rate of each button version using A/B testing.
Let's say your team has developed a new feature update and is planning to release it to the public. There can be some uncertainty and risk because it is hard to predict how users will react to the change. Will the new update have a negative impact and drive users away from the app? The best way to know for sure is to adopt an A/B testing approach by releasing it to a subset of users to measure its impact prior to making a full deployment. This gives you enough room to uncover bugs and refine the feature without disrupting the experience for everyone.
Knowing up front whether a new feature will improve or worsen user experience can be a challenging decision. If we don't adopt the proper mechanisms and processes to test new features we stand a high risk of introducing new bugs. By incorporating A/B testing into our feature release workflow, we can minimize these situations by testing the new feature with a small sample of users before deploying it.
Most software features are designed and tested in-house before making their way into the hands of users. While this is somewhat of a standard approach in feature deployments, it may cause user experience issues. This is because the majority of end users are not tech-savvy and aren't thinking the way engineers do.
Adopting an A/B testing approach prior to making a final feature deployment decision can prevent or lessen the risk of interfering and affecting user experience. Features can be tested and released to a subset of end users initially where the current metrics can be recorded and compared to a previous benchmark. As a result, developers may choose to improve or cancel the feature altogether without the risk of blindly affecting the entire user base.
Scenario: You’ve thought up a small change for your app. You write and test the code, and everything looks good. As you’re about to push it into production, you stop and ask yourself, “Will the users like this?”
You start having doubts, that maybe the idea isn’t as good as you previously thought. Still, you continue to have a strong feeling that it’ll make your app better.
One solution to this dilemma is to gradually introduce the change to a portion of users and track its impact on them. This is called A/B testing, and it’s a simple, low-risk way of letting your users pick which variant yields better results.
To keep an app or website functioning, a business may choose to roll out new features or make updates. If these updates or features are somehow shipped with bugs and are not well received by users, this may cause a decline in user engagement and can lead to revenue losses.
This is a step-by-step guide on implementing an A/B testing scenario using ConfigCat and visualizing the results in a funnel with Amplitude. There is a working sample application on GitHub if you want to follow along.
Separating your customers into distinct segments will help your product in all sorts of ways. It can help you track the usage of your app in a more meaningful and granular way. It can also reveal how specifically different segments behave differently, which will help you prioritize future feature development as well as focus your marketing efforts.
