Analytics is typically the first stop when trying to understand how your business is performing. For most, strategies and design changes are usually centred around what your analytics data is telling you. The problem with this is your data just shows you something has affected your performance, but not what.
The typical development cycle starts with an idea being generated. It then continues through the cycle one step at a time from development through to analyzing performance. Once completed, the cycle starts again with a new ideas being generated and so on.
During each new deployment, your analytics data tracks your performance, and you usually end up with a graph similar to the one below. When trying to determine how each version affects your performance you typically mark the start of each new deployment. From the data shown below, it looks like each new version released has made a positive impact on your performance but the problem with analytics is only the effect is shown without a direct relationship to the cause.
There are two major downfalls to this development approach:
The first is that there are endless external factors affecting your analytics data. How can you tell if V1 is better than 2 or 3, if when V1 was active, there was more media attention driving users to your product? What if you are not even aware of the external factor causing changes to your KPIs in the first place? Maybe instead of media buzz more people received app store gift cards for their birthday while V2 was in the wild driving more people to search for a product like yours.
The second downfall is that you have to wait until the next release to make a change. In the mobile world, things are changing quickly. So while you're waiting to release an update to your app, the world is changing around you. The data you saw a few weeks ago when you were building the new version of your app is different from what's happening today. This means with pure analytics you are always a few steps behind.
Go beyond analytics
The only way to truly take these external factors out of the equation is through A/B Testing. A/B Testing allows you to test multiple features side by side. You will be able to see directly how each new feature or small variation of your app affects your performance and make data-driven decisions on how to proceed. Since you are running each variation side-by-side, external factors are no longer a concern, because you have controlled for these external factors.
By setting up an experiment to test new features, you will be given your results similar to the graph above. You now have a very clear picture on how each version affects your performance. From the original analytics graph, V1 looked very promising but your A/B testing data is showing you that the change in your analytics data was caused by an external factor. A/B Testing provides a very clear picture, and you can see that V2 is a clear cut winner.
Supercharge your development cycle
A/B testing takes your development to the next level as you can set up experiments for every feature you deploy and truly understand how each change affects your app and your users. Making data-driven decision is the best path to success, and you can only confidently make these decisions through A/B Testing. No idea should be left untested. You never know how your users will react to the biggest or the smallest changes.
Don’t be left in the dust!
All of the best technology companies understand the power of A/B Testing. They may not call their strategies A/B testing or be vocal about it when they do. But they are all doing data-driven development that focuses 100% on whether each new feature or change to their product drives more users, more engagement or more revenue. If you don't believe this and think that companies like Apple are sitting in a room coming up with amazing ideas in a vacuum, without data, you will be disappointed when markets pass you by.