The mark of a successful business is its commitment to constant improvement. A/B testing can impact that significantly. Change is inevitable, especially in today’s fast-paced environment. If your brand is out of step with the latest technology or design trends, it’s at risk of being passed over by consumers.
But of course, change is never easy, especially when it comes with a big price tag and runs the risk of lost sales if things go wrong.
In the past, marketers relied on time-consuming market research and surveys to see which advertising strategies got the best response from test and control groups. Now, thanks to digital technology, they often resort to A/B testing to compare two or more variants against one another and see which works best.
However, one of the most limiting aspects of traditional A/B testing is the fact that only a small number of variants can be tested against each other at any given time. So, if your company is running a large campaign or building a new sales model, it could take a long time and a lot of sunk costs before you optimize things to perfection.
Further, any data you collect during the process needs to be cleaned and verified quickly and fed back into the campaign or model.
Thankfully, modern automation technology can make A/B testing better in numerous ways by tackling the weakest points of a manual approach. Here’s how:
1. Finds Correlations Between Variables
Let’s say that your marketing team is working on a rebrand for your business, starting with the website design. They want to change the entire look; everything from the layout to the color scheme to the font needs to be re-hauled.
Obviously, many factors come into play here and they all need to be thoroughly tested in order to improve the UX and functionality, while also creating an impressive appearance.
In order to test all of these changes with traditional A/B testing, it could take months, given that few variants can be tested simultaneously.
For instance, suppose you find out during an initial A/B test that a black CTA button on a white background works best. In another A/B test, maybe users preferred a background with an image rather than a plain color.
But, once these two elements are put together in the final design, the CTA may no longer stand out, reducing the number of clicks and rendering much of your research practically useless.
Even if you’re doing manual multivariate testing, it is impossible to say how one factor influences another that is not in the same category.
Automated A/B testing on the other hand employs machine learning to connect the dots between the correlations of these variants. Uber uses a combination of multivariate and A/B/n testing to measure correlations and causations in the UX of their app.
According to Uber Engineering, they run over 1,000 variants at any given time and attempt to deduce how changes affect user behavior. This intelligent platform is able to compare multiple test results and optimize the final design and structure accordingly.
2. Reduces Costs and Eliminates…
