The mark of a successful business is its commitment to constant improvement. 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 Monotony
Many marketers dread A/B testing because it takes so long to collect a statistically significant sample. Furthermore, data analysis is incredibly tedious and boring. Sometimes the numbers can be incredibly close, and all of your calculations need to be double or even triple-checked for accuracy.
Testing is also not cheap if your project is large; some businesses need to hire additional help from developers and data scientists to drudge through the data and get the desired insights.
Automation takes all of these factors out of the game by digitizing the entire process and creating real-time reports as the data rolls in. Automated systems make the information understandable to clients, management, or even the most inexperienced marketers, by creating charts and graphs that visualize the data.
3. Minimizes Losses During the Testing Process
Traditional A/B tests could often have a negative impact on the UX in some cases, particularly if you are testing new website designs and features. If you’ve ever been selected to test out the beta version of a website or app, you may have experienced this. Sometimes the loading speeds will decrease or the app may crash unexpectedly.
This can obviously interfere with the results of the tests, since the user will clearly have a negative experience no matter which testing variant they were exposed to.
Automation helps eliminate this issue with machine learning technology – changes that break the UX can quickly be reversed and the page can be restored to its previous version in real-time.
4. Maps Tactics to Conversions More Accurately
The goal of any A/B testing is ultimately to boost conversions. Now, this can be done in many ways, such as nurturing more leads, creating a better CX, or improving brand sentiment through more empathetic interactions.
But overall, every marketer wants to see whether or not, and to what extent, these changes boost the bottom line.
When A/B testing is conducted properly, it can help raise conversion rates quickly. With machine learning, automated A/B testing can connect the dots between different variables and determine how they influence conversion rates as well as attribute outcomes to their most likely causes.
For example, if you’re bringing in more leads, you can know for sure whether you owe it to changing CTA buttons on the landing page or using improved subject lines for your emails.
With automation in testing, you can also use predictive analytics in forming hypotheses, such as testing email subject lines to determine which segment will respond best to the content in question.
The key here is to keep tweaking your automation workflow to increase the number and impact of conversions. And remember that various metrics will have a “trickle-down” effect – such as a significant boost in CTRs, email open rates, or an extended amount of time spent on a specific webpage.
Choose the Right Tools
Clearly automated A/B testing has a lot of significant benefits to offer, but make sure you choose a platform with features that not only speed up the process but also improve it while maintaining the accuracy of data. Look for tools with the following capabilities:
- Simultaneous multivariate testing
- Customizable settings for test and control groups
- Sample size adjustment
- Real-time reporting
What facets of your branding and marketing do you A/B test? What insights have you discovered? Where have you stumbled? Have you given automated A/B testing a try? Please share your results in the comments!
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