We’re past the good ol’ days of marketing where marketers relied on a ‘gut feeling’ to figure what works for their audience. Data-driven decisions have changed marketing overtime for the good and marketing automation has become its new ‘torchbearer’. One way to witness it in its purest form is through A/B and multivariate testing.
Uplift by MoEngage, which we introduced a few weeks back is a step in this direction. With Uplift, now marketers can test-out their campaigns by way of A/B and Multivariate testing. Marketers can test their campaigns, its various elements, measure the revenue impact and optimize for it. Marketers can compare results of A/B and multivariate testing with a ‘control group‘ to decide the best way forward.
As seen above, Variation B provides an Uplift of up to 60% in engagement compared to control group and Variation A.
Why MoEngage Uplift?
Uplift gives you insights and allows you to answer questions about your app or website or even your users. By understanding and implementing A/B testing and Multivariate testing, you can stand to gain unprecedented gains to your marketing campaigns. So, are you ready for this major boost in your marketing?
Uplift for A/B testing
A/B testing simply put, is a technique to determine the right message for your audience while comparing and choosing two different messages (Variates) while comparing the results with results from the control group. Check out the graphic below showcasing A/B testing for a push notification. Here 50% of the audience receive a push notification with a ‘smiley’ in it vs 50% of the audience who receive a plain text push notification. It’s a simple case of going with ‘Variation A’ or ‘Variation B’ depending on the statistics. Check out the animation below depicting the power of A/B testing for adding ’emoji’ in push messages.
In the above, ‘Messaging A’ wins the Uplift test. Now compare this figure with results from the control group to determine the best way forward.
What is Multivariate testing
Multivariate is similar to A/B testing except here you’re comparing and choosing changes to more than one element in the messaging again in comparison to a control group to performance. Multivariate testing provides more insight in terms of how different elements interact with or complement each other. Check out the graphic below showcasing Multivariate testing for push notification. Here a marketer can experiment with more ‘elements’ than just adding smileys. You can further experiment with changes to CTAs, delivery times, segments and more. Check out the animation below depicting the power of Multivariate testing for adding ‘personalization’ and ‘image’ in push messages.
Here, ‘Variation B’ wins the Uplift test. Now compare this figure with results from the control group to determine the best way forward.
How do I decide between A/B testing and Multivariate testing?
Well, rather than choosing between both, you need to look at them as two different approaches for optimizing your messaging.
Here are a few differences we’ve chalked out:
Remember, there no right or wrong while choosing between A/B and or Multivariate testing. Depending on your case, both A/B and multivariate testing can prove to beneficial when it comes to optimizing your campaigns. To learn more about use cases where A/B or Multivariate testing can benefit your business in particular, drop-in your email below:
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Tags: a/b testing, a/b testing multivariate testing, marketing experimentation, multivariate testing