A/B testing is simple in theory: what works better, version one or two? But in reality, A/B testing can be a complex, messy experiment — and if not done properly, you can find some false-positive results that can lead to poor marketing choices. This is one reason why A/B testing is sometimes avoided. On the flip side, A/B testing is one of best-budget friendly tactics toward improving open rates, on-page time, conversions, and more.
If you could increase your sales funnel size by 20 percent, you’d do it, right? Of course. With A/B testing you can find that simply changing the CTA button from blue to red improves response rates, or even more in-depth alterations like content substance/structure can cause significant variation in conversion ratios.
So in the fight against misinformation, we’ve laid out three important tips to keep in mind when A/B testing. The best business decisions are data-driven, but we need to first ensure that the data is accurate.
Tip 1: Numbers Can Lie. Analyze the Right Way
Two terms to be aware of in A/B testing:
Statistical Significance: Evidence that the result we see in the sample also exists in the population. Or another way to look at it is, the likelihood that a relationship between two or more variables is caused by something other than chance. [Basically, we want to know that the data is reliable and not an outlier]
Statistical Confidence: Represents the frequency of possible confidence intervals that contain the true value of the unknown parameter. To put this in terms that actually make sense, this is a way to gauge how confident we are that these results can be repeated over and over again.
Since A/B testing is going to yield results regardless if done properly or not, it is vital to know how to analyze data for validity. In order to do this and evaluate the statistical significance and confidence, you need to look at your test’s sample size, role of chance, and duration.
Sample sizes must be large enough to deduct reliable conclusions. A sample size between 1-100 is considered very low and is susceptible to misleading results.
The role of chance is more subjective, but it requires critical thinking to question if the results indicate true trends by consumers. Results can be skewed by an endless list of factors like consumer mood, season of the year, time of the day, etc. Just do your best in questioning your results and verifying that it isn’t an oddball.
Allowing testing to continue in duration helps to reduce results from being false-positives. Early in an A/B test it can seem that a clear version works better than the other. However, tests are prone to spikes in trends, especially in the beginning. Therefore, giving A/B tests ample time to level out and produce a large enough sample size will fight off the desire to make early conclusions.
Tip 2: Test a Variety of Aspects, But Be Careful
A/B testing traditionally tests one change between the two versions. However, testing a variety of variables between A/B is possible, and highly effective for research — when done properly. By playing with elements like CTA buttons, form submissions, artwork, and more, a multivariate A/B test can save the need for several rounds of A/B testing. However, it is important to know your limit with multivariate testing. Pitfalls exist. Two common issues include, sample size and not being able to identify which variable is effective.
When using multivariate testing, it is easy to test several templates, but this can cause inconclusive results as sample sizes get spread out among the numerous variations. Unless you are able to capture a large sample size, stick to just two multivariate versions. Additionally, this type of A/B testing can cause confusion as to which aspects of a test are useful, and which are harmful to conversions.
Tip 3: Begin With A/A Testing
Any good experiment has a control. Why don’t we do this more with our marketing? A/B testing is highly variable, so in an effort to minimize error, performing an A/A test is a great starting place. First, it ensures that your software is performing properly and the test is set up correctly. Secondly, an A/A test gives you a baseline for conversions. After all, we only want to make changes to pages, emails, etc. when we know the new variant will improve our current results.
Want More A/B Testing Tips?
At Venta Marketing, we are helping businesses meet their goals head-on. As leaders in the digital marketing industry, we know the ins-and-outs of A/B strategy and technique. Contact us today if you are curious about our services!