A/B testing, also known as split testing, compares two different variants of any piece of content testing each of them with an audience of similar size to find which of them is more effective. The technique helps marketers optimize their assets for converted customers and increased leads.
However, there are some myths associated with this kind of testing which prevent many marketers from taking accurate and data-driven decisions.
Here are these common split testing myths busted for you.
- Marketers’ instincts are more effective than split testing.
The best marketers can be wrong, too. Years of experience can form a solid understanding about what generally converts your visitors into leads, but instincts mustn’t be the only trusted thing in a matter where statistics is easily drawn upon.
- A/B testing should be used prior to every single decision.
Split testing is able to aid you in many marketing decisions, but this doesn’t imply that every decision has to be tested. There are changes simply not worth testing.
- Multivariate testing is better than split testing.
While both are great ways of using data to make marketing decisions, these two testing types are used for different purposes. While A/B testing tries the effectiveness of one element in different treatments, multivariate testing aims to analyze the effectiveness of many element combinations across many treatments. Thus, these types shouldn’t even be compared.
- A treatment which works for that marketer, will also work for me.
Despite multiple split testing case studies proving certain successful layouts, designs, or copies in terms of conversion rates optimization, following others’ success without testing is a bad practice. Mostly this is because the original site was different in traffic, audiences, marketing funnels, products, promotions or anything else.
- A/B testing requires technical competence and a large budget.
In general, your technological knowledge level needed for performing split tests depends on your available resources. If your budget is near zero, you can use free A/B testing tools such as Google Analytics’ Content Experiments which are a bit tech-savvy apply. On the other hand, the all-in-one software for marketers offered by HubSpot is more expensive but less technologically challenging.
- Split testing fits only sites with tons of traffic.
A/B testing involves studying only two treatments that means you don’t need that many visitors to get results. The sufficient number will be at the points where you can reach statistical significance, which is at least 95% of confidence in your results.
- A/B testing affects your SEO negatively.
Keep calm: Google encourages testing different site copies and even provides guidelines on the content testing ways to gain more conversions and visitors on your site, without penalizing for the duplicate content.
- It’s unnecessary to run the test up to the end if one treatment immediately stands out.
The both decisions about waiting for the visitors’ number that’s statistically significant and determining the time for running your A/B test should rely on your confidence interval.
- Winning treatments always look pretty.
Winning treatments are better than their alternatives, but not necessarily beautiful. Guide your marketing with more A/B tests rather than subjective opinion.
- You’re measuring only one conversion rate.
Your A/B testing results mustn’t stop at a single metric – instead, examine how the treatment moves a set of metrics. Otherwise, you may miss some larger and more important conversion insights.
- You’re done when your A/B test is over.
Even if you’ve already found some dramatic results, use split testing continually to your content for leads and conversions.
Experiment with A/B testing to make successful decisions and promote your business!
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