A/B testing is an invaluable tool for landing page optimization when implemented correctly. An A/B test involves testing two versions of a web page — an A version (the control) and a B version (the variation) — with live traffic and measuring the effect each version has on your conversion rate. To minimize wasting time, money and effort on changes that will yield little to no benefit – or even make things worse – take the following best practices into consideration while conducting A/B testing on your website.
Start simply, advance a little later
Although some people are really good at A/B testing, there is always some skill involved with testing some of your website’s more complex features. Instead, start your A/B testing with something simple, like moving your registration form to the left of the page instead of the right. Then, as you start to understand the A/B testing process, you can start conducting more sophisticated experiments.
Small steps can lead to significant results
Mostly people think that big, sweeping changes need to occur in an A/B test. The fact is, the minute details of the page are just equally important. Understand that something simple can still drive big improvements.
Test 1 Variable on a Page, But Don’t Limit Yourself to one Variable
In order to see if a feature on a page is working effectively or not, you have to isolate it in your A/B test. Test one item at a time, but remember that your web pages are also made up of a number of other features. You don’t have to limit yourself just to testing color background or text size. Think also about your images, videos, language, bullet points, and headlines etc.
Keep testing on the go
Your first A/B test may have been a huge success, helping you discover a new way to make your web page more effective. However, there’s always room for more optimization on your website. Try conducting an A/B test on another feature of that same page. For example, you can test for headlines, body copy, color schemes, images, adding features, etc. Then move onto another page of your site and do some testing there. There’s quite a good chance you can still increase leads elsewhere, too. Do not leave testing if you are done with one website, keep testing.
Don’t let go even if the test shows no positive output
You may decide to conduct an A/B test on the headline for a web page but see no statistically significant result to persuade you to run with one page over the other. Don’t think your A/B test failed. Be innovative, use the failed data to help you figure out a new iteration on your new test. For example, consider testing new headlines, and see if that makes a difference. If not, the headline may have no bearing, but there may be another feature of your page you can adjust to increase leads. For example, try switching up your call-to-action button, and see if that makes a significant difference. Having an innovative bent of mind will never make you face failure in testing.
Rules of A/B testing
- Hypothesis – Every test starts with a hypothesis that you’re trying to prove or refute. The hypothesis is a short sentence that summarizes what you’re trying to prove and should include the tested variable and the success metric that determines the winner.
- One Variable – By its nature, in A/B testing we’re testing only one variable. This means that everything else must stay constant.
- Clear and Aligned Success Metric – Define one success metric that will determine the winner based on the effect you are trying to get. The success metric and the variable should be as aligned as possible.
- Volume and Statistical Significance – For a successful test you have to have enough volume to make the data statistically significant. The volume needed isn’t just in the test groups, but also in the results and the difference between them.
- Test Group and Splits – Volume should be applied not just to the overall size of the test but also to the test group. You can decide to do an even split between the control and test groups (50/50) or apply an uneven split of up to 95/5.
- Randomization – Since you’re only testing one variable, you want to eliminate the variables in the audience selection process. Your control and test groups should be picked randomly. A random sample is a sample chosen that allows all subjects an equal probability of being selected.
- Always be Testing but Apply Common Sense – While everything is testable, not everything should be tested. Always use common sense. You should also test variable you believe will increase your performance and not ones that will have a marginal effect.
- Documentation – This is one of the most neglected elements in testing in which software can help. If you’re diligent about testing, you should be fanatic about documenting your tests and results.
Also Read: 5 Things Impacting A/B Testing Result
Referral: hubspot
