Start of Main Content

If you’re an association marketer, member engagement is a key measure of your success. You can track member engagement through conversions on your website like email newsletter sign-ups, call-to-action click-throughs, downloads, and article shares. A/B testing is a great way to look at these activities and figure out how to optimize them to maximize member engagement.

What is A/B Testing?

A/B testing involves testing changes to your website to see which versions resonate more with your audiences. It compares two variations of content shown to similar audiences at the same time to tell you which one is more effective. By understanding what content works better with your members, you can fine-tune your content to increase engagement on your website.

A/B testing data

What is Multivariate Testing?

Multivariate testing is like A/B testing, except you test multiple variables at once. Each variation of an element you want to test must be included in the test to find the combination that generates the best results with your members.

» How to Analyze Audience Behavior on Association Websites

What’s an Appropriate Test?

You can come up with a lot of different ideas for A/B tests, but your goal should always be to optimize member conversions.

Your considerations should include:

  • Does the test have the potential to lift conversions? (click-throughs, email sign-ups, shares, etc.)
  • What type of resources will be needed to implement the test? Development? Creative?
  • What are the elements that you want to test and how many recipes/variations will this require?

These are all important questions to ask when deciding what tests to run. If a particular test will take a lot of resources but the impact is low, then it may not be worthwhile. Prioritize your tests based on the ones that will have the highest impact on your website based on your digital goals.

How Do You Know a Test Will Improve Website Engagement?

Statistical significance is the likelihood that the improvement in your conversion rates in a test isn’t due to random chance. It allows you to be certain that the results of the test will lead to improvements in conversion rates. You want to aim for at least a 95% significance level for a variation to win. This will give you confidence that the changes in your association’s website based on A/B and multivariate testing will improve member engagement.

How Long Does an Average A/B or Multivariate Test Run?

Tests need to run long enough to reach statistically significant results. The number of variations a test has and the level of traffic to the page tested also affect how long it needs to run. If the page has low traffic (less than 1,000 visits per week), it will take a long time to reach statistical significance. Also, the more variations a test has, the longer it needs to run.

When considering tests for your association’s website, it’s critical to take a strategic approach and map out a clear plan. Use your data in your current analytics tools to identify areas of opportunity on your website and create a roadmap of tests you would like to run, ordered by highest impact to lowest impact. Keep in mind seasonality changes on your website and try to pick times to test when your traffic isn’t affected by major campaigns running on your website. Lastly, continuously conduct tests on your website to ensure you are consistently meeting your members’ needs and generating the highest engagement possible from them.

Learn more about our work with associations or contact us to start A/B testing on your website.

Published:

Take advantage of our expertise with your next project.