A/B testing, or split testing, is a cornerstone of data-driven marketing for any growing business. It allows you to compare two versions of a marketing asset and see which performs better.
For busy event professionals, A/B testing is easy to overlook. It takes time, intentionality, and effort. But we promise you…it's worth it. In what you learn through A/B testing, you can:
We’re breaking down the best practices to help you A/B test your event business’s marketing materials so you can set yourself up for business growth and success.
Planning Pod empowers event professionals to focus on the strategic aspects of running their businesses, like effective marketing. Our platform helps you handle the details so you can grow your business and create more unforgettable experiences.
Define your goals. It is essential that you establish clear objectives for any A/B test prior to execution. We recommend starting by identifying your big picture goals for your event business. Are you prioritizing lead volume? Brand awareness? Increased site traffic? Any marketing materials should contribute to accomplishing a specific goal for your event business.
If you’re more focused on business growth, you might prioritize higher click through rates (CTRs) on ads that lead to a high-converting landing page. Or conversely, improving the conversion rate of a landing page attached to an ad campaign with high CTR. On the other hand, if you’re trying to increase brand awareness, maybe it makes more sense to improve open rates for an e-newsletter you’ve created.
Whatever your event business goals may be, taking the time to identify them helps you choose the right metrics to track during any A/B test.
Let’s talk A/B test execution. To really drill down on making your marketing efforts effective, you need to approach your A/B tests like a scientist.
That starts with isolating variables.
No matter what marketing piece you’re testing, you should always test one element at a time (whether it be your headline, image, or CTA) to pinpoint the cause of performance changes. Testing too many things simultaneously muddies the waters and makes it difficult to interpret results.
Speaking of outcomes…the results of your A/B test are only as good as the audience that engaged with it. And it’s super important that you don't rely on small sample sizes. This is why you need to account for statistically significant audience sample sizes.
Not sure how to determine the minimum audience needed for statistically significant results? Use a sample size calculator like this one.
Beyond ensuring a statistically significant sample size for your A/B tests, you need to remember that randomization is key. Translated, this just means you should randomly split your audience between the control (original) and variation (modified) versions of the marketing piece you’re testing. This eliminates bias and ensures a fair comparison. Most A/B testing tools or platforms have built-in functionality for audience randomization.
At this point in your A/B testing journey, it is crucial to determine which relevant metrics to track. Depending on the goals you set for a marketing piece or campaign, you might track clicks, conversions, open rates, bounce rates, or time spent on a landing page. You can use website analytics tools and email marketing software for data monitoring.
Now let’s get into some best practices for A/B testing individual types of marketing materials or campaigns, and what tools you can use to best execute your tests.
Most major Pay-Per-Click (PPC) platforms like Google Ads and Meta Ads Manager offer built-in A/B testing functionalities that fit the best practices we discussed above. These tools allow you to create variations of your ads and easily compare their performance.
Several landing page creation platforms like Unbounce and Optimizely offer built-in A/B testing functionalities. These tools allow you to create variations of your landing page and seamlessly run split tests.
Most email marketing platforms like Mailchimp and Constant Contact offer A/B testing functionalities for subject lines, sender names, and email content.