Every event organiser makes dozens of marketing decisions based on gut feeling. Should the headline image feature the artist or the venue? Should the event description lead with the lineup or the experience? Should the call-to-action button say "Buy Tickets" or "Get Your Tickets"? A/B testing (also called split testing) gives you a method to answer these questions with data rather than intuition.
What is A/B testing?
A/B testing involves creating two versions of something, showing each version to a different group of people, and measuring which performs better against a defined goal. Version A might be your current event listing image; version B is an alternative. Half your audience sees version A, half sees version B. After enough people have been exposed to both, you compare the conversion rates and declare a winner.
The key principles are that you change only one variable at a time (otherwise you cannot attribute the difference to a specific change), you need a large enough sample size for the result to be statistically meaningful, and you define your success metric before running the test rather than picking the metric that looks best afterwards.
What to test in event marketing
Event listing images
The main image on your event listing is often the first thing a potential attendee sees. Test different image styles: photos of previous events vs. designed graphics, close-up artist shots vs. crowd shots, bright colourful images vs. dark moody ones. On platforms like Facebook and Instagram, you can run the same advert with different images to see which generates more clicks.
Event descriptions
The way you describe your event affects whether people read on and, ultimately, whether they buy. Test different opening lines. Does leading with the headliner name work better than leading with the experience ("An intimate evening of jazz in a candlelit cellar bar")? Does a shorter, punchier description outperform a longer, more detailed one?
Call-to-action buttons
The wording and design of your ticket purchase button matters more than most organisers realise. Test "Buy Tickets" vs. "Book Now" vs. "Get Tickets" vs. "Reserve Your Spot." Test button colour, size, and placement. These seem like minor details, but small changes to high-traffic elements can move conversion rates by a meaningful amount.
Email subject lines
Email A/B testing is one of the easiest places to start because most email marketing platforms have built-in split testing features. Send two different subject lines to small test groups, wait a few hours to see which gets more opens, and then send the winning version to the rest of your list.
Pricing presentation
How you present your ticket pricing can affect conversion. Test showing the full price upfront vs. highlighting a saving ("£30, save £10 off door price"). Test whether displaying a "from £X" price drives more clicks than showing the standard price. Test whether listing all ticket tiers on the event page helps or creates confusion. Your pricing strategy deserves the same rigour as your creative content.
Landing page layout
If you have a dedicated event landing page, test different layouts. Does putting the ticket purchase button above the fold (visible without scrolling) improve conversion? Does including video increase time on page and conversion? Does adding testimonials from previous attendees help? Each of these is a testable hypothesis.
How to run an A/B test properly
Define your hypothesis
Before you test anything, state what you expect to happen and why. "I believe that using a photo from last year's event as the listing image will increase click-through rate because it shows people what the experience looks like" is a hypothesis. "Let us try some different images" is not. The hypothesis gives you a framework for interpreting the results.
Choose one variable
If you change the image, the headline, and the description all at once, and version B outperforms version A, you have no idea which change made the difference. Test one element at a time. This is slower but produces actionable insights. If you must test multiple changes together (often called multivariate testing), you need significantly larger sample sizes and more sophisticated analysis tools.
Determine sample size
The number of people needed for a statistically significant result depends on the expected difference in conversion rates and the baseline conversion rate. As a rough guide, you need at least a few hundred conversions (not just visitors) for each variation to detect a meaningful difference. Free online calculators for A/B test sample sizes can help you estimate the required traffic.
For event organisers with smaller audiences, this means you may need to run tests over longer periods or accept that some tests will not reach statistical significance. Even inconclusive tests provide directional data that is better than no data at all.
Run the test for long enough
Do not call a test based on the first few hours of data. Behaviour varies by time of day and day of week. Run tests for at least one full week to capture these natural variations. If your test shows a clear winner with high statistical confidence before the week is up, you can end it early, but err on the side of patience.
A/B testing tools for event organisers
For email testing, your email platform's built-in A/B feature is usually sufficient. Mailchimp, Brevo, and Campaign Monitor all offer this.
For website and landing page testing, Google Optimize was a popular free tool but was discontinued in September 2023. Alternatives include VWO (Visual Website Optimizer), Optimizely, and Convert. If you use WordPress, plugins like Nelio A/B Testing provide on-site testing capabilities.
For advertising creative, both Meta Ads Manager and Google Ads have built-in A/B testing features. Meta's Experiments tool lets you run controlled tests comparing different ad creatives, audiences, or placements. Google Ads allows you to run campaign experiments that split traffic between the original and variant campaigns.
For simpler tests, even a spreadsheet approach works. Run version A for one week and version B for the following week, recording the same metrics for both periods. This is not a true controlled experiment (external factors may differ between weeks), but it provides useful data for organisations without access to sophisticated testing tools.
Interpreting results and avoiding common mistakes
The most common mistake is declaring a winner too early. If version B has a 3% conversion rate after 50 visitors and version A has a 2% rate, the difference is likely due to chance. You need enough data for statistical significance. Most testing tools display a confidence level; aim for at least 95% confidence before making decisions.
Another frequent error is testing things that do not matter. The colour of a button border is unlikely to make a meaningful difference to your ticket sales. Focus your testing on high-impact elements: the headline, the main image, the pricing presentation, and the call to action. These are the elements that genuinely influence purchase decisions.
Finally, remember that test results are specific to your audience and context. What works for a heavy metal festival will not necessarily work for a classical concert series. Build your own library of test results over time, and let that accumulate into a nuanced understanding of what your specific audience responds to. This data-driven approach to marketing is what separates organisers who consistently improve their results from those who rely on hunches. For broader insights on using data in your event business, see our guide on how analytics helps organisers sell more tickets.
