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How do you A/B test display ads for different audiences and segments?

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Display ads are a powerful way to reach and engage your target audience online. But how do you know which ad design, copy, or placement works best for different segments of your audience? That's where A/B testing comes in. A/B testing is a method of comparing two versions of an ad to see which one performs better on a specific metric, such as click-through rate, conversion rate, or revenue. In this article, you'll learn how to A/B test display ads for different audiences and segments, and how to use the results to optimize your display advertising strategy.

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1 Why A/B test display ads?

A/B testing display ads can help you improve your ad performance and achieve your marketing goals. By testing different elements of your ads, such as images, headlines, colors, calls to action, or landing pages, you can find out what appeals to different segments of your audience, and what drives them to take action. A/B testing can also help you avoid wasting money on ineffective ads, and increase your return on ad spend (ROAS).

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Joe Fried

Visionary media executive + global citizen driving transformative business outcomes for world-class brands
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Test and learn agendas, and A/B testing structures, are critical to identifying opportunities to drive incremental improvement for advertising campaigns and is a cornerstone of a performance culture. Without this approach, you are essentially "setting and forgetting" and missing out on opportunities to improve campaign efficacy.

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2 How to define your audience and segments

Before you start A/B testing display ads, you need to define your audience and segments. Your audience is the group of people who are likely to be interested in your product or service, and who you want to reach with your display ads. Your segments are the subgroups of your audience that share common characteristics, such as demographics, behaviors, preferences, or needs. For example, if your audience is online shoppers, you can segment them by age, gender, location, purchase history, or product category. To define your audience and segments, you can use tools such as Google Analytics, Google Ads, or Facebook Audience Network, which can provide you with data and insights on your website visitors, customers, and prospects.

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3 How to create and run A/B tests

Creating and running A/B tests for display ads requires following certain steps. First, you should choose a hypothesis and a metric. A hypothesis is a statement that predicts how a change in your ad will affect your metric. For instance, a hypothesis could be “Adding a discount code to the headline will increase the conversion rate of the ad”. You should then create two versions of your ad: the control, which is the original ad that you want to test, and the variation, which is the modified ad that includes the change that you want to test. Next, you need to split your audience and segments into two groups: one group that will see the control ad and another group that will see the variation ad. To run your A/B test for a sufficient period of time, you should use tools such as Google Optimize, Optimizely, or VWO to calculate the statistical significance of your A/B test results and determine the minimum sample size and duration of your test. Finally, analyze and interpret your A/B test results by comparing the performance of your control and variation ads on your chosen metric, and check the confidence level of your A/B test results.

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4 How to use A/B test results to optimize your display ads

Once you have your A/B test results, you can use them to optimize your display ads for different audiences and segments. To make the most of your A/B test results, you should implement the winning version of your ad and scale up its budget or reach. If the control ad performs better than the variation ad, you can keep it as your default ad and try to create another variation. Additionally, if different segments respond differently to your ads, you can segment and personalize them to match each segment's preferences, needs, or behaviors. You can also use tools such as Google Ads or Facebook Audience Network to create dynamic display ads that automatically adjust to each segment.

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5 How to avoid common pitfalls of A/B testing

A/B testing display ads can be a powerful way to improve ad performance and achieve marketing goals, but there are common pitfalls to avoid. Testing too many variables at once can make it hard to isolate the effect of each variable and determine which is responsible for the difference between control and variation ads. Additionally, running too many or too few A/B tests can lead to unreliable results or missed opportunities. Finally, ending A/B tests too early or too late can mean incomplete data or wasted time and money. To combat these issues, prioritize A/B tests based on goals, resources, and opportunities, and use tools such as Google Optimize to calculate sample size and duration, as well as monitor statistical significance and confidence level.

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Vito Curcuru

Founder @ Consult Vito | Marketing, Brand Identity, Social Media Strategy, Inbound Marketing
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Four pitfalls to testing include 1)expecting too much, 2) testing time 3) too many elements and 4) the constant desire to keep testing. 1) The extreme learning may not happen in an A/B test. There is an expectation that A/B testing will solve a lot of marketing problems. Sometimes you won't get the extreme data point. Keep testing. 2) Ad platforms should be given time to test each ad and optimize. Let the test run for two weeks if you can. 3) The best learning is often done from offer vs no offer or element A vs no element A. People often want to put 20% off vs 40% off or element A vs element B. This kind of test can work, but start slowly when testing to get some kind of base information. 4) Take time to analyze results.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you A/B test display ads for different audiences and segments?

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1 Why A/B test display ads?

2 How to define your audience and segments

3 How to create and run A/B tests

4 How to use A/B test results to optimize your display ads

5 How to avoid common pitfalls of A/B testing

6 Here’s what else to consider

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