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Are your marketing campaigns actually working, or just… working? A/B testing (or split testing) is your crystal ball, giving you data-backed answers to boost ROI.

  • Test Everything: Test headlines, images, CTA text, or even pricing.
  • One at a Time: Only change one element to know exactly what caused the win.
  • Segment: Dig deeper to see if “A” beats “B” with different audience segments.
  • Implement & Optimize: Use the winner and repeat the process.

Stop guessing what your audience wants and let them show you with data.

Are your marketing campaigns performing as well as they could be? Whether a campaign isn’t meeting expectations or is providing satisfactory results, there’s always the potential to improve it. The challenge, though, is figuring out which tweaks will boost performance. The only way to know for sure is to test them. That’s where A/B testing comes in.

What Is A/B Testing in Marketing?

What Is A/B Testing?

Before we get into how to use A/B testing, let’s define it. A/B testing, also called split testing or bucket testing, is a method for testing which version of an ad, landing page or any other element of a marketing campaign performs better. To conduct an A/B test, you change one aspect of your campaign and run both variants, collecting data on performance. You can then implement the change that got the better results.

Maximize Your Marketing Campaigns ROI

For example, you might write two different phrases for your call-to-action (CTA) button on a landing page. You would then run both these versions at the same time and collect data about which led to more conversions. You can use this same process to test an ad, social media post, email or any other element of a campaign, as well as to refine different aspects of that same landing page.

When Can You Use A/B Testing?

When Can You Use A/B Testing?

A/B testing can be valuable anytime tweaking an element of an online property or ad could improve performance and help you achieve the campaign’s goal. As mentioned earlier, you can’t know for sure if a change will improve your campaign’s performance until you test it. You may be able to use general best practices to get you started, especially for broader strategies. When it comes to the details, though, you need to test different ideas to determine what will work best for you.

Maximize Your Marketing Campaigns ROI

That’s why split testing is important. It allows you to test your actual campaigns with your real audience and gives you data to back up the decisions you make. Optimizing your marketing in this way can help you meet your campaign goals, whether you’re looking to boost sales on an e-commerce site, increase engagement on your social media channels or achieve some other objective.

You can test these elements at any point during your campaign. A/B testing can be useful at the start of an initiative, but you should also use it to fine-tune your strategy as you go. The more testing you do, the more you can refine your campaign. Over time, you can improve your campaign’s performance drastically. You should also periodically come back and re-test elements, since what works best can change over time.

A variety of media types might play a role in achieving these objectives. Some of these channels include:

  • Marketing emails
  • Banner ads
  • Social media posts
  • E-newsletters
  • Website pages
  • Mobile advertising
  • Marketing campaign strategies

Choosing Campaign Elements to Test in A/B Testing

You can use A/B testing to evaluate the effectiveness of any element that may influence customer behavior or impact conversion rates. Some examples of items to test are:

  • Headlines
  • Subheadlines
  • CTA text
  • CTA button
  • Email subject lines
  • Images
  • Graphics
  • Product descriptions
  • Ad copy
  • Layout
  • Color scheme
  • Subheadings
  • Offers
  • Free trial lengths
  • Pricing structures

With so many options, how do you decide which elements to test? To get the maximum benefit from your testing, it’s best to have a structured method for choosing what to assess. In a recent survey by Econsultancy and RedEye, 74 percent of respondents who used a structured approach to conversion increased their sales.

First, you’ll want to conduct some research. Look at your data from past campaigns to get an idea of what you might want to improve. Use web analytics and mouse tracking analysis to find out how visitors use your site. If you’re considering A/B testing email marketing, look at data such as open rates, click-through rates and conversions. You can also use qualitative research such as surveys and interviews to get people’s opinions on your site, emails or whatever you’re considering testing. Finally, don’t forget to compare elements of your campaigns to known best practices. This research should provide you with a list of factors to test.

Next, you need to choose which elements to test first. Prioritize them based on how much value a potential improvement could provide. You can estimate their value using research methods such as those listed above. Also, evaluate how difficult it would be to make the necessary adjustments and consider aspects such as how long it would take, how complex making the change is and any risks involved.

Finally, test the elements in the order in which you prioritized them. You can assess every component of a campaign if you want, but make sure you only run one test at a time. That way, you know for sure which element led to the improved or worsened performance you find through your testing.

How to Do A/B Testing for Marketing Campaigns

So, how do you run A/B tests for your marketing campaigns? Here’s a basic process you can follow.

  1. Determine which campaign elements you want to test: First, you need to decide what to test. Look for landing pages, ads or other aspects that are underperforming, or evaluate past campaigns. Then, use web analytics and other research tools to develop a hypothesis for why it is not performing well. You might be wondering, for example, whether your CTA button is too small. Rank the elements you’re considering testing, and start with the top-priority item.
  2. Create two variations of that element: Once you decide what you want to test, choose or create the two variants. For example, you might design two versions of a banner ad — one with an image and one without. Alternatively, you can test a new version of an element against an existing one. For instance, you could leave one landing page as-is and compare it with the same page, but with a larger CTA button.
  3. Establish a plan for measuring your results: Make sure you have a strategy in place for tracking the metrics of your campaigns. Know what indicators you’re measuring, whether that’s more sales, more newsletter signups, more comments on your Facebook posts or something else. Also, define how big of a change would be statistically significant. If you’re testing something for an existing campaign element, you can use its current performance as a baseline.
  4. Set a timeline for your test: Determine how long you will run the test. Make sure your testing period isn’t too short or too long, as this can lead to inaccurate results.
  5. Run the test: Now, it’s time to run the test. Make sure you test one element at a time, so you know which element influenced the results. To avoid factors that may skew the results, run the two variations simultaneously and try to keep the groups seeing each version similar in size, demographics and other variables. If running a large test on a website page, you may randomly split your visitors between the two variations. If testing a marketing email, you can create two test groups of customers with similar or identical demographics.
  6. Check your results and implement changes: Once your test has run for the predetermined amount of time, you’ll have your results. If your test didn’t produce conclusive results, adjust your hypothesis and run another one. If a clear winner did emerge, implement the variation that performed better. Feed the data from your analysis into your data management platform to help you improve your current and future campaigns.
  7. Repeat the process: You can use A/B testing over and over to continue to refine your marketing campaigns for even better performance. After your first test, run another with the next element on your prioritized list. This element can be part of the same item you just tested, or part of another one. You should also repeat A/B testing as trends and customer preferences change over time.

Maximize Your Marketing Campaigns ROI

Implementing A/B Testing Results to Maximize Campaign Performance

The way in which you run your tests is crucial for getting accurate results, but what you do once you have your results is essential, too. You should implement the best-performing variant, but there are also other ways you can use your results to improve campaign performance. Here are a few tips for using your A/B test results.

  • Implement results across your site: Once you’ve applied what you’ve learned to the web page, email or ad you tested, try using it on other similar elements, too. If a red CTA button works better on one landing page, for example, it might also work better on another. You can even test these other pages to verify your results. One split test can lead to improved performance across your entire site or marketing campaign.
  • Track differences between audience segments: You can also drill down further into your test results to get even better insights. One of the best ways to do this is by looking at your results across various audience segments. There’s a huge range of segments you can look at. You can split audiences based on the kinds of devices they’re using to access your site, whether they’re new or returning visitors and whether they came to your page directly or through an internal link. You can also look at demographic data such as age, location, gender and income level, as well as data about interests, beliefs and preferences.
  • Use different elements across segments: Use what your tests teach you about your audience segments to create campaigns tailored to different kinds of customers. For instance, you might find a page with large graphics works better for users on smartphones, while a bit more text works best for those on desktops. You can then create two variations of your page for both audiences.
  • Use results to inform future tests: Using the results of each test to perform future tests can help you work more efficiently and get better results. If you find your customers like videos, for example, you can try testing more videos in the future.
  • Combine the results of tests: You can also analyze different elements of pages you’ve already run tests on to refine them further. If you first tested the subject line of an email, for example, next test the copy in the body of the email. The more you test, the more data you have. Combining the results of your tests can help you further improve your campaigns.
  • Archive your test results: Once you finish each test, make sure you archive the results in an organized way. Using a DMP can help with this. Saving this test data allows your knowledge to grow over time, helping you improve your marketing campaigns over the long term.

A/B Testing Audience Segments

Marketers often associate A/B testing as comparing creative or landing pages, but the same discipline applied to audience segments. They can deliver some of the most powerful insights into who’s actually converting. Testing audiences helps you validate assumptions, uncover hidden opportunities, and fine-tune data strategies that directly impact your ROI.

Why Test Audience Segments

Even a strong campaign can underperform if it’s shown to the wrong people. Testing audience segments allows marketers to identify which audiences deliver the best engagement or conversion rates for each creative message or channel. Instead of relying on intuition or outdated personas, A/B testing audience segments helps verify what data actually moves the needle.

Key benefits include:

  • Performance validation: See which audience attributes (demographics, behaviors, intents, or lookalikes) actually convert.
  • Budget efficiency: Shift spend toward the audiences that outperform benchmarks.
  • Data quality insights: Identify gaps or inconsistencies in your audience data that may be hurting results.

How to Structure Audience Segment Tests

Start simple: test two clearly defined audiences against a consistent creative and placement. For example, you might compare:

  • First-party vs. third-party segments
  • Interest-based vs. behavioral segments
  • Lookalike audiences vs. contextual targeting

Keep all other variables constant (creative, bid strategy, placements, and budget split)  to isolate performance differences driven by audience definition alone. Over time, expand your tests to include micro-segmentation or multi-cell experiments (e.g., demographic + behavioral combinations).

Metrics That Matter

When evaluating results, don’t just look at CTR or impressions. Focus on metrics that reflect your true goal like conversion rate, cost per acquisition (CPA), or return on ad spend (ROAS). Also watch for engagement quality indicators like dwell time or scroll depth on landing pages.

Pro Tip: Feed Learnings Back Into Your Data Strategy

Audience A/B testing shouldn’t be a one-off project. Build a feedback loop where the winning audience traits inform future segment creation, enrichment, and cross-channel activation. Lotame’s products make it easy to unify first-, second-, and third-party data, refine segment definitions, and retest hypotheses — turning A/B testing into an ongoing audience optimization engine.

Benefits of A/B Testing in Marketing

Benefits of A/B Testing in Marketing

Maximize Your Marketing Campaigns ROI

Marketers can derive tremendous benefits from using A/B testing. Here are some of the main advantages it provides.

  • Increased certainty about strategy: With A/B testing, you no longer have to guess what will resonate with your audience. It provides you with firsthand data about your campaigns and your audiences, which you can use to drive your marketing strategy. Backing up your marketing decisions with data will likely help increase buy-in from management and other departments within your company.
  • Improved campaign performance and progress toward goals: A/B testing allows you to continuously improve your campaigns’ performance and helps you make progress toward your campaign goals. Tests of different elements can help with different objectives. Some common goals include boosting conversion rates, increasing web traffic, reducing bounce rates and increasing engagement with content. Implementing the results of your A/B tests can also contribute to reaching broader business goals, such as increasing overall sales.
  • Improved ROI: A/B testing helps improve the ROI of your campaigns and overall marketing activities, ultimately boosting your company’s bottom line. Refining your campaigns using A/B testing makes them more effective. Thanks to today’s digital technologies, split testing is also inexpensive. Improving the performance of your existing campaigns and reducing ad waste through A/B testing is much more cost-effective than increasing the reach of your campaigns. It helps you make the most of the traffic you already have, rather than paying for more.

How Lotame Can Help

How Lotame Can Help

Lotame’s Spherical Platform can help you conduct A/B testing to improve your campaigns’ performance. Learn how Lotame can help you maximize campaign performance and better reach your target audience, contact us today.

Frequently Asked Questions

How many audience segments should I test at once?

You should ideally test two audience segments at a for clean results. Anything more turns into a multivariate experiment and muddies the insights unless you have very large budgets.

How long should an audience A/B test run before I trust the results?

You would run a test until each audience hits statistically meaningful volume (impressions and conversions). Stopping early is how marketers end up “optimizing” into noise. There’s not set number of days as it all depends on budget and how quickly you can get to statsig. 

Should my creative stay exactly the same when testing audiences?

Creative, placements, bids, and budgets should remain identical. If anything else changes, the test is skewed and results might not be trustworthy.  

What budget split should I use for an audience test?

For a budget split, 50/50 is the cleanest choice. If one audience is tiny (e.g., niche first-party), force-even budgets instead of relying on platform automation.

Which metrics matter most when comparing audience performance?

The metrics that matter most for A/B testing are conversion rate, CPA/CPL, ROAS, and lead quality indicators. CTR alone can be a trap as it tells you who clicks, not who converts.

Can I test first-party and third-party audiences against each other?

Absolutely you can test first- and third-party audiences against one and other. This is one of the most valuable tests you can run because it reveals where your strongest intent signals actually come from.

How do I know if my audience segments are too similar to produce meaningful differences?

To know if your audience segments are too similar, if their definitions overlap more than ~40–50 percent, you’ll see blended results. Use distinct sources or attributes (behavior, intent, demographics, purchase history) to keep the test clean.

How do I apply findings from audience A/B tests to future campaigns?

To apply findings from audience A/B tests for future campaigns, feed winning traits back into your audience strategy: refine lookalikes, enrich segments, tighten behavioral definitions, and adjust suppression lists. Over time this creates a compounding data advantage.

How much traffic do I need for an A/B test to be statistically valid?

The amount of traffic you need for an A/B test varies. You generally need a few hundred conversions per variation for confident results. Low-volume tests lead to false positives that break performance when scaled. You can use a sample size calculator to get a sense of how much traffic you might need.

What’s the difference between A/B testing and multivariate testing?

A/B tests isolate one variable at a time, while multivariate tests compare multiple variables simultaneously. Multivariate testing requires much higher traffic and is often overkill for most campaigns.

How do I pick a winning metric?

Choose the KPI tied to your business goal — conversions, CPA, ROAS, or lead quality. Avoid optimizing for vanity metrics like CTR unless the goal is explicitly upper funnel.

What should I do if my A/B test results are inconclusive?

If results are flat or statistically indistinguishable, it means the variable didn’t meaningfully influence user behavior. Move on to a higher-impact variable or rethink your strategic hypothesis.

How do seasonality and external events affect A/B test accuracy?

Major holidays, news cycles, and platform algorithm changes can distort results. Always annotate tests with timing context and avoid running experiments during volatile periods.

How can I ensure my A/B testing isn’t hurting overall performance?

Set a minimum performance floor and daily budget guardrails. If a test variant tanks, pause it quickly and protect your baseline.

About the Author

Danielle Smith

Danielle Smith

Vice President, Marketing

Danielle Smith is VP of Marketing at Lotame, overseeing marketing efforts and raising awareness and adoption of its next-gen data solutions among brand marketers and media owners.

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