How to A/B Test Your Marketing Messages Using Tracked Links
A basic A/B test just needs two comparable variants and consistent UTM tagging, no specialized split-testing software required.
Priya Nair
Senior Marketing Analyst
Campaign Tracking & Analytics Blueprint Guide
What is the basic logic of an A/B test?
An A/B test shows two different versions of something, a headline, an image, a call-to-action, a subject line, to comparable audiences, then measures which performs better against a specific, predefined metric. The key requirement for a valid test is that the two versions are comparable in every way except the one variable being tested, so any performance difference can be confidently attributed to that variable.
How do you use utm_content to distinguish test variants?
For messages distributed through trackable links, email, social posts, ads, utm_content is the parameter suited to labeling which variant a click came from. If you're testing two call-to-action phrasings in an email, both linking to the same landing page in the same campaign, you'd keep utm_source, utm_medium, and utm_campaign identical between the two versions while setting utm_content to something like cta_shop_now for one and cta_learn_more for the other.
How do you test email subject lines with tracked links?
Subject line testing needs a slightly different approach, since UTM parameters live in the link, not the subject line, and the subject line is what determines whether someone opens the email in the first place. You typically need your email platform's built-in split-testing feature to measure open rate differences, but once someone opens and clicks through, utm_content can further track click behavior by which subject line variant they received, revealing whether the subject line's messaging also influenced click quality.
How do you structure a landing page or offer test?
If you're testing two landing pages or offers rather than two variants of the same page, structure the test by sending different UTM-tagged links (with distinct utm_content or distinct URLs entirely) to comparable audience segments, for instance, alternating which variant every other email recipient sees, or splitting a paid ad's audience into two equally sized groups, each seeing a different version.
What makes an A/B test comparison fair?
The validity of any link-based A/B test depends on exposing both variants to comparable audiences under comparable conditions at the same time. Running variant A one week and variant B a different week introduces a confounding factor, time-based fluctuations unrelated to the actual variant, that can make results misleading. Both variants need to run simultaneously to the same overall audience, split as evenly and randomly as possible.
How do you read the results in your analytics platform?
Once both variants have collected sufficient data, filter your analytics reports by utm_content to compare their performance directly, click-through rate, conversion rate, and revenue per click are typically the most meaningful metrics for judging a winner, rather than raw click counts alone, especially if the two variants received different amounts of exposure.
What mistakes should you avoid in link-based A/B testing?
A frequent mistake is testing too many variables at once, changing both the headline and the image, for instance, which makes it impossible to know which change actually drove any observed difference. Another common mistake is drawing conclusions from too small a sample size, where the apparent difference between variants could easily be due to random chance; a meaningful test needs enough total clicks and conversions in each group to produce a statistically reliable result.
How do you build a repeatable testing habit?
The real value of link-based A/B testing comes from doing it consistently, not just once. Building a habit of testing one specific variable in most major campaigns, a subject line, a call-to-action phrase, an image style, and recording results over time builds an increasingly detailed understanding of what resonates with your specific audience.
Frequently Asked Questions
Do you need special software to run an A/B test on marketing links?
No, a disciplined UTM tagging habit using utm_content to label variants, plus a functioning analytics platform, is enough to run genuinely useful A/B tests on messaging and creative.
Which UTM parameter should you use to label A/B test variants?
utm_content, while keeping utm_source, utm_medium, and utm_campaign identical between the two versions so only the variant itself differs.
Why did my A/B test results look misleading?
Two common causes are running variants at different times instead of simultaneously, and drawing conclusions from too small a sample size where the difference could be due to random chance.
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