Step-by-Step Guide to A/B Testing
Looking for a low cost, high reward way to … optimize marketing campaigns? Enhance UI/UX? Increase conversions?
Pro Tip: Consistently improve marketing results by making A/B testing a regular part of your process.
A/B testing, or split testing, is marketing research in which you segment your audience to test creative variations and determine if one version statistically outperforms another. This technique helps marketers better understand their audience and how to effectively grow engagement, increase conversion, and reduce bounce rate. A/B testing is most commonly used to optimize the performance of emails, landing pages, digital ads, social media, blog content, calls to action, and more.
Not sure where to start? Follow these simple steps to successfully run an A/B test.
Step 1: Select one variable to test
Identify one independent variable and measure its performance. Isolating just one element will make it easy to evaluate what was responsible for changes in performance. Keep in mind, even minor changes can have an impact on audience behavior.
Step 2: Confirm research goals
Although you'll monitor and measure a variety of campaign metrics, choose a primary metric that will signify success. In addition, determine how significant the results must be to rationalize choosing one variation over another. The higher the percentage of your confidence level, the more certain you can be about your decision.
Step 3: Version the content
Create two versions of creative – one that is an unaltered version of whatever you’re testing (the “control”) and a second version with a single changed element (the “challenger”). For example, test the performance of a social media ad that utilizes the same post copy but features different images.
Step 4: Segment your audience
Split your sample into random groups of equal size. By randomizing cohorts, you minimize the chance that other factors will influence results. It is also important to ensure the sample size is large enough to achieve statistically significant results.
Step 5: Test versions simultaneously
Unless the variable your testing is related to timing, run the two variations at the same time. Consumer response may vary based on the time of day, day of the week, or month of the year.
Step 6: Measure and implement results
If one version is statistically stronger than the other, complete your test by disabling the losing variation. This experiment not only has immediate impact on your current campaign results, but you can also apply these invaluable insights to future efforts.
Don’t stop there! Due to the cost-effective nature of this solution, you can continue to test other features and elements to identify additional opportunities for optimization.
