What Is A/B Testing?2 min readReading Time: 2 minutes
A/B testing is an essential digital marketing tactic for optimizing your campaigns. With A/B testing, you compare two versions of a marketing piece (usually digital) to see which performs better. The test runs for a predefined period of time to assess performance. Ultimately, a winner is selected based on performance to meet your defined winning metric (most commonly on email open rate or click-through rate).
Sophisticated organizations us A/B testing to optimize many aspects of user experience (especially for e-commerce websites). If you’re just getting started with this tactic, try it with your email marketing.
Many companies that employ this testing strategy on landing pages or websites also combine their efforts with heat mapping. Heat mapping tools can show you what parts of a webpage attract the attention of users. This information enables marketers to place their most important call-to-action on whatever part of the landing page attracts the most attention.
- Split Testing
- Bucket Testing
- Multivariate Testing (usually has more than 2 versions being tested)
- Landing Pages
- Website Design
- E-Commerce User Experience
Getting Started: The Most Common Email Elements to Test
- Subject Lines
- Body Copy
- Header Graphic
- Color Scheme
- Location of Call-to-Action
- Content of Call-to-Action
- Email Length
A/B Testing Best Practices
At a minimum, you should A/B test all email subject lines. Run your email A/B tests for a minimum of 2 hours before you select a “winner.” The built-in testing capabilities in most email marketing platforms allow you to select your test duration and KPI to identify the winner. These systems will then automatically execute your more global “winning” email to the rest of your audience.
If you are testing a website or landing page user experience, you may need to run your test for an extended period of time (ie, weeks or months instead of hours). Your testing period depends on how much traffic you direct to the pages. It may take some time to get a data set that’s large enough to draw conclusions.
If you’re just getting started with A/B testing, check out this great guide from Neil Patel.