How do you use A/B testing to optimize your SEO, content, or email marketing strategies?

Up front, you need to understand that you need a big enough sample size for it to really mean anything. I find many startups jump in to do A/B tests but the results are meaningless because they don't have enough data (email addresses, clicks, impressions, etc.) Make sure you are not spending time on A/B testing when you should be figuring out what your target audience's pain points are. You don't use A/B test for that.

The goals could vary in size. Sometimes an incremental improvement of even less than 1% can add up to a lot if the impact is to a huge number...Think of what a 0.25% improvement in conversion is to a web page that gets 1,000,000 clicks. At the same time, challenge the team to see if there are big swings to be made before working on incremental improvements.

I think the goal of the A/B test should be attached to a higher-level campaign. For example: Hypothesis: if we change messaging from "meeting automation" to "inbound conversion", our outbound SDR cadences will lead to an X% increase in booked demos. This way, you can get senior leadership see the point of the test. Also, even if/when it fails, you have a clear takeaway that can inform future tests.

Also, choose the time frame. As a business, you may not have the luxury to run a test for more than a week. Equally so, you should define factors that will cause you to stop or pause the test. There are many times when it's fairly clear that a test isn't working and it's not worth it to continue.

Testing button colors is much more focused on conversion rate optimization. A/B testing applied to content strategy serves as a way to test messaging and campaigns at a higher level. Understanding the key message that resonates with a target audience is the first step. Then we can get into the details of button colors, length of email, tone, etc. But big picture comes first in my mind.

I've often noticed that test results can come back with a positive bias at the onset of a test. It may help to run an A/A test just to see how much that tends to be. You may end up with a false positive that way when running a test not long enough.

In my experience during analyzing the results, we’d need to remember to always link the metrics to the most important goal, and not only to look at the statistical significance but also to check economic/practical significance. Some tests are statistical significant, mainly due to big sample size, but the lift is so minimal. For this, we may need to redesign the tests or figure out why before implementing any changes.

Be careful not to refine your content to get better clicks, opens, etc but no new sales (donations, attendance, etc). Remember, your organization's ultimate. Standard digital marketing goals sometimes diverge from the company's goals if you get lost in the weeds.