3 A/B Testing Mistakes You Should Avoid

How to Run Effective Tests for Better Results

A/B testing is a powerful tool for handmade sellers looking to optimize their marketing strategies and boost conversions. However, running successful tests involves more than just comparing two versions of an email or webpage. Let’s dive into three common A/B testing mistakes and how to avoid them.

Tips + Real-World Examples from My Experience:

❌ Not Testing Your Flows: A common oversight is focusing solely on A/B testing email campaigns while neglecting automated flows. These flows are your "silent revenue generators" and can provide valuable insights. In my business, I started testing my welcome series and cart abandonment flows. By tweaking subject lines and send times, I saw a 15% increase in conversion rates. Now, it's a standard practice for me to run A/B tests on every flow, which not only helps identify what resonates with my audience but also makes future adjustments easier.

❌ Not Letting Tests Run Long Enough: Ending tests too soon can lead to misleading results. It's tempting to call a winner after a few days, but doing so often results in false assumptions. I learned this the hard way when I prematurely ended a product description test on my site. The initial results looked promising, but when I let the test run longer, the data revealed a different outcome. Since then, I make sure to wait for a statistically significant amount of data before drawing conclusions.

❌ Ignoring External Factors: Timing can significantly impact your A/B test outcomes. Running a test during peak holiday shopping or after a sudden traffic spike can skew results. I once ran a test on my email open rates without considering an ongoing holiday weekend promotion. The higher-than-usual open rates led me to believe the test was a success. When I re-ran the test under normal conditions, the results were drastically different. Now, I always account for seasonality and marketing campaigns when planning my tests.

Question: 

Are you letting your A/B tests run long enough, or are you rushing to conclusions that might be costing you sales?