Interpreting A/B test results involves comparing the performance metrics of both versions. For example, if version A has a higher open rate but version B has a higher conversion rate, you need to weigh which metric is more important for your campaign goals. Statistical significance is also important; ensure that the results are not due to chance by using tools that calculate the significance level.