12 Conversion Metrics You Should Be Tracking

[Shakil1984]

Are you testing the effect of changing a button color? Then change only the color of the button in the challenger variant and nothing else. If you also change the text on the button or the layout of the page, you’ll find it difficult to determine which change had the greatest impact on the results.

Changing multiple elements at once can also lead to inaccurate results as the changes may interact with one another in unexpected ways.

Mistake 2: Ignoring the statistical significance
In A/B testing, it’s possible that the results of a test come from chance rather than a true difference in the effectiveness of the variants. This can lead to false conclusions about which variant is better, resulting in poor decisions based on inaccurate data.

Here’s an example: saudi arabia email list your test shows that variation A has a slightly higher conversion rate than variation B, but you don’t take into account how significant the results are. So you end up concluding that variation A is the better option. However, considering the statistical significance would have made it clear there wasn’t enough evidence to conclude that variant A was indeed better.

Ignoring statistical significance in A/B testing leads to a false sense of confidence in the results, causing you to implement changes that may not have any real impact on performance.

Mistake 3: Not running tests for long enough
This next mistake goes hand in hand with mistake #2: ending a split test before it has had enough time to collect sufficient data to produce a statistically significant. You’ll end up with inaccurate conclusions about the element you’re testing.

Imagine an A/B test runs for only a week and you declare a particular variant the winner. In reality, the results were only due to chance. Make sure you’re running tests long enough to accurately capture the differences between the versions.