Here, the CTA is the variable.
Posted: Sun Jan 05, 2025 9:21 am
Automatic alerts about anomalies on your website" window.
Step 2. Find your variable
The data from your analytics tools should provide you with plenty of inspiration for testing variables.
When starting out, it's best to focus on one variable at a time. It will then be easier to spot the reasons for changes in performance.
Here is an example:
If all of your top-performing blog posts include numbers in their titles, you could run A/B tests on your older, lower-performing posts by rewriting their titles to include numbers.
Next, track page views in Google Analytics to see if the change improves engagement.
"Views" column highlighted in the Google Analytics Pages and Screens report
In this case, your variable is the title format .
Or, to improve conversion rates for an email list opt-in form, you could experiment with different call-to-action (CTA ) messages on your landing page to find the most persuasive wording.
Here, the CTA is the variable.
Step 3. Decide on a test hypothesis
Your test hypothesis is the idea you want to prove or disprove with your A/B test.
Continuing with our previous example, your hypothesis could be:
"Blog post headlines that contain numbers are more compelling than blog post headlines that do not contain numbers."
And how will proving or disproving your hypothesis benefit your company? It could be that:
"Adding more listicles (articles that belgium phone number database typically include numbers in their headlines) to our content plan will grow our website traffic and increase engagement."
Even if you end up disproving your hypothesis, you can always modify it based on what you learn during the testing process. Then, start a new experiment, with more data, that will help you further optimize your site.
Step 4. Set your goals and test period or sample size
Most tests involve measuring multiple metrics. If you choose your most important metric before you start, you'll know exactly how to measure effectiveness.
If you're testing two new features at the same time, measuring the same metric for each audience segment (i.e., your primary metric or KPI) will allow you to directly compare performance.
Step 2. Find your variable
The data from your analytics tools should provide you with plenty of inspiration for testing variables.
When starting out, it's best to focus on one variable at a time. It will then be easier to spot the reasons for changes in performance.
Here is an example:
If all of your top-performing blog posts include numbers in their titles, you could run A/B tests on your older, lower-performing posts by rewriting their titles to include numbers.
Next, track page views in Google Analytics to see if the change improves engagement.
"Views" column highlighted in the Google Analytics Pages and Screens report
In this case, your variable is the title format .
Or, to improve conversion rates for an email list opt-in form, you could experiment with different call-to-action (CTA ) messages on your landing page to find the most persuasive wording.
Here, the CTA is the variable.
Step 3. Decide on a test hypothesis
Your test hypothesis is the idea you want to prove or disprove with your A/B test.
Continuing with our previous example, your hypothesis could be:
"Blog post headlines that contain numbers are more compelling than blog post headlines that do not contain numbers."
And how will proving or disproving your hypothesis benefit your company? It could be that:
"Adding more listicles (articles that belgium phone number database typically include numbers in their headlines) to our content plan will grow our website traffic and increase engagement."
Even if you end up disproving your hypothesis, you can always modify it based on what you learn during the testing process. Then, start a new experiment, with more data, that will help you further optimize your site.
Step 4. Set your goals and test period or sample size
Most tests involve measuring multiple metrics. If you choose your most important metric before you start, you'll know exactly how to measure effectiveness.
If you're testing two new features at the same time, measuring the same metric for each audience segment (i.e., your primary metric or KPI) will allow you to directly compare performance.