How to Choose the Best Marketing Hypothesis Testing Method
Posted: Sat Feb 22, 2025 9:33 am
In this section, we'll show you how to choose the right testing tools to get reliable and useful results that improve your marketing campaigns.
A/B testing – checking the effectiveness of changes
A/B testing is one of the most popular methods of testing hypotheses.
Example
Let's imagine a company wants to find out whether placing a banner at the top of a page will increase conversions compared to placing a pop-up in the middle.
Some users will be directed to the old version of the page with a banner, and others luxembourg mobile database to the new one with a pop-up. Based on the test results, we see that the conversion on the new page is higher — 25% versus 17%. In addition, the version with a pop-up brought in ₽11,235 in revenue, while the page with a banner only brought in ₽8,132.
A/B testing confirmed the hypothesis that the pop-up works better and should be left.
To ensure the correctness of the test, it is important to put forward two hypotheses:
Zero: the changes will not produce the desired effect (it needs to be refuted);
Alternative: Improvements will result in a 7% increase in conversion.
Always track key indicators and do not complete testing ahead of schedule. The final data may change significantly. After receiving the results, carefully analyze them to understand whether it is worth making changes.
We tell you how to apply the growth methodology in contextual advertising, how to make the process of testing hypotheses constant and effective, how to correctly evaluate the results of the testing.
A/B testing – checking the effectiveness of changes
A/B testing is one of the most popular methods of testing hypotheses.
Example
Let's imagine a company wants to find out whether placing a banner at the top of a page will increase conversions compared to placing a pop-up in the middle.
Some users will be directed to the old version of the page with a banner, and others luxembourg mobile database to the new one with a pop-up. Based on the test results, we see that the conversion on the new page is higher — 25% versus 17%. In addition, the version with a pop-up brought in ₽11,235 in revenue, while the page with a banner only brought in ₽8,132.
A/B testing confirmed the hypothesis that the pop-up works better and should be left.
To ensure the correctness of the test, it is important to put forward two hypotheses:
Zero: the changes will not produce the desired effect (it needs to be refuted);
Alternative: Improvements will result in a 7% increase in conversion.
Always track key indicators and do not complete testing ahead of schedule. The final data may change significantly. After receiving the results, carefully analyze them to understand whether it is worth making changes.
We tell you how to apply the growth methodology in contextual advertising, how to make the process of testing hypotheses constant and effective, how to correctly evaluate the results of the testing.