A/B testing of newsletter subject lines

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A/B test results show which solution will provide a higher conversion rate to the desired target action. For example, in which case more users will click on a link, register on a website or in an application, subscribe to a newsletter, fill out a feedback form. There are also more complex tests that are aimed at studying long-term metrics, such as the average check or the impact of changes in the product on profit.

To be sure of the reliability of the results, it is better to conduct the test with a control sample. To do this, you need to divide the audience on which the australia consumer email list variants will be tested not into two groups (A and B), but into three (A, A and B). Show one version to two groups, and another to the third. This will reveal whether external factors influenced the test result and whether there were any errors in collecting metrics.

Imagine you are testing a subscription form on a store website.

At this time, another department launched an advertising campaign to promote a certain product in the catalog, and many users came to the site. They want to buy now and are not interested in subscribing at all, so they can distort the results of the experiment.