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What is A/B Testing
As the name suggests, A/B testing is comparison-based testing. The experiment is performed by executing the product or campaign in two different options and then comparing them to derive inference and analyze the result. It is used most commonly for testing two variations to understand which one performs better when exposed to different audiences. There is a difference between the two versions, like the color of the call to action(CTA) button, change in the subject line of the email, etc. It gives us an insight that which one performs better in a small sample so that we can use that for a broader audience.
Steps in A/B testing
1.Choose your objective- For any research or experiment, you must know your research question. Forming the test around the aim is an efficient way to minimize distractions and provides a framework for the analysis. Eg. Maximize clicks, sign-up for the newsletter, sales, etc.
2. Choose the variable- You think people will open your email if the subject line has action words or if it has some ambiguous message. Will this strategy also lead to click for the landing page to your website or not. These can be the determinants for choosing the variable.
- Colors, Sentences, layouts, etc. are standard variables to understand the psychology of the viewer.
3. Performing the test-
After taking care of the two steps above, create two versions of the email/ad/landing page– version A and version B. Divide the target audience into two groups so that a person cannot see both the versions, and observe which of the version performs better.
4. Analyze the data-
The winning version will be the one that you can comfortably send to a large number of people, expecting to see the same rate of conversion.
If there is no clear winner then either use any of the versions or redo the tests; in any case, you will be confident about your ad performing the same way it did during the test.
A/B testing takes time from its inception until the analysis, and it is suitable for a slight single variation in the versions. If there are more than two variables, the results become inconclusive because we cannot assess the performance of the version is dependent on which one.
There are many tools that are user-friendly and also provide analysis and reports to lighten the workload. Optimizely, Hubspot Email campaign, VWO are some of the reliable ones. For a data-driven decision making A/B testing can be one of the crucial tools.