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5 Key Parameters To Imporve Your A/B Tests

 “A/B tests are comparing two versions of a web page to see which version performs better.”

It is comparing the existing version of a web page with the modified version. A small change in how your web page conveys can bring high conversion rate. The purpose of the test is to identify the most effective way to increase the conversion rate. To do A/B tests we create to variants of same web page for a particular goal, say A and B. Variant A is shown to half of the visitors and variant B is shown to another half. Thus it helps to identify which version of web page is giving maximum conversion rate.

AB testing

Any little change in the web page can change the visitor behaviour. Some of the key parameters that can be tested by A/B testing are

  • Headlines
  • Sub headlines
  • Paragraph text
  • Call to Action Text
  • Call to action button
  • Images
  • Landing pages
  • Promotional tagging and offers

Before doing A/B testing it’s important to know which variable we need to test. In order to know which ad gives more conversion rate, we have to display one ad to a st of people and another ad to a different set of visitors. The conversion rate of each advertisement campaign can be analysed. This helps us to identify which ad is more effective in giving results. In the same way we can do A/B test on email campaigning also. We sent a particular email to a group of visitors say A. Another version of same email is send to another group of visitors, say B. The version of email which brings more conversion rate can be used next time.

How can we make A/B tests more effective?

Let us discuss major five parameters to improve the A/B testing

  1. Balanced Control group and the test group – Control Group is the group of visitors who get the original version. And the test group is the group of visitors to whom we send the new version for testing or comparing the performance. It is important that we have a balanced control group and the test group. It will be difficult to get a read on the base line without a good sized control group. Often the control groups are limited to increase the learning potential of test group. It is advised to keep the control group to 50% in the beginning. With the progress of A/B testing this rate can be reduced. This helps to know what is happening in the current version.
  2. Identical Control group and the test Group – It is important that we keep the test group and the control group identical. For example, we need to know whether giving an offer to a particular age group can bring more conversion. For this we identify a particular product for a particular age group. For the test group we give a discount offer also. In order to get the perfect overview of comparison we have to have an identical control group. That is the control group should have visitors of same age group for the same product as that we set for the test group. This can be done by defining the segment and then allocating visitors in control group and the test group.
  3. Testing the same variable – A/B testing is done to identify a specific change in the web page is bringing changes in the behaviour of visitor. So it is important that we test the same variable. For example we are doing A/B test on landing pages, both the test group and control group should be analysed for the two different versions of landing page, and not any other variable.
  4. Testing at the same period – It is important that we tract the two versions in the same period of time. The demand for the product or content we want people to change varies with time. The user behaviour is also changing. There are products that sell at a particular time or season. Sometimes we have to change the strategies depending on the completion. All these factors make it important to analyse the control group and the test group at the same period.
  5. Traceability – A/B tests should be like that the results are timely tracked. The whole procedure of A/B testing should be thus planned and executed. It should be ensured that the control group are reached perfectly for the particular test. Suppose in testing email marketing it’s important to ensure that we send the emails to the control group. The behaviour cannot be tracked if the emails are not reached to the control group. Analyse the each variant, like conversion rates, purchases, downloads, clicks etc, which we have set for A/B testing for their performance. Proper tracking of the parameters helps to invest effectively in different campaigning.

 Image Credits: Google

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