What is an A-B Split?

The A-B split is a test method where specific markets campaigns or elements performance is monitored. In this method of marketing, users are randomly divided into two groups, one representing a control group, and the other one representing a test group. This is done so as to be able to monitor specific marketing element campaigns performance. The reason for implementing the A-B test is to help find out an effective variable that can improve the campaigns element response.

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How does an A-B Split Work?

For many years, A-B split has been applied in business researches and campaigns. Examples of such include direct mail campaigns, interactive media effectiveness testing like email blasts and banner adverts. The A-B split is used to directly compare a change against the existing experience. This way, you will be able to ask questions that focus on the changes and afterward collect information on the impact change. Contrary to other surveys or focus group where a hypothesis is applied, the A-B split testing is done using real people, in a real buying situation. It is a situation where people also make real decisions. Whatever these people tell you, is always what they think you should know. In other words, the A-B split test will put you in a position where you will get to hear from your real customers and not from surveys.

How A-B Test Works

This process is implemented using real people who are also deeply involved in what you are testing. The users, in this case, are usually your customers. Note that since you cannot carry out this process on all your customers, you will only sample out a number of them to participate in the test. However, to ensure that you have a statistical figure which is significant, you will have to pick enough customers for this purpose. This is because a good number of participants will represent good customer coverage, hence increasing the likelihood of your accurate results recording. When doing sample selection, ensure that the selection process is done in a random manner. The purpose of random selection is to ensure that there are no accuracy threats to your end results. In other words, random selection will guarantee you credible results because your selection is natural, and it is not in any way influenced. To achieve a successful random selection, there are two important components you need to implement. These components will help you ensure that your A-B split test reflects real customer behavior: 

Customers should not be aware of the split

When the customers happen to know that they are part of the research test, it will definitely influence their behavior. For accurate results, it should be done naturally. You should apply the test to customers when on their normal daily purchasing routine a time when they are making real decisions. 

Customers are randomly split

The two or so groups of customers that will form part of the research test, should be randomly split. Meaning there should be no criteria for selecting the target group. This will ensure that the group is similar so that you do not experience different results which in this case will not be credible.

A-B Split Example

Randomly selected the customers and then split them into two groups. One group to represent control, and the other one to represent variation. After customers have been split into these two groups, different market elements should be sent to them at the same time. You will then monitor their behavior (reaction) and from it, you can tell the elements performance. For instance, a mail may be sent to customers to ask them to take action on a certain element. For example, the email may be to ask subscribers to enroll for a course within 5 days at discounted rates. When sending the mail, do not include a call to action to the half of the target group, or rather mentioned about the discount being offered when one enrolls for the course. This means, among your target customers, half will get the message on call to action while the other half will not. The next thing would be for you to check how each group responds to the mail. From the test, you will be able to know whether or not the mail had an influence on the group in any way. You will monitor the number of those who responded by enrolling for the course because of the discounted offer, against those who did not get the offer. You should be able to tell from the response whether the discount offer had an influence on the group and if the offer is worth it.

Uses of A-B Split

• It is used to improve marketing strategies. Based on customers behavior from the A-B split test, you will be able to come up with decisions regarding the best offer, headline or ad copy, and so on.
• A-B split test can be used to eliminate assumptions from your digital marketing campaign options. This method works on validated significant data which makes it more accurate. It guarantees accurate results that can confidently be used to generate more leads during marketing campaigns.

Advantages of Using A-B Split

• A-B split is accurate and therefore does not work on assumptions. This is because the sample used in the A-B test is based on random selection. This means that the outcome derived from the randomly selected sample (customers) are not influenced in any way a situation that guarantees credible results. On a lighter note, we can say that the A-B split test eliminates the I think concept, and confidently replaces it with the I know concept which illustrates certainty about the outcome.
• The A-B split test is less costly, hence, it can be done on a continuous basis. This enables firms to be up to date with the emerging market trends, as they can frequently update their online tools to match the changes. They can also easily further develop the firms online business to meet their customers needs.

Limitations of Using A-B Split

• The A-B test is complicated when it comes to testing multiple steps at a go. For this reason, there is a possibility that it will lead to incredible results, which is not good for your business. To prevent this, it is important that you find out where the test will be effective for the test. This will eliminate the possibility of incredible results.
• Another A-B split test limitation is that it is highly open to selection effect (this refers to biased sample selection). If the selection does not include random selection, then there is a likelihood that your test data will come from self-selected customers. This will lead to invalid results which are likely to have a negative effect on your business if you happen to rely on them.

Conclusion

It is true that A-B split test can assist you to get credible results that will enable you to quickly grasp your customers needs. This means it puts you in a position where you can easily understand their needs and attend to them is a more satisfying manner However, note that everything has its own weak side. Therefore, It is important for any business person with an online business, to avoid biased selection when in the process of choosing a test sample. This is the only way you will be able to prevent credibility associated threats to your test results.

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