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A-B Split Definition
The A-B split is a test method where specific market’s 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 campaign’s element response.
A Little More on What is A-B Split
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 element’s 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 firm’s 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.
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.
Reference for “A-B Split”
- https://neilpatel.com › Blog › Conversion Rate Optimization
Academic Research on “A-B Split”
Destination advertising: assessing effectiveness with the split-run technique, Kaminski, P. F., Gordon, G. L., di Benedetto, C. A., & Schoenbachler, D. D. (1995). Journal of Travel & Tourism Marketing, 4(2), 1-21. This article assesses the effectiveness of split-run destination advertising technique. According to the authors, the state tourism divisions are currently made accountable for tourism revenues. Tourism analysts have recently proposed the use of split-run techniques, also known as advertising tracking to assess the effectiveness of tourism advertising. Such techniques, according to the authors, allow for the assessment of communication goals and could also be used to investigate the relationship between tourism advertising and its impacts. The paper, thus, examines the use of split-run assessment technique in tourism advertising at the state level. The findings of an empirical study are, thus, presented to evaluate the effects of state advertising.
Measuring advertising effectiveness in destination marketing strategies, Woodside, A. G. (1990). Journal of Travel Research, 29(2), 3-8. This paper investigates the advertising effectiveness of tourism destination marketing strategies. According to Woodside (1990), true experiments are necessary to test the effectiveness of destination advertising in causing visits. The paper describes the examples of A-B ad copy split tests in different industries as well as the values and challenges of advertising conversion research studies. The author then presents the A-B-C split as an effective tourism advertising research, using C as the control group that is not exposed to advertising.
Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments, Hrong-Tai Fai, A., & Cornelius, P. L. (1996). Journal of statistical computation and simulation, 54(4), 363-378. This research presents the f-statistics for multiple df hypotheses tests using one-moment and two-moment approximations which may be regarded as extensions of both Jeske and Harville (JH) and Giesbrecht and Burns (GB) methods. The author explores the restricted maximum likelihood (REML) estimates of variance components which had been developed by both JH and GB in reference to t-tests of single degree of freedom hypotheses. The research focuses on tests of hypotheses that consider the main-plot treatment factor in split-plot experiment. The findings reveal a Type I error.
The effects of split marketing on the physiology, behavior, and performance of finishing swine, Scroggs, L. V., Kattesh, H. G., Morrow, J. L., Stalder, K. J., Dailey, J. W., Roberts, M. P., … & Saxton, A. M. (2002). Journal of animal science, 80(2), 338-345. This study examined the effects of split marketing on the behavior, physiology, and performance of finishing swine. The authors used a sample of 120 8-weeks old barrows to examine the effect of split marketing. The subjects (pigs) were assigned based on weight in a randomized complete block design to 3 different treatments: SM (split-markets group), C (control group), and MC (Modified group). The animals were videotapes at different times: the initial 72 hours, 72 hours before SM treatment, and after treatment. A blood test was then conducted from each of the animals to determine the neutrophil:lymphocyte ratio, the CBG levels, cortisol, and plasma haptoglobin levels. The findings reveal that the levels of the Neutrophil:lymphocyte ratio, plasma haptoglobin, and CBG levels changed between the periods, but not between the treatments.
Integration of the profit-split transfer pricing method in the design of global supply chains with a focus on offshoring context, Hammami, R., & Frein, Y. (2014). Computers & Industrial Engineering, 76, 243-252. This study integrates the profit-split transfer pricing method in the global supply chain design with a focus on the context of offshoring. The authors present the optimization model for global supply chains design with an emphasis on the transfer pricing for tangible and intangible elements. A split transfer pricing method was adopted in reference to OECD guidelines and accepted by the fiscal authorities. The feasibility and solvability of the model are tested using experimental analyses and impacts of transfer pricing on supply chain are shown.