A B Testing Definition

Cite this article as:"A B Testing Definition," in The Business Professor, updated February 24, 2019, last accessed October 29, 2020, https://thebusinessprofessor.com/lesson/a-b-testing-definition/.


A/B testing explained

A/B testing is a comparison method that is used to compare two versions of an app or webpage against each other to identify the version that performs best. The method is also known as “bucket testing” or “split testing”.

Digital marketing is one of the key elements for any company that aims to achieve its set goals or increase the sale of its products and services. Moreover, achieving such goals requires a series of strategies, one of which is A/B testing.

The A/B testing involves developing and introduction and comparing two versions of same elements to determine the best element.  The test helps us to appropriately use email marketing strategy, or increase the efficiency and effectiveness of a landing page. When an individual detects that a page is facing some challenges, it is required to take appropriate action to improve the click throughs and opening rates.

A Little More on A/B Testing

Technically, A/B testing can be termed as a technique used by the analysts to compare the effectiveness of different elements that serve the same purpose. The is based on using two groups of users where one element is tested in one group and the other element on another group. After that, the evaluation is carried out using the numerical and statistical methods to determine the most convenient element. Generally, the parameters to be evaluated are established before testing take place. This comprise of determining the methods of evaluation, the time required for the evaluation process and the objectives and goals of evaluation.  In addition, it is important to recognize that this type of evaluation involve modifying only single element or one variable since such modifications does not influence other variables that may affect the result. Besides when the test comprises of several variables at a time, it gives rise to another type of test known as multivariate tests which is more complex that A/B test.

The use of this of multivariate tests helps to detect various problems on the website and further explore the causes of the difficulties such as few users subscribing, high rebound rate, or few people conversing on the website, the act that is attributed problems of design, overload of the information and inappropriate font size among other issues. These problems have direct influence on the revenue of the company. Therefore, it is important for the company to carry out continuous testing using A/B testing to increase the bandwidth and allow higher percentage of communication.

A/B testing method

A/B testing methods is based creating and evaluating two versions of the same elements that the company is expected to introduce into the market. For example, the company may intend to introduce blue and yellow CTA buttons. The method involves the evaluation of each version to determine the effective version that work best.  Contrarily, making many variations has considerable benefits since it keeps many users connected and it does not have any negative impacts on the users. many variations also increase the revenues to the company because it increases the number of subscribers to the link.

The effective use of A/B model is based on focusing the attention on the elements that affect the rate of opening an email, and the clicks that made by the users on the landing page.  Some of the elements tested in the A/B test include;

  • ¬†¬†¬†¬†¬†¬†¬†¬†Colors, sizes, words, and location of the CTAs
  • ¬†¬†¬†¬†¬†¬†¬†¬†The titles and the bodies of the product description
  • ¬†¬†¬†¬†¬†¬†¬†¬†The form extension and the type of field used
  • ¬†¬†¬†¬†¬†¬†¬†¬†The visual structure or the design of the webpage
  • ¬†¬†¬†¬†¬†¬†¬†¬†The ways of presenting the promotional offers and the prices of the products.
  • ¬†¬†¬†¬†¬†¬†¬†¬†The presentation of the elements of images such as content, purposes, quantity and location of the pages and landings of the products.
  • ¬†¬†¬†¬†¬†¬†¬†¬†The quantity of texts on the blog post or web page.

Any change that occurs to the variables under test are useful, and therefore, I recommend that at the start of the test, the analyst should try to differentiate variables as much as possible to arrive at conclusive decision on what the direction the test should take.  For example, when the company has the website that has both yellow and green colors and intends to optimize the Ads, it should not start the process by carrying out A/B test through switching between the yellow tones. Rather the process should begin by testing the color that work best between yellow and green.

Secondly, I would recommend that the analysist should not put a limit of the number of tests he performs. The analyst can improve the result by carrying out many tests to determine with high level of certainty the best element.  The analysist should not base his conclusion on the first test. Rather it should be based on critical analysis of various successive tests. In additional before deciding on the element, the analysist should ask questions such as, is the test effectively done? Has the test managed to improve the website? After confirming these questions, the analyst can go on make conclusion.

Besides, during the process, the analysts observe inquisitive behavior trends. Appropriate observation of A/B trend behavior helps the analyst to determine the improvements in the tests. The method is also a quantitative approach that measures and improve understanding the behaviors patterns and help in developing solutions to problems facing the effectiveness.  Lastly, the analysts should not alter the data and the results since they are the key elements for improving the platform.

References for A/B Testing

Academic Research On A/B Testing

  • Measuring advertising effectiveness in destination marketing strategies, Woodside, A. G. (1990). Journal of Travel Research, 29(2), 3-8. This paper presents in-depth examination of A/B model and its application in determining variables that affect marketing. ¬†According to Woodside, A. G. (1990), use of A/B test model provide an important evaluation and understanding the effectiveness of destination advertisement. ¬†For example, the use of A/B model and copy split tests especially, in the tourism industry helps the companies to evaluate and understand the trends in the industry. Therefore, it enables the company to design mechanisms that enable them to maximize their profits. On the other the copy split tests present critical evaluation of various group to develop a design the facilitate the achievement of company goals. The split test model is based on putting the data into three different groups. The model uses two groups for comparison and the other group for control experiment. ¬†The split mode tests and inform about the industry actions. The split model evaluation also helps to understand the factors that influence advertisement and make strategies that would help increase the revenues.
  • Experimental design on the front lines of marketing: Testing new ideas to increase direct mail sales, Bell, G. H., Ledolter, J., & Swersey, A. J. (2006). International Journal of Research in Marketing, 23(3), 309-319. This paper focuses on use of experiment for testing the effectiveness of advertising destinations. It further explores the model that can be used to detect the effectiveness of the model. Bell, Ledolter & Swersey, (2006), presents that testing new ideas increases the sales of new males, the act that is attributed to increased model testing. According to them, the current marketers embrace use complex experimental designs for testing advertisement and market strategies. The marketers often use factorial designs multivariable methods to evaluate the market trends. The models provide the marketers with the opportunity to increase the power, speed and profitability of their testing designs. An appropriately developed and managed experimental designs improves the understanding of the marketing professionals regarding the benefits of a specific marketing strategies.
  • Marketing universals: Consumers’ use of brand name, price, physical appearance, and retailer reputation as signals of product quality, Dawar, N., & Parker, P. (1994). The Journal of Marketing, 81-95. This paper presents the in-depth analysis of factors or variables considered by the customers in making their purchases. Consumers use various variables such as brand name, physical appearance, price and the reputation of the retailer in determining the quality of the products. In this regard, testing provide a critical analysis of these variables to determine the trends in the market and develop strategies geared towards meeting the needs of the consumers. Based on the marketing universals, the marketers used the evaluation models to determine the behaviors of their customers towards their products. According to Dawar, & Parker (1994), marketing refers to the consumer behaviors within a market segment towards a specific product category that is common across the cultures. ¬†Through use of various criteria for market universality, Dawar, & Parker evaluates how variables such as brand name, retailer reputation, physical appearance and the prices of the products affects the consumer behavior towards a particular product. The research samples presented in 38 countries revealed that the use of quality signals across cultures shows few differences in high priority consumer pigments. As such, it was revealed that some consumer behaviors are universal as other remain uncommon across cultures. Therefore, understanding the differences that occurs across cultures is important in designing marketing strategies.
  • Testing new direct marketing offerings: the interplay of management judgment and statistical models, Morwitz, V. G., & Schmittlein, D. C. (1998). Management Science, 44(5), 610-628. This paper explores the mechanics that can be used by the marketers to launch a new product in the market. According to Morwitz & Schmittlein (1998), the effective introduction of new product of service through direct marketing is preceded by the testing of that product or service. Such tests are conducted within the subset of the customers. This helps in making hard marketing decisions such as a go/no-go decision regarding the introduction of new product into the market. ¬†Moreover, the test is used to effectively direct the products to the potential market segments. More precisely the results of the test are used to determine whether a certain rental list of customer predictions should be rented, and also determine (for both rental and in-house lists) the customer segments that should receive the product or offering.
  • Pre-testing in questionnaire design: a review of the literature and suggestions for further research, Reynolds, N., Diamantopoulos, A., & Schlegelmilch, B. (1993). Market Research Society. Journal., 35(2), 1-11. The pre-test of the design enables the marketers to forecast the potential market segments and develop and developing marketing strategies geared towards meeting the needs of the customers in the segment markets.
  • The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations, MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). Journal of marketing research, 130-143. ¬†¬†This paper focuses on determining the impacts of attitudes towards the ads and the final effects on profitability. According to MacKenzie, ¬†Lutz, & Belch (1986), attitudes towards ads is one of the mediating variables that is assumed to influences the brand advertisement and purchasing behavior. As such, various models tend to depicts the relationship between brand-related responses and ad-related responses. ¬†MacKenzie, Lutz, & Belch provides critical analysis of structural of the models that affect the attitudes towards through use of two sets of data sourced from pretest setting. The results of the analysis revealed that ads have direct and indirect influence on the brand attitudes. The ads affect brand cognitions under a particular set of conditions in the within the pretest settings.
  • Testing the robustness of the job demands-resources model., Llorens, S., Bakker, A. B., Schaufeli, W., & Salanova, M. (2006). International Journal of Stress Management, 13(3), 378. ¬†¬†This paper presents the elements that affects ads and should be taken into consideration when developing marketing policies Llorens, Bakker, ¬†Schaufeli & Salanova, (2006), use job demand resource model to provide critical evaluation of elements that affects the ads in the market. To deal with problems facing the ads in introducing or advertising for new products, Llorens, Bakker, ¬†Schaufeli & Salanova, present ways to minimize the errors that occurs on the ads. They revealed that the job demand resource model is based on a formula (1 – őĪ) √ó sigma¬≤. Using this formula helps the analysist to determine and minimizes the variance errors that might occurs inn the evaluation. However, their evaluation was based drawbacks because some figures were not correctly presented. The job demand model shows that the demand for jobs and resources give rise to two relatively independent processes which is employee motivation and health impairment. ¬†In addition, the structural equation used in the model provide some evidence for employee motivation and health impairment. Multigroup analysis using the model shows that although the strength of the relationships differed across countries, the structural paths of the model remained the same. In that regard they concluded that job demand-resource model basic structure is maintained even irrespective of the nationality and the occupation in which they are applied. This does also does not rely on the method used to collect data and assess key variables.
  • Testing for a unit root in time series regression, Phillips, P. C., & Perron, P. (1988). Biometrika, 75(2), 335-346. This paper provides new models for detecting the availability of a unit root in time series model. The test by Phillips & Perron, (1988), provide different and new methods for determining the presence of unit root in time series model. ¬†The approach allows wide range weakly dependent data especially, the heterogeneously distributed data, because it is nonparametric with regard to nuisance parameters. the approach give room for models with time trend and fitted drift to be used in discriminating between the stationary and nonstationary unit roots. The limiting statistical distributers are obtained from the local alternatives and unit root null.
  • Effectiveness in sales interactions: a contingency framework, Weitz, B. A. (1981). The Journal of marketing, 85-103. ¬†¬†This article presents effective approach for moderating the interactions between the salesperson and customers. The effectiveness in the sales interactions is also assumed to be one of the elements that affects buying. The approach of effectiveness in sale interactions is based on the analysis of moderating influence of the salesperson, customer buying and salesperson-customer relationship. The model presents a contingency framework and the directions related to the model`s frameworks.
  • Corporate social responsibility and cause-related marketing: an overview, Br√łnn, P. S., & Vrioni, A. B. (2001). International journal of Advertising, 20(2), 207-22. This article evaluates the role of Corporate social responsibility in creating marketing awareness among the customers. According to the author, Corporate social responsibility is also one of the variables used by the companies for marketing their products through what is known as cause-related marketing. ¬†According to Br√łnn & Vrioni (2001), corporate social responsibilities aims at satisfying social needs and that form important part of marketing. However, it requires huge investments to yield measurable outcomes. The corporate social responsibility is used as a tool to increase the customer loyalty and build its image before the public. The corporate social responsibility campaigns depend on how the customers perceive the efforts of the company in providing their services to the public. Lastly, the response of the customers to CSRs depends on the level of skepticism. The customers with high level of skepticism tend to respond, and accept the CSR initiatives positively. On the other the customers with low level of skepticism tend to oppose and respond to the he CSR negatively.

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