Multivariate Testing

Multivariate Testing (MVT) is an analytics tool used to compare multiple variables (or elements) in a single test (or experiment). The sole purpose of a multivariate testing tool is to determine the best combination of elements to understand its impact on the marketing campaign.

Marketers use this tool for analyzing the traffic driven to a certain website. Here, they try to combine different site elements such as color, content, and design. Later, they find a perfect match that can drive the landing page's traffic. For instance, if the color changes to green along with the site palette colors and font, it can increase the site traffic by 20%.

• Multivariate testing is a business analytical technique that allows businesses to compare multiple elements and improve a website's traffic. 
• Statistician Sir Ronald Fisher first mentioned this testing in 1935 in the book The Design of Experiments. In 2007, it got a boost in marketing.
• The process of the MVT approach includes defining goals, selecting elements, using tools to create combinations, and lastly, implementing them.
• However, it differs from A/B testing as the process is slightly complex. In contrast, the latter involves only two elements for quicker comparison.

Multivariate testing in marketing is an effective way of including many elements in a single experiment. It is like a permutation and combination test to get the best result. Here, the user involves several variables likewise to save time. Also, users can utilize multivariate testing tools to execute the test. As a result, it becomes easy for marketers to understand the effect of these elements on the campaign. In short, it works as a testing game where many players can participate at the same time. The major application of multivariate testing is visible in online marketing campaigns.

The origin of multivariate testing in statistics dates back to the mid-20th century. At that time, in 1935, statistician Sir Ronald Fisher first mentioned the context in 'The Design of Experiments.' Later, many other similar persons contributed to this field. However, a major application of this topic in marketing was put forth by authors Johannes Ledolter and Arthur J. Swersey in 2007.

Different types of multivariate analysis tools are useful in such campaigns. It includes full factorial, partial factorial, and Taguchi testing. As the name suggests, full factorial testing involves equal distribution of website traffic to several elements. For example, by using this method, all six combinations will receive 1/6th of the total traffic. In contrast, the partial factorial method only considers a fraction of site traffic. It is suitable for low-traffic websites.

Lastly, the Taguchi technique is an old yet faster testing tool. In marketing, most of the MVT tools use this technique for quicker results. A few include Adobe Target multivariate testing, Google Optimize, Kameleon, Optimizely multivariate testing, and others. It detects the best component on the site that can surge the traffic.

As discussed above, marketers use various multivariate testing tools to perform this technique. However, there are certain steps involved to implement this testing in any campaign. Let us look at them:

#1 - Identifying And Defining The Objectives

The foremost rule of implementing a multivariate method is to define the objectives that must be achieved. For example, some firms aim to increase site traffic, engagement, or higher sales. Based on it, the business can further select the variables.

#2 - Selection Of Variables (Elements)

After defining the hypothesis, the next step is to select the components from the website for combinations. The marketers can choose the sample size to be more than 2 to unlimited. For instance, a firm can have a set of 8 elements (like color, font, text size, background, alignment, and others) for testing.

#3 - Creating Different Combinations

As the sample size gets decided, the following stage is combination. Here, businesses can use testing tools to create possible combinations out of the elements. For instance, if five elements have around three variations, the Adobe Target multivariate testing can create combinations from up to 20 audiences.

#4 - Distribution Of Traffic

From the above-created combinations, the traffic distribution to each is also essential. Thus, with the help of the different MVT types, marketers can choose to equally or partially distribute site traffic to all.

#5 - Data Analysis

After implementing the multivariate technique, the next step is to analyze the final observations. It helps in determining the best combination that can drive the website's traffic. Also, firms can see whether predefined objectives and hypotheses are achieved or not.

#6 - Implementation

With the above results obtained, the marketers can implement the successful or winning combination.

Let us look at the examples of multivariate testing techniques to comprehend the concept better:

Example #1

Suppose Samuel is an employee working in the marketing team of a famous clothing brand. He has been in this corporate position for more than three years. However, in the past months, the sales from the site traffic have reduced. As a result, the management was given the responsibility to Samuel for increasing the sales rate. He went through the website and was skeptical about the interface. Therefore, he decided to implement MVT.

Samuel then analyzed the website and noticed a current traffic of 5%. Also, the font, color, and shopping category were severely messed up. Besides, the payment option failed to show any coupons. Hence, with the MVT approach, Samuel took five elements, namely font, color, text size, contents, and option to create multiple combinations. He then added these variables to a multivariate testing tool and received 20 matches. He later analyzed all of them and picked the best of all.

After analysis, he corrected the payment and categories option and saw a surge of 10% in traffic. Likewise, the clicks also resulted in effective sales for the business.

Example #2

According to the latest news updates as of October 2023, the chief marketing officer (CMO) of Domino’s, Sarah Barron, mentioned the various testing methods in the Festival of Marketing held this month. She also mentioned how the application of multivariate testing has helped increase sales value. In addition, this method has also enhanced the customer experience by a larger fold. However, she does not suggest a complicated strategy like it behaves to be. Instead, Domino's strives to provide enough value for the penny spent by customers.

Following are the advantages and disadvantages of multivariate testing in the digital arena. Let us look at them:

Although multivariate and A/B testing have similar functions to perform, they have many differences during implementation. Let us look at them:

This article has been a guide to what is Multivariate Testing. We compare it with A/B testing, explain its examples, how to do it, advantages, and disadvantages. You may also find some useful articles here -

• State Space Model
• Mahalanobis Distance
• Bivariate Analysis