Marketers use multivariate advertising to determine the best way of advertising a product to consumers, based on a wide range of consumer characteristics such as age and income. This complex advertising technique employs a wide range of tests and stands in stark opposition to the much simpler Taguchi method of testing marketing campgains. Marketers in the multivariate testing process most commonly use experiments in ad preference.
Multivariate testing constitutes the process of advertising through the multivariate method. Basically, marketers use this form of testing to create a number of advertisements and conduct tests on those ads. Based on variables such as consumer age, income, gender, background, industry and interests, these tests help determine the best method of advertising a product for each market. The multivariate method differs greatly from the A/B, or Taguchi Method, which involves showing groups of consumers an "A" ad and a "B" ad and generating info on these two options based on a single set of questions and information.
Multivariate advertising methods constitute the way in which marketers collect and analyze data used in multivariate testing. Writing for marketing research firm Decision Analyst, Michael Richarme identifies 11 multivariate advertising methods. These methods are multiple regression analysis, logistic regression analysis, discriminant analysis, multivariate analysis of variance, factor analysis, cluster analysis, multidimensional scaling, correspondence analysis, cojoint analysis, canonical variation and structural equation modeling. The method used in multivariate advertising directly affects the techniques employed by researchers.
Multivariate advertising techniques depend upon the data collection and analysis method used by a marketing campaign. Regression analysis, for instance, maintains one dependent variable and a number of independent variables. If using regression analysis for advertising coffee, you might use the amount of coffee consumers drink each week as the dependent variable and use consumer age, industry and gender independent variables. Analysts then search for the optimal method of advertising based on the data produced. G.C. Beri, author of the book “Marketing Research,” warns against the use of one method or set of techniques above the others, as it may limit the vision, scope and effectiveness of multivariate advertising.
How Multivariate Advertising Works
Basically, marketers convert information to numbers and create charts based on these numbers. A straight line, or linearity, on such a chart indicates a solid trend that connects a series of varies to a constant or dependent value. Linearity allows marketers to understand how consumer habits relate to the consumer, and therefore how to create an ad that speaks directly to a consumer base. Specificity in ads revolves around giving the consumer what works for them or convinces them to buy. Multivariate testing helps marketers arrive at this information.
Advertising for the Web
Multivariate advertising proves especially prevalent on the Internet for two reasons. First, the Internet allows marketers to essentially access every consumer demographic in existence by placing ads on a wide variety of sites in a number of countries. Second, computer programs can take information directly from web ads and analyze it using any and all multivariate methods, producing enormous and complex sets of data and even providing analysis. Web-based multivariate advertising techniques often consist of little more than purchasing the right software and placing a set of ads across multiple markets, or hiring a web marketing company to conduct all this research.
- Visibility Magazine; Conversion Optimization - Does Multivariate Testing Increase Sales?; James Lisi; 2011
- Decision Analyst; Eleven Multivariate Analysis Techniques; Michael Richarme; 2001
- “Marketing Research”; G.C. Beri; 2008
- “Essentials of Health Care Marketing”; Eric N. Berkowitz; 2011
- Marketing Experiments; Multivariable Testing; 2005
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