Editor's note: Michael Lieberman is the founder and president of Multivariate Solutions, a New York-based research consulting firm. He can be reached at michael@mvsolution.com.

I like to say that there is nothing new under the sun statistically speaking. Almost all the math in common multivariate analyses were proven more than a century ago. Most new products are a mélange of existing techniques with a simple twist.

Every so often, however, a new technique emerges that leverages prevalent methodologies with growing bandwidth of personal and cloud computing. In this piece I will introduce importance weighting, a useful technique in marketing that allows marketers to assign varying levels of importance or priority to different factors or elements within their marketing strategies. I will outline one that is gaining popularity in the marketing research world – Johnson’s relative weights analysis.

In 2000, Jeff Johnson wrote a technical paper introducing relative importance weights. Prior to that, researchers relied on traditional statistics (e.g., correlations, standardized regression weights) that are known to yield affected information concerning variable importance – especially when predictor variables are highly correlated with one another. In the context of market research, relative weights refer to the importance or influence of different attributes or factors on consumer preferences or purchasing decisions. Common uses for relative weights are:

In Johnson’s relative weights analysis, the focus is on determining the relative impact of each variable on the dependent variable, taking into account the influence of other variables in the model. The relative weights of the variables are calculated based on their unique contribution to the outcome variable while considering the presence of other variables in the model.

The Johnson method utilizes not only standardized outcomes from regressi...