Chapter 11: Causality and Optimization

Marketing Mix Modeling and Optimization

Introduction

Half the money I spend on advertising is wasted; the trouble is I don’t know which half.

Over the last century, various versions of this quote have been attributed to John Wanamaker, Henry Ford, and William Procter, among others. Yet, 100 years after these marketing pioneers, in this day and age of big data and advanced analytics, the quote still rings true among marketing executives. The ideal composition of advertising and marketing efforts remains the industry’s Holy Grail. Certainly, there are many advertising agencies and market research firms that promote their proprietary methodology in pursuit of the optimum allocation of marketing resources. Also, there have been decades of research in marketing science on this topic. Yet, despite all commercial and academic efforts, there is a remarkable lack of universally accepted methods for marketing mix modeling and optimization. As a result, the current practice remains “more art than science.”

We speculate that the lack of a well-established marketing mix methodology has little to do with the domain itself. Rather, it reflects the fact that marketing is yet another domain that frequently has to rely on non-experimental data for decision support. As such, marketing mix optimization is a rather prototypical problem that mirrors the challenges of many other fields.

What is perhaps unique to marketing is the large number of instruments, i.e., the wide range of advertising channels and promotions, that can be utilized as individual levers in reaching and convincing consumers. Moreover, many marketing instruments can be easily quantified in terms of cost. Hence, the marketing domain lends itself as a teaching example for this chapter.

Marketing Mix Modeling Workflow

pageBackground, Challenges, and ObjectivespageMachine LearningpageTotal Effect vs. Direct EffectpageDirect Effects AnalysispageCosts and ResourcespageTarget Optimization