Marketing Mix Modeling for Marketers

In the first article of this series, I outlined the common machine learning use cases that a marketer can consider for personalizing the experience to the consumer. In the subsequent articles, segmentation, churn , Customer Life Time Value (CLTV) and recommendation algorithms were explained in detail. In this article, I touch up on another common use case that that marketers can consider for personalization — Marketing Mix Modeling

Marketing Mix Modeling

Big Idea: Behaviorally-targeted advertising is 2.7 times as effective as non-targeted advertising[1]

Earlier companies focused their marketing efforts on traditional channels such as newspaper, radio and TV. In the digital age, this trend has shifted to advertising via social media and other digital channels such as email and mobile. Marketers always wanted to maximize the Net Present Value (NPV) of their investments to increase the ROI for advertising spends rather than the spray and pray method and relying on guess work.

The Marketing Mix Modeling (MMM) enables marketers to identify campaigns that could bring in higher revenue, decrease marketing spend and help to better target the campaigns. One of the key use cases of Marketing Mix Modeling is to determine the appropriate marketing budget to spend for holiday season to get the best ROI. It also helps to identify the low impact campaigns that had made only a minimal contribution to revenue and generated high production costs (E.g. Does the costly TV ads…