Marketers have hit a wall in their ability to accurately measure ROI across their media mix—due in large part to limited access to consumer data by walled gardens such as Google, Facebook, and Amazon, and to privacy constraints. Author Gian Fulgoni suggests two paths analysts can pursue to address this.
Marketing-mix-models are designed to measure the impact on brand sales of each of the key elements of a brand’s marketing mix. Through statistical analyses, the modeling uses sales and marketing time-series data to estimate the impact of various marketing tactics on sales. Marketers have relied on these models to determine their marketing budgets since the 1980s, when supermarket retailers first began using point-of-sales UPC scanners, which provided the granular sales data they needed to run the models.
“How can researchers build actionable marketing-mix models for accurately measuring marketing ROI without sufficient data at the individual consumer level?,” Fulgoni asked.
Gian Fulgoni wrote the Numbers, Please column since the JAR introduced it in December 2013. His last column appeared in the December 2018 edition, in which Editor-in-Chief John B. Ford described him as a “consistently provocative and timely voice both in the Journal as well as in the industry that we serve.” A cofounder and former CEO of comScore, Inc., he retired from comScore in 2017 and is now a partner at 4490 Ventures, a venture capital fund based in Milwaukee.