This study explores the identification of nonlinear and time-varying effects in marketing mix models (MMM). It highlights the challenges of conflation in model selection and proposes a framework for simulating and estimating these effects using Gaussian processes. The study emphasizes the importance of accurately identifying the underlying response to optimize marketing spending.
The research provides insights into the complexities of marketing effectiveness and offers practical solutions for improving model accuracy. By addressing the issue of conflation, the study aims to enhance the decision-making process in marketing strategies.
Member Only Access
Is there a better way to measure the digital behavior of consumers considering a complex or expensive purchase and improve outcomes going forward? Measurement company Arrivalist shared techniques they have used successfully, illustrated with six case studies. Their work has application for both considered and impulse purchase brands.
Member Only Access
To improve data quality, the marketing and advertising industry could establish a “Principles of Data Quality” disclosure; create a roster of items to ask of data suppliers; request transparency—data firm disclosures—including consumer authentication procedures, multiple data source descriptions, model target techniques and testing results, recency of data, integration techniques, data labeling, and top-line descriptions for all of the above; create best practice standards.
Member Only Access
Using data to microtarget messages to Chobani customers produced a positive sales impact, according to this case study. The authors show how loyalty card data, linked to household address and then to a digital address was used to identify key audience segments to whom messages were specifically directed – with implications for future efforts.
Member Only Access
