Evolving Practices in Marketing Attribution and Media Mix Modeling for Data-Driven Decision-Making
Year: 2024
Type: article
Abstract: As businesses now face a range of challenges, technology integration is a dire necessity. The integration of machine learning, real-time analytics, and granular data ingestion into media mix modeling (MMM) frameworks offers valuable online and offline insights. Also, first-touch, last-touch and other traditional models of attribution are getting replaced with probabilistic and algorithmic ones that are more sophisticated. The purpose of this study is to examine the critical role of evolving practices in marketing attribution and media mix modeling (MMM). It dwells on innovative practices that integrate attribution and MMM. It demonstrates that Shapely value-based attribution, Bayesian hierarchical modeling, and synthetic methods allow for effective evaluation of the trade-offs among model scalability, accuracy, and transparency. The study also contributes to solving the problems of channel saturation, loss of signals, and data privacy regulations. The findings suggest the importance of technology integration, data-driven decision-making and integrative practices that suit marketing analytics and dynamics. Thus, the efficiency of marketing can be achieved at a significant extent through strategic models, data-driven decision-making and other related practices.
Author: Sayee Phaneendhar Pasupuleti
Institution: The University of Texas at Dallas
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4412537804