Multichannel Data-Driven Attribution Models: A Review and Research Agenda

Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.