Mapping the Customer Journey: Lessons Learned from Graph-Based Online Attribution Modeling

Abstract

Advertisers employ various channels to reach customers over the Internet, who often get in touch with multiple channels along their “customer journey.” However, evaluating the degree to which each channel contributes to marketing success and the ways in which channels influence one another remains challenging. Although advanced attribution models have been introduced in academia and practice alike, generalizable insights on channel effectiveness in multichannel settings, and on the interplay of channels, are still lacking. In response, the authors introduce a novel attribution framework reflecting the sequential nature of customer paths as first- and higher-order Markov walks. Applying this framework to four large customer-level data sets from various industries, each entailing at least seven distinct online channels, allows for deriving empirical generalizations and industry-related insights. The results show substantial differences from currently applied heuristics such as last click wins, confirming and refining previous research on singular data sets. Moreover, the authors identify idiosyncratic channel preferences (carryover) and interaction effects both within and across channel categories (spillover). In this way, the study supports advertisers’ development of integrated online marketing strategies.