Mapping the Customer Journey: A Graph-Based Framework for Online Attribution Modeling

Abstract

Advertisers employ various channels to reach consumers over the Internet but often do not know to what degree each channel actually contributes to their marketing success. This attribution challenge is of great managerial interest, yet so far academic approaches have not found wide application in practice. The authors introduce a graph-based framework to analyze multichannel online customer path data as first- and higher-order Markov walks. According to a comprehensive set of criteria for attribution models, embracing both scientific rigor and practical applicability, four model variations are evaluated on four, large, real-world data sets from different industries. Results indicate substantial differences to existing heuristics such as “last click wins” as well as alternative attribution approaches. Applying the proposed framework to four different data sets enables generalizations and helps identify avenues for future research. The framework offers support to practitioners by facilitating objective budget allocation and allows for future applications such as real-time bidding.

Keywords: online advertising, attribution, marketing models, Markov models, multichannel