Helping Firms Reduce Complexity in Multichannel Online Data: A New Taxonomy-Based Approach for Customer Journeys

Retailers can choose from a plethora of online marketing channels to reach consumers on the Internet, and potential customers often use a vast range of channels during their customer journey. However, increasing complexity and sparse data continue to challenge retailers. This study proposes a taxonomy-based approach to help retailers better understand how channel usage along the customer journey facilitates inferences about the underlying purchase decision processes. A test of this approach with a large, clickstream data set uses a proportional hazard model with time varying covariates. Classifying online marketing channels along the dimensions of contact origin and brand usage uncovers several meaningful interaction effects between contacts across channel types. The proposed taxonomy also significantly improves model fit and outperforms alternative specifications. The results thus can help retailers gain a better understanding of customers' decision-making progress in online, multichannel environments and optimize their channel structures. (C) 2015 New York University. Published by Elsevier Inc. All rights reserved.