A Research Framework for Analyzing Customer Journeys in the Digital Age: An Abstract

Abstract

Analyzing multiple customer contact points that span the so-called customer journey is an essential prerequisite for the targeted implementation of advertising (Qaqish 2018). Various procedural models seek to provide methods for conducting such analyses (e.g., Rosenbaum et al. 2017); they differ in terms of how many contact points they analyze and how much importance they assign to contact points that take place online (Ament 2017; Brynjolfsson et al. 2013). Yet in previous approaches, distinct online channels rarely are differentiated (Barwitz and Maas 2018). Against this background, it seems necessary to examine closely the various online contact points between potential consumers and an advertising firm. This study proposes a methodology to determine the significance of each online advertising channel, in isolation and in combination with other (online) channels. According to this, marketing decision makers can calculate direct and indirect value contributions of their advertising channels. Study starts with a literature review, to identify and describe various theoretical approaches to explaining purchase decisions. After that, study offers criticisms of extant approaches, methods, and attribution models, with a view to their potential applications to online marketing. This critique establishes a foundation for pursuing the theoretical research goals and designing the proposed model. This model aims to make a significant contribution to the analyses of the customer journey, particularly in a context of increasing digitization. To build on the deductively derived model, this research will attempt to confirm the proposed model, using empirical investigations. The empirical procedure ensures comprehensive coverage of concrete application scenarios within the model development framework, as well as careful considerations of pragmatic relevance. Empirical investigations base on real data, gathered by a German service company that sells educational services almost exclusively over the Internet. Spanning a period of almost 10 years, the data set refers to more than 60,000 inquiries from interested persons who have requested information, reflecting their interest in the company’s offerings. Data set also contains information about approximately 10,000 customers who previously used or currently use the company’s services. Thus, it reveals details about when each customer has purchased each specific service. Based on the theoretical, deductive considerations and the initial empirical investigations, it is reasonable to assume that a more differentiated analysis of online advertising channels is worthwhile in the context of understanding customer journeys. By identifying channels that produce particularly high or low purchase probabilities, whether in isolation or in combination with other channels, marketing decision makers gain valuable information about which channels they should promote and which they can safely discontinue.