Among marketing practitioners and researchers, there is growing interest in the collection and analysis of detailed process data that track the various decisions and reactions of digital consumers. This thesis develops quantitative methods to analyze these process data and providing guidelines for marketing actions. Specifically, it focuses on digital consumers' processes of online content consumption (Essay I) and product search (Essay Il).

The first essay proposes in-consumption social listening, an approach to analyze live social media activities that are generated by consumers during product consumption to extract information on live consumption experience. The approach is demonstrated in the context of online movie watching where viewers can react to movie content with live commen...[ Read more ]

Among marketing practitioners and researchers, there is growing interest in the collection and analysis of detailed process data that track the various decisions and reactions of digital consumers. This thesis develops quantitative methods to analyze these process data and providing guidelines for marketing actions. Specifically, it focuses on digital consumers' processes of online content consumption (Essay I) and product search (Essay Il).

The first essay proposes in-consumption social listening, an approach to analyze live social media activities that are generated by consumers during product consumption to extract information on live consumption experience. The approach is demonstrated in the context of online movie watching where viewers can react to movie content with live comments that are visible to other viewers. The essay proposes a novel measure, moment-to-moment synchronicity (MTMS), to capture consumers' in-consumption engagement. MTMS refers to the synchronicity between temporal variations in the volume of live comments and those in movie content mined from unstructured video, audio, and text data. I find that MTMS has a significant impact on viewers' post-consumption appreciation of movies, and it can be evaluated at finer level to identify engaging content. Finally, we discuss the relation between MTMS and existing in-consumption measures and the value of integrating supply-side content information into in-consumption analysis.

The second essay proposes a partial information search model to capture consumer product search process (e.g., clicking into product pages) in online retailers with a realistic information structure about products. The model can accommodate differential product attribute sensitivities during search and choice and incorporate heteroscedasticity in search utilities across consumers and products, to allow for richer behaviors. I first carefully document the breadth and depth of limited product information availability at Amazon.com. I then apply the model to search and sales data from its microwave category. The analyses suggest that displaying more product attribute information increases consumer welfare by saving search efforts, leads to more differentiated and elastic market demand, and affects competitive relations between brands.