Advanced Applications of Network Analysis in Marketing Science

The world today is becoming a highly connected place. Firms, consumers and the devices they use are increasingly part of a complex, global network of connected entities. These networks represent a gold mine for marketing scholars that may be interested in developing a better understanding of consumer behavior, and for practitioners who are keen to discover new ways of gaining and retaining customers. This cumulative dissertation focuses on the use of network analysis to generate and analyze novel data in marketing. The work moves beyond the scope of traditional social network analysis to consider networks composed of different types of nodes at varying levels of granularity. The key aim is to demonstrate that network analysis can be used to investigate novel explanatory and outcome variables that hold contextual meaning and can deepen our understanding of the research question at hand. Essay 1 develops a predictive method based on product networks to identify customer projects in a retail setting. Essays 2 and 3 show the value of network analysis in new product development by recasting product ideas as networks of their constituent features. Essay 4 leverages network thinking to enable novel analyses of the fragmented and anonymized event data that will be generated in abundance by the Internet of Things. Ultimately, the essays showcase a selection of advanced applications of network analysis in marketing science that are arguably of value to scholars as well as practitioners, and likely to gain in relevance in the future.

Chinmay Kakatkar is passionate about using advanced analytics to help organizations extract value from data. He received his PhD from the Ludwig-Maximilian University of Munich. He previously obtained an MPhil from the University of Cambridge, and a BEng from Imperial College London.