Clickstream Data as a Source to Uncover con-Sumer Shopping Types in a Large-Scale Online Setting

Today’s technological advancements enable marketers to track consumer touch points in great detail. We analyze the users’ sequence of visits to a website (off-site clickstreams) as it gives insights into the overall decision-making process of consumers on their path to purchase or non-purchase. We show that search patterns based on the online advertising channel choice discloses specific shopping types. Operationalizing search behavior as navigational or informational based on information retrieval research and the level of consumer involvement; we use k-means clustering to categorize search patterns as Buying, Searching, Browsing or Bouncing. Our typology is based on a unique and comprehensive dataset from a leading European fashion e-commerce company and includes a total of almost 30 million clickstream journeys based on over 80 million lines of clicks from 11 advertising channels. This paper is the first to link the off-site online channel journey of consumers with their underlying search patterns to establish a typology of search types in a large-scale setting. Understanding the nature and characteristics of online consumer search and its implications for consideration and choice is also of high importance from a managerial perspective as our results offer e-marketers the ability to make inferences for automated marketing system decision-making.