Generating Consumer Insights from Big Data Clickstream Information and the Link with Transaction-Related Shopping Behavior

E-Commerce firms collect enormous amounts of information in their databases. Yet, only a fraction is used to improve business processes and decision-making, while many useful sources often remain underexplored. Therefore, we propose a new and interdisciplinary method to identify goals of consumers and develop an online shopping typology. We use k-means clustering and non-parametric analysis of variance tests to categorize search patterns as Buying, Searching, Browsing or Bouncing. Adding to purchase decision-making theory we propose that the use of off-site clickstream data—the sequence of consumers’ advertising channel clicks to a firm’s website—can significantly enhance the understanding of shopping motivation and transaction-related behavior, even before entering the website. To run our consumer data analytics we use a unique and extensive dataset from a large European apparel company with over 80 million clicks covering 11 online advertising channels. Our results show that consumers with higher goal-direction have significantly higher purchase propensities, and against our expectations consumers with higher levels of shopping involvement show higher return rates. Our conceptual approach and insights contribute to theory and practice alike such that it may help to improve real-time decision-making in marketing analytics to substantially enhance the customer experience online.