Baumann, AnnikaHaupt, JohannesGebert, FabianLessmann, Stefan2019-08-142019-08-1420192019http://dx.doi.org/10.1007/s12599-018-0528-2https://dl.gi.de/handle/20.500.12116/24461The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of customer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping patterns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In particular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value.Behavioral targetingClickstream datae-CommercePredictive analyticsPrivacyText/Journal Article10.1007/s12599-018-0528-21867-0202