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We present a strategy to forecast and assess customer behavior in the field of e-commerce. Starting point is the assignment of all actors in a market to an exhaustive set of potentially accessible users. Usually, only a small part of this set belongs to real customers in the context of commercial transactions. The user information generated by arbitrary online-interaction is much more voluminous than the customer data collected. Typical customer profiles – regular customers as well as change customers and churners – can be generalized to corresponding user profiles. However customer data is much better structured than the naturally granular, heterogeneous and often incomplete data of arbitrary users. These users cannot be connected in a simple way with standardized monetary indicators like the customer lifetime value (CLV). In consequence an effective exploitation of unstructured user data requires explorative and descriptive methods of correction and classification as well as statistical limitations arising from the heterogeneity of the data. As a clear focus the mean- and short-term user identification in the context of an effective classification is discussed. The main goal is to improve the representation and description of customer journeys together with an effect analysis of marketing activities. The traceable “journey” of all users hardly differs. Nevertheless, it might be the main base for decisions about general marketing campaigns and individual offers. Robust inferences on the base of many short-term user steps are an important goal. In this contribution, a case study of an online short break internet platform serves as an example.

How can predictive analysis of customer data be used to identity the decrease or increase of customer activity over time during the product search and the booking process? Another question that arises in this context is how many data is necessary to carry out a reliable analysis? We present a case study from an European travel website based on a session history of 15 months only here only a small part of the customers are known to the website and where a huge number of users regularly delete their browser histories. We focus on the analysis of the users' web traffic while visiting the booking platform. Based on a classification algorithm we identify the non-contractual customers who are mostly anonymous to the website, and investigate the short-term customer activity with respect to customer churn on a more microscopic level.