eShopper modeling and simulation

The advent of e-commerce gives an opportunity to shift the paradigm of customer communication into a highly interactive mode. The new generation of commercial Web servers, such as the Blue Martini’s server, combines the collection of data on a customer behavior with real-time processing and dynamic tailoring of a feedback page. The new opportunities for direct product marketing and cross selling are arriving. The key problem is what kind of information do we need to achieve these goals, or in other words, how do we model the customer? The paper is devoted to customer modeling and simulation. The focus is on modeling an individual customer. The model is based on the customer’s transaction data, click stream data, and demographics. The model includes the hierarchical profile of a customer’s preferences to different types of products and brands; “consumption” models for the different types of products; the current focus, trends, and stochastic models for time intervals between purchases; product affinity models; and some generalized features, such as purchasing power, sensitivity to advertising, price sensitivity, etc. This type of model is used for predicting the date of the next visit, overall spending, and spending for different types of products and brands. For some type of stores (for example, a supermarket) and stable customers, it is possible to forecast the shopping lists rather accurately. The forecasting techniques are discussed. The forecasting results can be used for on-line direct marketing, customer retention, and inventory management. The customer model can also be used as a generative model for simulating the customer’s purchasing behavior in different situations and for estimating customer’s features.