Identifying innovators for the cross-selling of new products

With recent advances in information technology, most companies are amassing extensive customer databases. The wealth of information in these databases can be useful in identifying those customers most likely to purchase a new product and in predicting when this adoption may take place. This can assist database marketers in determining when individuals should be targeted for the promotion of a new product, which may increase the efficiency of manufacturing and distribution, and assure a faster return on investments. For this purpose, we propose a model that considers the timing of past purchases across multiple product categories and produces estimates of each customer's propensity of ever purchasing in a particular product category and of the timing of their purchases. The model is designed to help managers identify the best prospects for a new offer in one of multiple categories based on generalizations obtained from past offers. The proposed model also provides projections of aggregate penetration for new brands within the database, based on sample estimates.