It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying clustering algorithms. Recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. This paper proposes a new micro targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. This micro-targeting method is more general than the previously considered direct grouping method. We empirically show that it significantly outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization. Index Terms: Customer segmentation, marketing application, personalization, micro targeting, customer profiles