A Probabilistic Approach to Maximize Cross-Selling Revenues of Financial Products

Customer oriented approach and developing analysis for customers have become more important as the competition became a global issue, especially after technological advancements. Companies aim to sell more products to new customers and also to their current customers while keeping them in the portfolio and making sure that they are happy. Cross-selling of products and services to their customers is almost as important as gaining new customers. In this paper, a probabilistic and integrated method of cross-selling financial products is proposed. The proposed method first segments the customers based on the selected criteria and then calculates the probability of buying each product using product and customer relationship matrixes. Then, the expected yield for each group of customers and offer is calculated, and the best cross-selling strategy is determined. The proposed methodology is applied to a Turkish bank that aims to sell financial products through cross-selling. The results show that the methodology successfully determines the product order to be used in cross-selling in an effort to increase the success rate in the selling process and expected revenue

Inventory Model with Partial Backordering When Backordered Customers Delay Purchase after Stockout-Restoration

Many inventory models with partial backordering assume that the backordered demand must be filled instantly after stockout restoration. In practice, however, the backordered customers may successively revisit the store because of the purchase delay behavior, producing a limited backorder demand rate and resulting in an extra inventory holding cost. Hence, in this paper we formulate the inventory model with partial backordering considering the purchase delay of the backordered customers and assuming that the backorder demand rate is proportional to the remaining backordered demand. Particularly, we model the problem by introducing a new inventory cost component of holding the backordered items, which has not been considered in the existing models. We propose an algorithm with a two-layer structure based on Lipschitz Optimization (LO) to minimize the total inventory cost. Numerical experiments show that the proposed algorithm outperforms two benchmarks in both optimality and efficiency. We also observe that the earlier the backordered customer revisits the store, the smaller the inventory cost and the fill rate are, but the longer the order cycle is. In addition, if the backordered customers revisit the store without too much delay, the basic EOQ with partial backordering approximates our model very well