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    Intelligent Classification-Based Methods in Customer Profitability Modeling
    (Springer-Verlag Berlin, 2015) Ekinci, Yeliz; Duman, Ekrem
    The expected profits from customers are important informations for the companies in giving acquisition/retention decisions and developing different strategies for different customer segments. Most of these decisions can be made through intelligent Customer Relationship Management (CRM) systems. We suggest embedding an intelligent Customer Profitability (CP) model in the CRM systems, in order to automatize the decisions that are based on CP values. Since one of the aims of CP analysis is to find out the most/least profitable customers, this paper proposes to evaluate the performances of the CP models based on the correct classification of customers into different profitability segments. Our study proposes predicting the segments of the customers directly with classification-based models and comparing the results with the traditional approach (value-based models) results. In this study, cost sensitive classification based models are used to predict the customer segments since misclassification of some segments are more important than others. For this aim, Classification and regression trees, Logistic regression and Chi-squared automatic interaction detector techniques are utilized. In order to compare the performance of the models, new performance measures are promoted, which are hit, capture and lift rates. It is seen that classification-based models outperform the previously used value-based models, which shows the proposed framework works out well.
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    Optimal ATM replenishment policies under demand uncertainty
    (Springer Heidelberg, 2021) Ekinci, Yeliz; Serban, Nicoleta; Duman, Ekrem
    The use of Automated Teller Machines (ATMs) has become increasingly popular throughout the world due to the widespread adoption of electronic financial transactions and better access to financial services in many countries. As the network of ATMs is becoming denser while the users are accessing them at a greater rate, the current financial institutions are faced with addressing inventory and replenishment optimal policies when managing a large number of ATMs. An excessive ATM replenishment will result in a large holding cost whereas an inadequate cash inventory will increase the frequency of the replenishments and the probability of stock-outs along with customer dissatisfaction. To facilitate informed decisions in ATM cash management, in this paper, we introduce an approach for optimal replenishment amounts to minimize the total costs of money holding and customer dissatisfaction by taking the replenishment costs into account including stock-outs. An important aspect of the replenishment strategy is that the future cash demands are not available at the time of planning. To account for uncertainties in unobserved future cash demands, we use prediction intervals instead of point predictions and solve the cash replenishment-planning problem using robust optimization with linear programming. We illustrate the application of the optimal ATM replenishment policy under future demand uncertainties using data consisting of daily cash withdrawals of 98 ATMs of a bank in Istanbul. We find that the optimization approach introduced in this paper results in significant reductions in costs as compared to common practice strategies.
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    Optimization of ATM cash replenishment with group-demand forecasts
    (Pergamon-Elsevier Science Ltd, 2015) Ekinci, Yeliz; Lu, Jye-Chyi; Duman, Ekrem
    In ATM cash replenishment banks want to use less resources (e.g., cash kept in ATMs, trucks for loading cash) for meeting fluctuated customer demands. Traditionally, forecasting procedures such as exponentially weighted moving average are applied to daily cash withdraws for individual ATMs. Then, the forecasted results are provided to optimization models for deciding the amount of cash and the trucking logistics schedules for replenishing cash to all ATMs. For some situations where individual ATM withdraws have so much variations (e.g., data collected from Istanbul ATMs) the traditional approaches do not work well. This article proposes grouping ATMs into nearby-location clusters and also optimizing the aggregates of daily cash withdraws (e.g., replenish every week instead of every day) in the forecasting process. Example studies show that this integrated forecasting and optimization procedure performs better for an objective in minimizing costs of replenishing cash, cash-interest charge and potential customer dissatisfaction. (C) 2014 Elsevier Ltd. All rights reserved.