multi-objective assignment model

The main purpose of this paper is to propose a multi-objective model for assigning service/product to clustered customers. The main practical objectives of this model from the perspective of the bank are reduced cost and risk and increased customer satisfaction. Design/methodology/approach: In this paper, five indicators of recency, frequency, monetary, loan and deferred have been identified and customers have been clustered, accordingly using K-means approach. Then, a three-objective mathematical model has been designed to assign optimal service/product as response to customer. Finally the model has been solved by simulation based optimization.

In the case study, all information about five characteristics of customers was extracted from the database, 31953 customers were placed in seven clusters and the validity of these clusters was measured. A three-objective mathematical model was designed based on the characteristics of 13 types of bank products/services. Then, the simulation modeling solutions were improved using the simulated annealing algorithm. In this study, Weka and R-Studio, Arena and Longo were used for data mining, simulation and optimization, respectively.

The limitations of this study include inability of simulation instruments for drawing, solving all probable states (more scenarios) and solving the model for those states. It is recommended to develop the mathematical model with respect to customer, so that after problem solving, the bank would be able to make decision on providing services and products to its customers. Simultaneously, the objective functions would be fitted within their most reasonable states and ultimately, using a model, the parameters related to each product can be set for the new customer referring to the bank.

Products/services were assigned according to customer needs in a way that cost and risk were reduced and the utility of assignment was increased through the proposed model and simulating the behavior of each cluster of customers.

Paradigm shift in the banking industry is changing from e-banking to digital banking. In digital banking, assigning/customizing products/services, regarding the needs of customers, is very difficult .The banking industry is not well equipped to respond to the digital banking expectations of most consumers. One of the most important challenges of banks is recognizing customers, clustering and assigning a service/product to each of the different clusters. The main policy in the banking industry is to increase customer satisfaction and reduce cost and risk in sales service. Therefore, each customer should have a dedicated service/product.

In this paper, authors attempted to use one of the clustering approaches in multi-objective programming. In addition, they proposed an approach for assigning product/service to customer by simulating and analyzing the behavior of each customer cluster.

Knowing customer behavior patterns, clustering and providing proper services to the customers is one of the most important issues for the banks.In this research, 5 criteria of each customer, including Recency, Frequency, Monetary, Loan and Deferred, were extracted from a bank database during a fiscal year, and then customers were clustered using K-Means algorithm. Then, a multi-objective model of bank service allocation was designed for each of the clusters. The purpose of the designed model was to increase customer satisfaction, reduce costs, and reduce the risk of allocating services. Given the fact that the problem does not have an optimal solution, and each client feature has a probability distribution function, simulation was used to solve the models. To determine the optimal solution, Simulated Annealing algorithm was used to create neighboring solutions and consequently a simulation model was implemented. The results showed a significant improvement in the current situation. In this research, we used Weka and R-Studio software for data mining and Arena for simulation and optimization

Knowing customer behavior patterns, clustering and assigning them is one of the most important purpose for banks. In this research, the five criteria of each customer, including Recency, Frequency, Monetary, Loan and Deferred, were extracted from the bank database during one year, and then clustered using the customer's K-Means algorithm. Then, the multi-objective model of bank service allocation was designed for each of the clusters. The purpose of the designed model was to increase customer satisfaction, reduce costs, and reduce the risk of allocating services. Given the fact that the problem does not have an optimal solution, and each client feature has a probability distribution function, simulation was used to solve it. In order to determine the neighbor optimal solution of the Simulated Anneling algorithm, neighboring solutions were used and a simulation model was implemented. The results showed a significant improvement over the current situation. In this research, we used Weka and R-Studio software for data mining and Arena for simulation for optimization. The results of this research were used to develop Business Intelligence software for customers in one of the private banks of Iran.