Data, information and knowledge management in the field of smart business
Mohammad Kazemi; Mohammad Ali Keramati; Mehrzad Minooie
Abstract
The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this issue with the field of information technology will be one of the important topics in the field ...
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The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this issue with the field of information technology will be one of the important topics in the field of information technology management. The main purpose of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers and will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for customer clustering that results in more resolution in clustering. This makes more suitable features to be placed next to the four features of LRFM and improve the performance of LRFM. Due to the high number of variations in this problem, it is not possible to do it manually and the proposed method tries to provide a separate pattern for clustering for the customers of each bank by examining different situations. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.IntroductionToday, the Achilles heel of all customer-oriented businesses is customer satisfaction and providing services tailored to each customer's situation. This issue has gone so far that regardless of customer satisfaction, any organization will face failure (Otto et al., 2019). One of the main current challenges for customer-oriented organizations is understanding the differences and ranking customers in order to optimally allocate resources. This issue is very important in managing the correct relationship with the customer. Banks are one of the main customer-oriented institutions in the country (Morzdashti et al., 2022). The bank does not do any proper clustering to know its customers and plan future goals. More precisely, it does not have information about the total number of customers and their distribution. Because of this, more time and money is wasted. As far as the research of this article has followed; The clustering that currently exists for customers does not have the necessary dynamics and people are clustered based on some characteristics such as transaction amounts, occupation or other general characteristics. LRFM model is a method used to cluster customers in customer relationship management. In this model, customers are clustered based on four characteristics of customer relationship, novelty of exchange, number of times of exchange and monetary value exchanged. In fact, the customer relationship length has been added to the RFM model and created the LRFM model. Because, the RFM model was not able to identify loyal customers (Moslehi et al., 2013).In the proposed model of this article, an attempt will be made to provide a dynamic method for using variables with the LRFM method to provide the possibility of implementing different clusters depending on the time of use. This issue will lead to more compliance of the proposed clustering method with reality.Research Question(s)What methodology is used to follow the process of presenting the proposed model?What features can be placed next to the LRFM model to provide appropriate results?What methodology is used to follow the process of presenting the proposed model?What features can be placed next to the LRFM model to provide appropriate results?What will be the structure of particle swarm algorithm?What similarity measure or clustering method would be suitable for customers?How can the LRFM model be improved by the particle swarm algorithm and the creation of different clusters based on the K-means method? Literature ReviewShrahi and Ali Qoli have implemented a clustering method for the customers of one of Sepeh Bank branches in Tehran (Shrahi and Ali Qoli, 2015). This model is based on K-means clustering algorithm. In this method, an attempt has been made to identify sixty companies loyal to the bank from among all legal customers. However, the K-means algorithm has some problems (Bagatini et al., 2019, Santini, 2016):Determine the optimal value for the number of clusters.The initial points that are chosen randomly at the beginning of the algorithm have a great impact on the final result.The order of data entry and their review is effective in the final result.Ayoubi has tried to cluster bank customers using Kohonen neural networks (Ayoubi, 2016). In this method, the training of a neural network is done using the training data, and after that it is possible to cluster the new customer.Yousefizad and Sorayai have also used the RFM model to cluster customers in order to design a model for providing services to customers, which consists of two stages (Yosefizad and Sorayai, 2017).suggested method:In this section, the proposed method of the article is described in full detail. MethodologyIn this part, how to improve the LRFM method using the combination of particle swarm algorithm and K-means method is described. All the steps of particle swarm algorithm are followed and its functions and parameters are specified. The steps of the proposed method will be as follows:Initialization: The schematic of the initial population matrix will be as shown in Figure (2). This matrix consists of two parts. The first part has one element that tries to suggest the number of clusters using the K-means method, and the second part will have 10 binary elements.Calculating the fitness of each particle: Using the fitness function, the fitness level is determined for each particle present in the population. This fitness level is based on clustering using the K-means method. The appropriateness of the clustering done is measured based on the intraclass variance criterion, which corresponds to the image of the fitness of each particle (Ahmar et al., 2018).Update of particle values: Using two parameters, local optimum (LBEST) and global optimum (GBEST), the values present in the particles can be updated. By LBEST, we mean the best value that the I-th particle has reached so far (the best-fit value for the I-th particle). Also, GBEST means the value that has the best fit until T iterations. These two values are used to update the values of other particles. ConclusionThis article tries to provide a dynamic method for clustering bank customers in order to improve their service. The LRFM method has four important features in the field of banking, but its problem is lack of dynamics. More precisely, it is possible that other characteristics such as financial, occupational, or daily transaction characteristics can be added to the four LRFM characteristics and improve the performance of this method. Among all the features that can be placed next to the four features of LRFM; Depending on the customer's data, the appropriate features should be selected. This choice is the responsibility of the particle swarm algorithm. This algorithm tries to put appropriate features along with the four LRFM features depending on the data conditions and customer information to get a better result in clustering. Also, because this algorithm methodK-means helps in finding the number of clusters.It is also possible to replace the particle swarm with other meta-heuristic methods and compare its results with the results in the article.
