Detecting Valuable Customers Using the Trade Patterns of Financial Transactions Applying Integrated RFM and OLAP

One of the challenges that banks are faced with is recognition and differentiation of customers and providing customized services to them. Recognizing valuable customers based on their field of business is one of the key objectives and competitive advantages of banks. To determine guild patterns of the valuable customers based on their transactions and value of each guild for the bank, the banking tools on which the customer’s transactions take place need to be surveyed. Using deeper insights into the value of each guild, banks can provide customized services to ensure satisfaction and loyalty of their customers. Study population was comprised of the holders of point of sale (POS) devices in different guilds and the transactions done through the devices in an 18-months period. Datamining methods were employed on the set of data and the results were analyzed. Data preparation and analysis were done though online analytical processing (OLAP) method and to find guild patterns of the bank customers, value of each customer was determined using recency, frequency, monetary (RFM) method and clustered based on K-means algorithm. Finally, specifications of customers in the most valuable cluster were analyzed based on their guilds and the rules were extracted from the model developed using C5 decision tree algorithm.

[1]  Taeho Hong,et al.  Segmenting customers in online stores based on factors that affect the customer's intention to purchase , 2012, Expert Syst. Appl..

[2]  Mehdi Mohammadzadeh,et al.  A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran , 2017, Procedia Computer Science.

[3]  A. Sargeant,et al.  Market segmentation in the Indonesian banking sector: the relationship between demographics and desired customer benefits , 2000 .

[4]  Liangzhong Shen,et al.  The Application Research of OLAP in Police Intelligence Decision System , 2012 .

[5]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

[6]  Zhuo Zhang,et al.  Review of customer segmentation method in CRM , 2011, 2011 International Conference on Computer Science and Service System (CSSS).

[7]  Sahar Tahanisaz,et al.  Evaluation of passenger satisfaction with service quality: A consecutive method applied to the airline industry , 2020, Journal of Air Transport Management.

[8]  You-Shyang Chen,et al.  Classifying the segmentation of customer value via RFM model and RS theory , 2009, Expert Syst. Appl..

[9]  Shu-Ching Wang,et al.  A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network , 2020, J. Ind. Inf. Integr..

[10]  T. Hughes.  Catastrophes, Phase Shifts, and Large-Scale Degradation of a Caribbean Coral Reef , 1994, Science.

[11]  Enrique Herrera-Viedma,et al.  A comparison between Fuzzy Linguistic RFM Model and traditional RFM model applied to Campaign Management. Case study of retail business , 2019, ITQM.

[12]  Hamid Eslami Nosratabadi,et al.  Evaluating the success level of data mining projects based on CRISP-DM methodology by a Fuzzy expert system , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[13]  Heyong Wang,et al.  Research on customer opinion summarization using topic mining and deep neural network , 2021, Math. Comput. Simul..

[14]  Konstantinos Tsiptsis,et al.  Data Mining Techniques in CRM: Inside Customer Segmentation , 2010 .

[15]  Rice Novita,et al.  Analysis for Customer Lifetime Value Categorization with RFM Model , 2019, Procedia Computer Science.

[16]  Partha Pratim Roy,et al.  Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction , 2019, Inf. Fusion.

[17]  R. Liebich,et al.  Impact detection using a machine learning approach and experimental road roughness classification , 2019, Mechanical Systems and Signal Processing.

[18]  SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP. , 2021, Accident; analysis and prevention.