پیش‌بینی ارزش عمر مشتری توسط مدل RFM توسعه‌یافته (مطالعه موردی شرکت بیمه)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی صنایع، دانشکده فنی و مهندسی گلپایگان، گلپایگان، اصفهان، ایران

2 دانشجوی کارشناسی مهندسی صنایع، دانشکده فنی و مهندسی گلپایگان، گلپایگان، اصفهان، ایران

چکیده

در شرایط رقابتی امروز، مشتریان مهم‌ترین منبع درآمد شرکت‌های صنعتی، تجاری و موسسات خدماتی به حساب می‌آیند. این درحالی است که پیش‌بینی ارزش طول عمر مشتری و تخصیص بودجه و منابع محدود به سودآورترین مشتریان، کمک شایانی به مدیران در جهت کسب بازار و افزایش سودآوری خواهد نمود. در این پژوهش، ابتدا به تعیین ارزش عمر فعلی مشتریان براساس مدل RFM توسعه‌یافته و با استفاده از وزن‌دهی سلسله مراتبی پرداخته و سپس، احتمال ریزش مشتریان را برپایه توزیع هندسی زمان انتظار مدل‌سازی می‌کنیم. در ادامه با استفاده از مدل زنجیره مارکوف به تحلیل تغییر وضعیت رفتار مشتریان پرداخته و در انتها، ارزش طول عمر هر مشتری که شامل ارزش عمر فعلی و آینده مشتری می‌باشد تعیین می‌شود. همچنین، به منظورکاربرد این پژوهش، رویکرد معرفی‌شده، در قالب یک مطالعه موردی در خصوص شرکت بیمه به کار گرفته شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Forecasting the Customer Lifetime Value by the Developed RFM Model: A Case Study in Insurance

نویسندگان [English]

  • Aliasghar Bazdar 1
  • Shirin Bahrami 2
1 Assistant professor in Department of Industrial Engineering, Faculty of Engineering, Golpayegan University of Technology, Golpayegan, Iran.
2 Student of Industrial Engineering, Golpayegan University of Technology
چکیده [English]

In the past years, researchers considered the proceeds from selling items or services as the most important source of corporate profits, because there was not much competition among companies. Nowadays customers are the most important source of revenue in the business institutions and service companies. Thereupon, customer satisfaction must be plan by company managers in order to preserve current customer and develop new customer in today's competitive conditions. However forecasting the future manner of customer can be useful to allocate budget and limited resource for preservation of the most profitable customers that will do a great help to the managers in order to gain market and increase profitability. In this paper, we present the approach to determinate current customer lifetime value and introduce the developed model to predict the future of customer lifetime value. At the first, the current lifetime value of the customers is determined based on developed RFM and using hierarchy weight method. Then in order to model the downfall probability of customers based on the geometric probability distribution for waiting time, customers must be group based on their characteristics by clustering approach. In this research, it is compared some clustering criteria for determining the best number of clusters. We are used some technical instruments such as Rapid Miner software for data preprocessing and also such as IBM SPSS and expert Choice for clustering analysis and compared theirs abilities. After that, we are modelled customer behavior via Markov chain procedure. Then customer lifetime value estimated for the future customers. The power of this research is the usage of developed RFM in order to weight customers before grouping. Because of this, the optimum number of clusters can be carefully determined. In order to demonstrate the applicability of this approach, the research used on the insurance company employed as the case study.

کلیدواژه‌ها [English]

  • RFM model
  • Customer Lifetime Value
  • Markov chain
  • Geometric probability distribution
  • Analytic Hierarchy Process
[1]Kupaii, M., & Bidgoli, B. (2007). A method for predicting customer lifetime value chains. International Information and Communication Technology Management, 1-6.
[2]Cuadros, A.J., & Dominguez, V.E. (2014) Customer segmentation model based on value generation for marketing Strategies. Estudios Gerenciales, 30, 25-30 .
[3] Bahrami, SH., & Bazdar, A. (2017).  The Development of RFM Model to analyze and describe the behavior of customers and the value of its properties. 13th Int. International Conference on Industrial Engineering,1-9.
[4] Chenga, C. J., Chiub, S.W., Chengc, C. B., & Wuc, J. Y. (2012). Customer lifetime value prediction by a Markov chain based data-mining model: Application to an auto repair and maintenance company in Taiwan. Scientia Iranica, 19, 849-855.
[5] Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327–1332.
[6] Yeh, I.C., Yang, K.J., & Ting, T.M. (2009). Knowledge discovery on RFM model using Bernoulli sequence.  Expert Systems with Applications, 36, 5866–5871.
[7] Carrasco, R., Blasco, M.F., & Viedma, E.H. (2015). A 2-tuple Fuzzy Linguistic RFM Model and Its Implementation. Procedia Computer Science, 55, 1340 – 1347
[8] Chalaki, k., & Bazdar, A.(2018). An alternative to the BG/NBD model for predicting the customer lifetime value. Int. J. of Engineering Research, 5, 150-162.
[9] Bazdar, A., & Chalaki, k.(2017). Stream of Variation Testing in order to Fault Diagnosis of Multistage Manufacturing Processes. Int. J. of Industrial Engineering Research in Production Systems, 5(10),.69-81 .
[10] Chiang, L., & Yang, CH. (2018) .Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach.Int. J. of Technological Forecasting & Social Change, 130, 177-187.
[11] Caigny, A., Coussement, k., & Bock, w. (2018). A New Hybrid Classification Algorithm for Customer Churn Prediction Based on Logistic Regression and Decision Trees. Int. J. of European Journal of Operational Research, 18, 1-33.
[12] Çavdar, A., & Ferhatosmanoğlu, N. (2018). Airline customer lifetime value estimation using data analytics supported by social network information. Int. J. of Air Transport Management, 67, 19-33.
[13] Yan, ch., Sun, H.,Liu,w., & Chen , j. (2018). An integrated method based on hesitant fuzzy theory and RFM model to insurance customers’ segmentation and lifetime value determination. Journal of Intelligent & Fuzzy Systems, 1-11.
[14] Personnel Insurance Research Group, Insurance Institute. Computational methods study and risks assessment in accident insurance.NO.19, Customized research projects of Central Insurance of Iran (2016).
[15] Khajvand, M., & Tarokh, M. J. (2011). Analyzing Customer Segmentation Based on Customer Value Components: A Private Bank. Int. J. of Industrial Engineering, University of Tehran, Special Issue, 79-93.
[16] Dunn, J.C. (1974). Well-separated clusters and optimal fuzzy partitions.  Int. J.  of cybernetics,4, 95-104 .
[17] Davies, D. L., & Bouldin, D. W. A cluster separation measure. IEEE Trans. Pattern Anal, Machine Intell, 4, 224-22.