Simultaneous Analysis of Monthly Water Clustering Shiraz Water Subscribers Based on the Value of Their Life Cycle and RFM Model

Document Type : Research Paper

Authors

1 Master of Industrial Engineering, Iran University of Science and Technology, Shiraz Water and Wastewater Company, Shraz, Iran.

2 Master of Industrial Engineering, Islamic Azad Shiraz University, Shraz, Iran.

Abstract

Due to the importance of identifying low water consumption subscribers, in this study, using data mining methods, segmentation and identification of water subscribers based on the life cycle value and RFM model are discussed. First, the values of RFM model indices for Shiraz water subscribers were extracted from the subscribers' database and pre-processed. After weighting these three indicators using hierarchical analysis, subscribers were segmented using self-organizing neural network; Then the lifetime value pyramid was drawn to identify key subscribers and clusters of key and valuable subscribers were identified. The results show that the water supply index is the most important and the recent payment index is the least important in determining the value of subscribers and based on this, subscribers were divided into six clusters. According to the life cycle value pyramid, the most valuable subscribers are located in cluster 3, which are mainly residential, commercial and industrial uses, are mainly residents of Zone 2, most of these subscribers have paid their bills on time and their water's amount is mostly low. By identifying valuable and key subscribers, suggestions were made to improve the service to subscribers by considering their value.

Keywords

References

 
آموزگار، م.، (1395)، "ارائه راه‌کار دو مرحله‌ای برای شناسایی الگوی مصرف برق"، نشریه علمی پژوهشی کیفیت و بهره‌وری صنعت برق ایران، 5(9)، 48-57.
امینی، ق.، فرمانی انتظام، ح.، جان صادق‌پور، ع.، و داودآبادی، ع.، (1397-الف)، "کاربرد داده‌کاوی در شناسایی مشترکین با مصارف غیرمجاز آب (مطالعه موردی: شرکت آب و فاضلاب قم)"، دومین کنگره علوم و مهندسی آب و فاضلاب ایران، اصفهان.
امینی، ق.، فرمانی انتظام، ح.، جان صادق‌پور، ع.، و داودآبادی، ع.، (1397-ب)، "شناسایی و استخراج الگوی مصرف آب به‌روش داده‌کاوی (مطالعه موردی: شرکت آب و فاضلاب قم)"، دومین کنگره علوم و مهندسی آب و فاضلاب ایران، اصفهان.
امینی، ق.، (1399)، "مدل‌سازی تشخیص مصرف غیرمجاز آب (مطالعه موردی: شهر قم)"، مجله آب و فاضلاب، 31(4)، 193-184.
رزمی، ج.، و قنبری، آ.، (1388)، "ارائه مدلی نوین برای محاسبه ارزش دوره عمر مشتری"، نشریه مدیریت فناوری و اطلاعات، 1(1)، 50-35.
طباطبایی، ا.، (1398)، نگرشی بر داده‌کاوی، دانشگاه آزاد قزوین، 10-15.
غضنفری، م.، (1399)، داده کاوی و کشف دانش. تهران، دانشگاه علم و صنعت ایران.
کاظمی، ز.، (1394)، "به‌کارگیری فرآیندکاوی برای بهبود فرآیندهای مدیریت دانش در مراکز تماس (مطالعه موردی: مرکز تماس 122 سازمان آب و فاضلاب استان تهران)"، پایان‌نامه کارشناسی ارشد، دانشکده فنی، دانشگاه تربیت مدرس.
کجوری نفت‌چالی، م. و، فریدونیان، ع.، (1394)، "شناسایی الگوی مصرف انرژی الکتریکی با داده‌کاوی"، سی‌امین کنفرانس بین‌المللی برق، تهران.
مهرگان، م. ر.، (1397)، پژوهش عملیاتی پیشرفته، تهران، انتشارات کتاب دانشگاهی، چاپ اول.
Berry, M.J., and Linoff, G.S., (2016), Data mining techniques: for marketing, sales, and customer relationship management, John Wiley and Sons, https://doi.org/10.4324/9781351016551.
Bin, D., Peiji, S., and Dan, Z., (2008), “Data mining for needy student identify based on improved RFM model: A case study of university”, International Conference on Information Management, Innovation Management and Industrial Engineering, New York, https://doi.org/10.1109/ICIII.2008.128.
Buttle, F., (2018), Customer Relationship Management: Concepts and Tools, Elsevier Butterworth Heinemann,
https://doi.org/10.4324/9781351016551.
Cheng, C.H., and Chen, Y.S., (2009), “Classifying the segmentation of customer value via RFM model and RS theory”, Expert Systems with Applications, 36, 4176-4184,
https://doi.org/10.1016/j.eswa.2008.04.003.
Foster, H.S., and Beattie, B.R., (1981), “On the specification of price in studies of consumer demand under block price scheduling”, Land Economics, 57(4), 624-629,
https://doi.org/10.2307/3145676.
Han, J., Pei, J., and Kamber, M., (2016), Data mining: Concepts and techniques, Elsevier, https://doi.org/10.4324/9781351016551.
Hughes, A.M., (1994), Strategic database marketing, Chicago: Probus Publishing, https://doi.org/10.1287/ijoc.2021.1121.
Keiningham, T.L., Aksoy, A., and Bejou, D., (2006), “Approches to measurement and management of customer value”, Journal of Relationship Marketing, 5(2), 37-54, https://doi.org/10.1300/J366v05n02_03.
Kreidler, M., (2018), Guide to auto insurance, Washington State Office of the Insurance Commissioner, viwed 2010/4/24, www.insurance.wa.gov.
Monika, C., and Amarpreet, K., (2018), “A comparative study of classification techniques for fraud detection”, Journal on Future Revolution in Computer Science and Communication Engineering, 4, 19-23.
Rosenblatt, F., (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms, (Vol. 55), Washington, DC: Spartan books.
Saggaf, M.M., Toksoz, M.N., and Marhoon, M.I., (2003), “Seismic facies classification and dentification by competitive neural networks”, Geophysics, (44), 1041-1063, https://doi.org/10.1190/1.1635052.
Seyedhosseini, S.M., Gholamian, M.R., and Maleki, A., (2011), “A methodology based on RFM using data mining approach to assess the customer loyalty”, International Journal of Industrial Engineering, 22(2), 171-179, https://doi.org/10.1300/J366v05n02_03.
Journal of Water and Wastewater Science and Engineering
Volume 8, Issue 2
July 2023
Pages 16-24

Files

History

  • Receive Date: 10 July 2022
  • Revise Date: 14 September 2022
  • Accept Date: 26 November 2022

Share

How to cite

Statistics