بررسی و خوشه‌بندی مشتریان، بر اساس مدل RFM و طراحی الگویی برای ارائه خدمات به مشتریان کلیدی

نوع مقاله: مدیریت استراتژیک(استراتژهای منابع انسانی- استراتژی های تولید- استراتژی های سرمایه گذاری- استراتژی های بازار و رقابتی)

نویسندگان

1 دانشجوی دکتری مدیریت بازاریابی، دانشگاه آزاد اسلامی واحد بابل، بابل، ایران

2 استادیار دانشکده علوم انسانی، دانشگاه آزاد اسلامی واحد بابل، بابل، ایران

چکیده

این تحقیق بررسی و خوشه­بندی مشتریان ،بر اساس مدل RFM و طراحی الگویی برای ارائه خدمات به مشتریان کلیدی می­پردازد. جامعه آماری.گروه اول، جهت تعیین وزن شاخص­های R, F, M ، 18 نفر از خبرگان بانک ملت استان مازندران هستند وگروه دوم جهت خوشه­بندی مشتریان بر اساس مدل RFM و با استفاده از داده­های اسنادی بانک مشتریان ،اصناف و فروشگاههایی که دارای POS)) بانکی می­باشند. روش تجزیه و تحلیل داده­ها تکنیک­ تحلیل سلسله مراتبی فازی، تکنیک آنتروپی، روش کا- میانگین و روش DBSCAN  می­باشد. طبق نتایج، وزن هر کدام از شاخص­های آر.اف ام. با استفاده از فرایند تحلیل سلسله مراتبی و آنتروپی بدست آمد و در نهایت وزن شاخص­ها بصورت ترکیبی برآورد گردید. وزن شاخص­ها به ترتیب  Mبرابر 5998/0، F برابر 2672/0 و R برابر 1330/0. هم­چنین در ادامه تجزیه و تحلیل داده­ها، خوشه­بندی مشتریان با دو روش K-Means و DBSCAN انجام شد. نتایج نشان داد روش K-means روش بهتری برای خوشه‌بندی مشتریان و ارائه خدمات می­باشد. بعد از خوشه بندی و تشکیل هرم مشتریان با روش K-means، مشتریان بانک بر اساس اطلاعیه­های ابلاغی در گروه­های (مهان، شایان، پویان، تابان، رویان و بحران) دسته بندی شدند که شعب بانک ملت با استفاده از این اطلاعات می­توانند، خدمات و تسهیلات مخصوص برای هر خوشه یا گروه از مشتریان در نظر بگیرند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Inspecting the Effective Factors on Identification and Maintenance of Key Customers Based on RFM Model and Designing a Model for Providing Services

نویسندگان [English]

  • Amir Uosefy Zad 1
  • Ali Sorayaei 2
1 Department of management, faculty of Humanities, Islamic Azad University, Babol Branch, Iran
2 Assistant proessor, Business Management Department, Faculty of Humanities, Islamic Azad University of Babol, Iran
چکیده [English]

The purpose of this research is to investigate and study the factors influencing the identification and preservation of key customers based on the RFM model and model design for the provision of services. The statistical population of the study consists of two different groups. In the first group, for determining the weight of the indicators (R F M) 18 experts from the Mellat Bank of Mazandaran province were randomly selected and for the second group in order to cluster customers based on the RFM model and using bank document data, those who were using the POS machine in 1396 were examined. The data analysis method is a fuzzy hierarchical analysis technique, entropy technique, K- Means method and DBSCAN method. According to the results, the weight of each of the RAF indexes were rated using the process of hierarchical analysis and entropy analysis and finally the weight of the indices was estimated as a combination. The weight of the indexes was M = 0.5998, F = 0.2672 and R = 0.1330. In addition, customer data clustering was conducted using K-Means and DBSCAN methods. Finally, the results showed that the K-means method is a better way to customer clustering and service delivery.

کلیدواژه‌ها [English]

  • Key Customers
  • RFM Model
  • Life Cycle Value
  • K-Means Method
  • DBSCAN Method

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