ارائه روشی برای بخش‌بندي مشتريان با استفاده از مدل RFM در شرایط عدم قطعیت
محورهای موضوعی :محمدرضا غلامیان 1 , عظیمه مظفری 2 *
1 - هیات علمی
2 - -
کلید واژه: مدل RFM, عدم قطعیت, بخش‌بندی, عدد خاکستری, داده‌کاوی,
چکیده مقاله :
هدف از انجام اين پژوهش ارائه روشی برای بخش‌بندی مشتریان یکی از بانک‌های خصوصی شهر شیراز بر مبنای مدل RFM در شرایط عدم قطعیت داده‌های مربوط به مشتریان است. در چارچوب پيشنهادي اين پژوهش ابتدا مقادير شاخص‌هاي مدل RFM شامل تازگي مبادله (R)، تعداد دفعات مبادله (F) و ارزش پولي مبادله (M) از پایگاه داده مشتریان استخراج و پیش‌پردازش شدند. با توجه به گستردگی دامنه داده‌های مذکور، برای تعیین وضعیت خوب یا بد بودن مشتری نمی‌توان عدد دقیقی تعیین نمود؛ لذا برای از بین بردن این عدم قطعیت، از تئوری اعداد خاکستری استفاده شد که وضعیت مشتری را به صورت یک بازه در نظر می‌گیرد. به این ترتیب با استفاده از یک روش متفاوت به بخش‌بندی مشتريان بانک پرداخته شد که طبق نتایج، مشتریان به سه بخش یا خوشه اصلی تحت عنوان مشتریان خوب، معمولی و بد تفکیک شدند. پس از اعتبارسنجی خوشه‌ها با استفاده از شاخص‌های دان و دیویس بولدین، ویژگی‌های مشتریان در هر یک از بخش‌ها شناسایی شد و در پایان نيز پيشنهادهايي جهت بهبود سيستم مديريت ارتباط با مشتري ارائه گردید.
The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values of RFM model indicators including exchange novelty (R), number of exchanges (F) and monetary value of exchange (M) were extracted from the customer database and preprocessed. Given the breadth of the data, it is not possible to determine the exact number to determine whether the customer is good or bad; Therefore, to eliminate this uncertainty, the gray number theory was used, which considers the customer's situation as a range. In this way, using a different method, the bank's customers were segmented, which according to the results, customers were divided into three main sections or clusters as good, normal and bad customers. After validating the clusters using Don and Davis Boldin indicators, customer characteristics in each sector were identified and at the end, suggestions were made to improve the customer relationship management system.
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