مدل خوشه بندی و پیش بینی ارزش طول عمر مشتری (مورد مطالعه: مشتریان مرکز شماره گذاری کالا و خدمات ایران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، دانشگاه مهرالبرز.

2 دانشیار، دانشگاه تهران.

10.52547/jimp.10.4.41

چکیده

یکی از مباحث مهم در زمینه حفظ مشتریان و چگونگی رفتار با آنها، ارزش طول عمر مشتری (CLV) است . هدف از این پژوهش، طراحی مدلی برای خوشه بندی و پیش بینی طول عمر مشتریان و همچنین ارزیابی مشتریان در مرکز شماره گذاری کالا و خدمات ایران است. در این پژوهش اطلاعات 74385 عضو این سازمان در بازه زمانی 1390 - 1396 دریافت شد. مشتریان توسط تکنیک داده کاوی CRISP طبقه بندی شده و درنهایت مدلی برای پیش بینی آن ها طراحی شد. ابتدا اعضا توسط مدل RFM و الگوریتم K-Means به 7 طبقه دسته بندی شده و سپس هر طبقه توسط روش محاسبه ارزش طول عمر مشتریان رتبه بندی شد. در ادامه توسط الگوریتم های رگرسیون لجستیک، درخت تصمیم و شبکه های عصبی، الگوهای پنهان بین داده ها و بخش های مختلف مشتریان کشف شدند. نتایج این پژوهش، رفتار مشتریان هر یک از خوشه ها را در خدمات مرکز و همچنین مدل رفتار مشتریان آتی را نشان داده است. این پژوهش با تحلیل خوشه ها به مدیران در ارائه راهبردهای بازاریابی، حفظ اعضای وفادار و جذب یا حذف اعضای غیرفعال، یاری می رساند. در پژوهش حاضر تعداد خوشه مناسب برای مشتریان 7 عدد است؛ همچنین در پیش بینی کلاس مشتریان عملکرد شبکه های عصبی با دقت 56 / 99 درصد نسبت دیگر الگوریتم ها بهتر بوده است.

کلیدواژه‌ها

عنوان مقاله [English]

Clustering and Prediction Model of Customer Lifetime Value (Case Studies: IRAN National Center for Numbering Goods and Services)

نویسندگان [English]

  • Fateme Nabizade 1
  • Saeed Rouhani 2
1 Masters, Mehralborz University.
2 Associate Professor, University of Tehran.
چکیده [English]

One of the important topics in this field is Customer Lifetime value
(CLV) that means how much profit a customer generates in his lifetime for a company. The main goal of this research is presenting a model for clustering and predicting customer lifetime value and customer evaluation in IRAN National Center for Numbering Goods and Services. In this research, 74,385 records of members at a specified time interval were used (from 2011 to 2017). Members are classified by CRISP methodology, resulting in the presentation of a model for predicting them. At first, members are classified into 7 clusters by RFM and K-Means. Next, each cluster is rated by CLV. Next, hidden patterns are discovered inside the data and various segments of members are then predicted through classification algorithms. Finally, the algorithms are evaluated and the final report is prepared. The results of this study exhibit the member’s behavior in each cluster on the organization’s services and membership or extended subscription and also future customer behavior are unveiled. This research helps the managers to come up with marketing strategies to keep loyal members and attract or remove inactive members by analyzing the clusters.

کلیدواژه‌ها [English]

  • Customer Lifetime Value
  • Data Mining
  • RFM
  • Clustering
  • Predict

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