پیش‌بینی ارزش طول عمر مشتریان بانکی با استفاده از تکنیک دسته‎بندی گروهی داده‎ها (GMDH) در شبکۀ عصبی

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استادیار/ گروه مدیریت MBA دانشکده مدیریت دانشگاه تهران

2 دانشجوی دکتری اقتصاد نفت و گاز، بازار و مالیه / دانشگاه علامه طباطبایی

3 مدیر توسعه کسب و کار / تجهیزات مخابراتی نت کالا

چکیده

امروزه نقش مدیریت ارتباط با مشتری به‎عنوان ابزار راهبردی در توسعۀ سازمان­های تولیدی و خدماتی و همچنین جذب و نگهداری مشتریان در صنایع رقابتی، انکارناپذیر است. شناسایی، ارزش­گذاری و دسته­بندی مشتریان و تخصیص بهینۀ منابع به آنها با توجه به ارزشی که برای سازمان­ها دارند، از دغدغه­های اصلیِ حوزۀ مدیریت ارتباط با مشتری است. در این مقاله با استفاده از شبکۀ عصبی GMDH به محاسبه و پیش­بینی ارزش طول عمر مشتریان، به‎عنوان ابزاری کلیدی در تحقق نقش مدیریت ارتباط با مشتری در صنعت بانکداری پرداخته شده است. برای این منظور، اطلاعات جمعیت­شناختی و مالی 5000 مشتری حقیقی ارزندۀ یکی از بانک‎های خصوصی کشور با شرط میانگین موجودی بیش از 500 میلیون ریال در حداقل یکی از حساب­ها، وارد شبکه شد. نتایج نشان داد به‎کمک این روش می‎توان با دقت بالای 90 درصد ارزش طول عمر مشتریان را پیش­بینی کرد که به نسبت روش­های آماری متعارف، دقت بیشتری دارد. پس از حذف متغیرهای مؤثر و مضاعف، شبکه بار دیگر آزمایش شد که در این حالت نیز پیش­بینی با دقت بیش از 85 درصد بود

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Predicting Customer Lifetime Value Based on Financial and Demographic Characteristics Using GMDH Neural Network Case Study: Individual Customers of a Private Bank of Iran

چکیده [English]

The role of customer relationship management as a strategic tool in development of manufacturing and service organizations, and also acquisition and retention customers in competitive industries, is undeniable. Identification, valuation and classification of customers and allocating resources to them based on their value for organization are the main concerns in customer relationship management. One of the most important tool in this direction, is calculating and predicting customer lifetime value (CLV). “CLV” is a value which is expected customer bring to the organization in specified period.
In this paper, calculating and predicting customer lifetime value is as a key tool in the implementation of customer relationship management in banking. The GMDH neural networks due to its high performance in terms of prediction, is applied and with genuine customer demographic and transactional information of a private Iranian bank , the CLV forecasting is evaluated. The results show that this tool can be used to accurately predict over 90% of customer lifetime value.

کلیدواژه‌ها [English]

  • customer lifetime value
  • Customer relationship management
  • GMDH Neural Network
  • prediction
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