دانلود مقاله ISI انگلیسی شماره 22201
ترجمه فارسی عنوان مقاله

تسهیل متقابل فروش در بازار مخابرات تلفن همراه به منظور توسعه مدل طبقه بندی مشتریان بر اساس تکنیک های داده کاوی ترکیبی

عنوان انگلیسی
Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
22201 2011 8 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 38, Issue 5, May 2011, Pages 5005–5012

ترجمه کلمات کلیدی
طبقه بندی مشتریان - الگوریتم های ژنتیکی - رگرسیون لجستیک - شبکه های عصبی مصنوعی - درخت های تصمیم گیری - بازار مخابرات تلفن همراه
کلمات کلیدی انگلیسی
Customer classification, Genetic algorithms, Logistic regression, Artificial neural network, Decision tree, Mobile telecom market
پیش نمایش مقاله
پیش نمایش مقاله  تسهیل متقابل فروش در بازار مخابرات تلفن همراه به منظور توسعه مدل طبقه بندی مشتریان بر اساس تکنیک های داده کاوی ترکیبی

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company’s case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance