دانلود مقاله ISI انگلیسی شماره 39889
عنوان فارسی مقاله

بهبود هدف گذاری ارسال مستقیم از طریق مدل سازی پاسخ به مشتری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
39889 2015 10 صفحه PDF سفارش دهید 7700 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Improving direct mail targeting through customer response modeling
منبع

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

Journal : Expert Systems with Applications, Volume 42, Issue 22, 1 December 2015, Pages 8403–8412

کلمات کلیدی
بازاریابی مستقیم - ایمیل مستقیم - مدلسازی پاسخ - بازاریابی پایگاه داده
پیش نمایش مقاله
پیش نمایش مقاله بهبود هدف گذاری ارسال مستقیم از طریق مدل سازی پاسخ به مشتری

چکیده انگلیسی

Direct marketing is an important tool in the promotion mix of companies, amongst which direct mailing is crucial. One approach to improve direct mail targeting is response modeling, i.e. a predictive modeling approach that assigns future response probabilities to customers based on their history with the company. The contributions to the response modeling literature are three-fold. First, we introduce well-known statistical and data-mining classification techniques (logistic regression, linear and quadratic discriminant analysis, naïve Bayes, neural networks, decision trees, including CHAID, CART and C4.5, and the k-NN algorithm) to the direct marketing community. Second, we run a predictive benchmarking study using the above classifiers on four real-life direct marketing datasets. The 10-fold cross-validated area under the receiver operating characteristics curve is used as evaluation metric. Third, we give managerial insights that facilitate the classifier choice based on the trade-off between interpretability and predictive performance of the classifier. The findings of the benchmark study show that data-mining algorithms (CHAID, CART and neural networks) perform well on this test bed, followed by simplistic statistical classifiers like logistic regression and linear discriminant analysis. It is shown that quadratic discriminant analysis, naïve Bayes, C4.5 and the k-NN algorithm yield poor performance.

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