Author
Ayvaz, Demet, Aydoğan, Reyhan, Akçura, Munir Tolga, Şensoy, Murat
Publication Date
2021-08
Publication Place
-
Elsevier
Subject
Decision tree classification, Deep learning, Feature extraction, Real-time marketing, Wide & Deep network models
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1567-4223
Record ID
baeb75d3-8667-4777-adfe-98d47bde85d6
Library Location
Business Administration, Computer Science
Date
2021-08
Sample Text
Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to offer, creating intelligent campaign management systems that can automate such decisions is essential. In addition, regulations or company policies usually restrict the number of accesses to the customers. Efficient learning of customer behaviour through dynamic campaign participation observations becomes a crucial feature that may ultimately define customer satisfaction and retention. This paper builds on the recent successes of deep learning techniques and proposes a classification model to predict customer responses for campaigns. Classic deep neural networks are good at learning hidden relations within data (i.e., patterns) but with limited capability for memorization. One solution to increase memorization is to use manually craft features, as in Wide & Deep networks, which are originally proposed for Google Play App. recommendations. We advocate using decision trees as an easier way of mining high-level relationships for enhancing Wide & Deep networks. Such an approach has the added benefit of beating manually created rules, which, most of the time, use incomplete data and have biases. A set of comprehensive experiments on campaign participation data from a leading GSM provider shows that automatically crafted features make a significant increase in the accuracy and outperform Deep and Wide & Deep models with manually crafted features.
DOI
10.1016/j.elerap.2021.101058
Cilt
48
