TY - JOUR
T1 - Campaign participation prediction with deep learning
AU - Ayvaz, Demet
AU - Aydogan, Reyhan
AU - Tolga Akçura, M.
AU - Şensoy, Murat
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Decision tree classification
KW - Deep learning
KW - Feature extraction
KW - Real-time marketing
KW - Wide & Deep network models
UR - http://www.scopus.com/inward/record.url?scp=85106550830&partnerID=8YFLogxK
U2 - 10.1016/j.elerap.2021.101058
DO - 10.1016/j.elerap.2021.101058
M3 - Article
AN - SCOPUS:85106550830
SN - 1567-4223
VL - 48
JO - Electronic Commerce Research and Applications
JF - Electronic Commerce Research and Applications
M1 - 101058
ER -