Campaign participation prediction with deep learning

Name: Campaign participation prediction with deep learning
Author: Ayvaz, Demet, Aydoğan, Reyhan, Akçura, Munir Tolga, Şensoy, Murat

İsim	Campaign participation prediction with deep learning
Yazar	Ayvaz, Demet, Aydoğan, Reyhan, Akçura, Munir Tolga, Şensoy, Murat
Basım Tarihi:	2021-08
Basım Yeri	- Elsevier
Konu	Decision tree classification, Deep learning, Feature extraction, Real-time marketing, Wide & Deep network models
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1567-4223
Kayıt Numarası	baeb75d3-8667-4777-adfe-98d47bde85d6
Lokasyon	Business Administration, Computer Science
Tarih	2021-08
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Campaign participation prediction with deep learning

Yazar Ayvaz, Demet, Aydoğan, Reyhan, Akçura, Munir Tolga, Şensoy, Murat

Basım Tarihi 2021-08

Basım Yeri - Elsevier

Konu Decision tree classification, Deep learning, Feature extraction, Real-time marketing, Wide & Deep network models

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1567-4223

Kayıt Numarası baeb75d3-8667-4777-adfe-98d47bde85d6

Lokasyon Business Administration, Computer Science

Tarih 2021-08

Örnek Metin 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