Campaign participation prediction with deep learning

Demet Ayvaz, Reyhan Aydogan, M. Tolga Akçura, Murat Şensoy

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.

Original languageEnglish
Article number101058
Number of pages12
JournalElectronic Commerce Research and Applications
Volume48
DOIs
Publication statusPublished - 2021

Keywords

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

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