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dc.contributor.authorAyvaz, Demet
dc.contributor.authorAydoğan, Reyhan
dc.contributor.authorAkçura, Munir Tolga
dc.contributor.authorŞensoy, Murat
dc.description.abstractIncreasingly, 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.en_US
dc.relation.ispartofElectronic Commerce Research and Applications
dc.titleCampaign participation prediction with deep learningen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-5260-9999 & YÖK ID 145578) Aydoğan, Reyhan
dc.contributor.authorID(ORCID 0000-0003-3982-4720 & YÖK ID 124612) Akçura, Tolga
dc.contributor.authorID(ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat
dc.contributor.ozuauthorAydoğan, Reyhan
dc.contributor.ozuauthorAkçura, Munir Tolga
dc.contributor.ozuauthorŞensoy, Murat
dc.subject.keywordsDecision tree classificationen_US
dc.subject.keywordsDeep learningen_US
dc.subject.keywordsFeature extractionen_US
dc.subject.keywordsReal-time marketingen_US
dc.subject.keywordsWide & Deep network modelsen_US
dc.contributor.ozugradstudentAyvaz, Demet
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff and PhD Student

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