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Semi-Supervised Response Modeling

Author

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  • Lee, Hyoung-joo
  • Shin, Hyunjung
  • Hwang, Seong-seob
  • Cho, Sungzoon
  • MacLachlan, Douglas
Abstract
Response modeling is concerned with identifying potential customers who are likely to purchase a promoted product, based on customers' demographic and behavioral data. Constructing a response model requires a preliminary campaign result database. Customers who responded to the campaign are labeled as respondents while those who did not are labeled as non-respondents. Those customers who were not chosen for the preliminary campaign do not have labels, and thus are called unlabeled. Then, using only those labeled customer data, a classification model is built in the supervised learning framework to predict all existing customers. However, often in response modeling, only a small part of customers are labeled, and thus available for model building, while a large number of unlabeled data may give valuable information. As a method to exploit the unlabeled data, we introduce semi-supervised learning to the interactive marketing community. A case study on the CoIL Challenge 2000 and the Direct Marketing Educational Foundation data sets shows that the transductive support vector machine, one of widely used semi-supervised models, can identify more respondents than conventional supervised models, especially when a small number of data are labeled. Semi-supervised learning is a viable alternative and merits further investigation.

Suggested Citation

  • Lee, Hyoung-joo & Shin, Hyunjung & Hwang, Seong-seob & Cho, Sungzoon & MacLachlan, Douglas, 2010. "Semi-Supervised Response Modeling," Journal of Interactive Marketing, Elsevier, vol. 24(1), pages 42-54.
    • Handle: RePEc:eee:joinma:v:24:y:2010:i:1:p:42-54
    DOI: 10.1016/j.intmar.2009.10.004
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    References listed on IDEAS

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    Cited by:

    1. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
    2. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    3. Zihayat, Morteza & Ayanso, Anteneh & Davoudi, Heidar & Kargar, Mehdi & Mengesha, Nigussie, 2021. "Leveraging non-respondent data in customer satisfaction modeling," Journal of Business Research, Elsevier, vol. 135(C), pages 112-126.
    4. Malthouse, Edward C. & Raman, Kalyan, 2013. "The Geometric Law of Annual Halving," Journal of Interactive Marketing, Elsevier, vol. 27(1), pages 28-35.

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