Semi-Supervised Response Modeling

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.

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

“…Advances have also been made in different types of machine learning applications for marketing; viz. hybrid unsupervised machine learning approaches for improving customer lifetime value predictions (see: Hu et al, 2013), and semi-supervised machine learning for finetuning marketing campaigns based on customer responses (and non-responses) (see: Lee et al, 2010). In spite of these advancements, marketing literature still lacks appreciation of innovative methods developed and well-applied in other subject domains (Davis et al, 2013).…”

“…As emerging research indicates (Zarrinkalam et al, 2018), more dynamic changes in the content may be classified using unsupervised machine learning techniques that detect previously unknown patterns from the data. Nascent studies have shown that semi-supervised approaches, whereby a small part of the unstructured data is also used for training the model, can perform better, leading to cutting-edge applications in marketing (see: Ilhan et al, 2018;Lee et al, 2010).…”

Improving direct mail targeting through customer response modeling

“…However, we complement academic literature by presenting and integrating the most popular classifiers into one predictive benchmark study over multiple response datasets, while summarizing the managerial implications for managers. Several statistical classification methods to predict customer responses have been proposed and utilized, such as logistic regression, discriminant analysis and naïve Bayes (Baesens et al 2002, Berger and Magliozzi 1992, Coussement et al 2014, Cui et al 2010, Deichmann et al 2002, Kang et al 2012, Lee et al 2010. These techniques can be very powerful, but each algorithm also make several stringent, but different, assumptions on the underlying distribution between the independent variables and the dependent variable.…”

Digital Enterprise Design & Management

“…ABM approaches can also be used for modeling user response to different sources of advertising. It can also be used response modelling to identify the most critical target groups, complementing traditional approaches for the same (Lee, Shin, Hwang, Cho, & MacLachlan, 2010). Tesfatsion introduced Agent-Based Computational Economics as the computational study of dynamic economic systems modeled as virtual worlds of interacting agents.…”