Classification analysis is an important tool to support decision making in customer-centric applications like, e.g., proactively identifying churners or selecting responsive customers for direct-marketing campaigns. Whereas the development of novel classification algorithms is a popular avenue for research, corresponding advancements are rarely adopted in corporate practice. This lack of diffusion may be explained by a high degree of uncertainty regarding the superiority of novel classifiers over well established counterparts in customer-centric settings. To overcome this obstacle, an empirical study is undertaken to assess the ability of several novel as well as traditional classifiers to form accurate predictions and effectively support decision making. The results provide strong evidence for the appropriateness of novel methods and indicate that they offer economic benefits under a variety of conditions. Therefore, an increase in use of respective procedures can be recommended.
Lessmann, Stefan and Voß, Stefan
"Customer-Centric Decision Support - A Benchmarking Study of Novel Versus Established Classification Models,"
Business & Information Systems Engineering:
Vol. 2: Iss. 2, 79-93.
Available at: http://aisel.aisnet.org/bise/vol2/iss2/3