Title

Comparison of Some Ai and Statistical Classification Methods for a Marketing Case

Publication Type

Journal Article

Publication Date

1997

Abstract

Recent progress in data processing technology has made the accumulation and systematic organization of large volumes of data a routine activity. As a result of these developments, there is an increasing need for data-based or data-driven methods of model development. This paper describes data-driven classification methods and shows that the automatic development and refinement of decision support models is now possible when the machine is given a large (or sometimes even a small) amount of observations that express instances of a certain task domain. The classifier obtained may be used to build a decision support system, to refine or update an existing system and to understand or improve a decision-making process. The described AI classification methods are compared with statistical classification methods for a marketing application. They can act as a basis for data-driven decision support systems that have two basic components: an automated knowledge module and an advice module or, in different terms, an automated knowledge acquisition/retrieval module and a knowledge processing module. When these modules are integrated or linked, a decision support system can be created which enables an organization to make better-quality decisions, with reduced variance, probably using fewer people.

Discipline

Business

Research Areas

Operations Management

Publication

European Journal of Operational Research

Volume

2

Issue

2

First Page

312

Last Page

325

ISSN

0377-2217

Identifier

10.1016/s0377-2217(97)00122-7

Publisher

Elsevier