Applications of Data Mining to Electronic Commerce

TL;DR

This paper discusses how data mining techniques are applied to electronic commerce, emphasizing the importance of rich data, large volumes, reliable collection, result evaluation, and integration challenges.

Contribution

It identifies key desiderata for successful data mining applications in electronic commerce and discusses practical considerations for implementation.

Findings

Rich customer data enhances mining effectiveness

Automated data collection improves reliability

Integration challenges impact deployment success

Abstract

Electronic commerce is emerging as the killer domain for data mining technology. The following are five desiderata for success. Seldom are they they all present in one data mining application. 1. Data with rich descriptions. For example, wide customer records with many potentially useful fields allow data mining algorithms to search beyond obvious correlations. 2. A large volume of data. The large model spaces corresponding to rich data demand many training instances to build reliable models. 3. Controlled and reliable data collection. Manual data entry and integration from legacy systems both are notoriously problematic; fully automated collection is considerably better. 4. The ability to evaluate results. Substantial, demonstrable return on investment can be very convincing. 5. Ease of integration with existing processes. Even if pilot studies show potential benefit, deploying automated solutions to previously manual processes is rife with pitfalls. Building a system to take advantage of the mined knowledge can be a substantial undertaking. Furthermore, one often must deal with social and political issues involved in the automation of a previously manual business process.