Correspondence: Tom Breur, Principal, XLNT Consulting, Langestraat 8-03, SE Tilburg 5038, The Netherlands. Tel: +31 6 463 468 75; E-mail: tombreur@xlntconsulting.com

¹is Principal Consultant at the marketing intelligence department of VODW Marketing (www.vodw.com). His core business is helping companies make their marketing activities more efficient and effective based on facts. His fields of interest span: predictive modelling, lifetime value management and retention.

¹runs consulting firm XLNT Consulting (www.xlntconsulting.com) dedicated to helping companies make more money with their data. His fields of interest span: data mining, analytics, data quality, IT governance, data warehousing and business models.

Received 21 May 2007; Revised 21 May 2007.

Abstract

There is a growing tendency to embed the entire data mining process within a single software solution. These data mining suites have data capture, pre-processing, model building and deployment all integrated. In two articles the authors will discuss the merits of interactive model building. In this first paper the 'why' question will be answered; in the second paper, the authors will demonstrate 'how' to do this. This 'interactivity' basically consists of overriding statistical parameters as they are derived from training data. The authors propose interactive model building as an alternative to automatic model building. First, interactive model building generates more knowledge on customer behaviour and on the structure of the data. Secondly, deliberately influencing the way models are being generated actually leads to better predictions of customer behaviour. The authors illustrate how the context of the business problem can and should be taken into account when developing models.