Correspondence: Tom Breur, XLNT Consulting, Langestraat 8-03, Tilburg 5038 SE, 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.

²runs 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 11 September 2007; Revised 11 September 2007.

Abstract

In the previous paper, the authors explained why there is a tendency to embed data mining solutions in end-to-end software solutions. The advantages of integrated data mining solutions lie in making the process less people dependent, but the disadvantages are that learning from the mining process is hampered. The topic of the previous paper was why to build data mining models interactively. In this paper, the authors will explain how to build decision trees interactively. In this paper, we will demonstrate how interactive model building generates more knowledge on customer behaviour and on the structure of the data. The authors present guidelines for interactive tree building. These guidelines demonstrate how knowledge on when and how the model will be deployed can be taken into account to optimise the model. Furthermore, they illustrate how the context of the business problem that is being addressed with data mining can and should be taken into account when developing models.