Palgrave Macmillan Ltd 0967-3237 $30.00 Vol. 15, 3, 137–145 Journal of Targeting, Measurement and Analysis for
MarketingINTRODUCTION
Both within the CRM product space as well asoutside it, integrated data
留学生论文代写mining solutions aredominating the marketplace. Successful executionof CRM
strategy depends largely on deliveringanalytic results. For data mining to becomeeffective, ease of model deployment is one of thedecisive factors. Because of this, the need forintegrated solutions goes even beyond the bordersof the data mining process: it extends into theareas of campaign management, personalisationand marketing automation. But this seamlessintegration of data mining technology in CRMsystems has evoked the illusion that machines cantake over the task of (predictive) modelling.Vendors sometimes suggest a future whereintelligent machines will automatically learnhow to respond to changing customer needs.Expectations of these adaptive Customer RelationOptimisation solutions have been set, and theneed for human intervention to develop businessintelligence and to manage this knowledge isoften severely underestimated.The authors do not oppose integrated norautomated solutions per se . In fact, embeddabilityis a key requirement to make data miningsolutions pay off. Neither do we opposeautomatic data mining solutions per se . Theneeded accompanying organisation andknowledge structure surrounding it is, however,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
Papers
Merits of interactive decision tree
building: Part 1
Received (in revised form): 21st May, 2007
Bas van den Berg
is Principal Consultant at the marketing intelligence department of VODW Marketing ( www.vodw.com ). His core business is helping companies make
their marketing activities more effi cient and effective based on facts. His fi elds of interest span: predictive modelling, lifetime value management and
retention.
Tom Breur
runs consulting fi rm XLNT Consulting ( www.xlntconsulting.com ) dedicated to helping companies make more money with their data. His fi elds of
interest span: data mining, analytics, data quality, IT governance, data warehousing and business models.
Keywords CRM , analytical CRM , data mining , decision tree , targeting , direct marketing
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 fi rst 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 infl uencing 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.
Journal of Targeting, Measurement and Analy
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