Abstract Details
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Activity Number:
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312
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Marketing
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| Abstract - #307758 |
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Title:
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The Ten Killer Data-Mining Errors
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Author(s):
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Samuel Koslowsky*+
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Companies:
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Harte Hanks
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Keywords:
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data mining ;
database marketing ;
analytics ;
targeted modeling
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Abstract:
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Most marketers and analysts agree that data mining plays a critical role in assuring a successful campaign. At time, errors, in executing an analytics project can subtly creep in. While analytic and technical errors can certainly harm a data mining exercise, most of the problems that emerge in a modeling project have little to do with technical issues. Rather, basic reasoning, and marketing related issues are at fault.
Errors emerge from all phases of an exercise. From establishing an appropriate objective, to allowing sufficient time for completion, to misinterpreting the results to deploying results incorrectly.
A good data mining analysis requires qualified Id personnel, domain knowledge experts, analysts and last but not least results summarized in an intelligible format.
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Authors who are presenting talks have a * after their name.
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