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‘New and improved’ direct marketing: A non-parametric approach

Advances in Econometrics

ISBN: 978-0-76230-857-6, eISBN: 978-1-84950-142-2

ISSN: 0731-9053

Publication date: 28 February 2002


In this paper we consider a recently developed non parametric econometric method which is ideally suited to a wide range of marketing applications. We demonstrate the usefulness of this method via an application to direct marketing using data obtained from the Direct Marketing Association. Using independent hold-out data, the benchmark parametric model (Logit) correctly predicts 8% of purchases by those who actually make a purchase, while the nonparametric method correctly predicts 39% of purchases. A variety of competing estimators are considered, with the next best models being semiparametric index and Neural Network models both of which turn in 36% correct prediction rates.


Jeffrey, R.S. (2002), "‘New and improved’ direct marketing: A non-parametric approach", Advances in Econometrics (Advances in Econometrics, Vol. 16), Emerald Group Publishing Limited, Bingley, pp. 141-164.



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