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. https://doi.org/10.1016/S0731-9053(02)16007-5
Emerald Group Publishing Limited
Copyright © 2002, Emerald Group Publishing Limited