Title

Targeting high value customers while under resource constraint : partial order constrained optimization with genetic algorithm

Document Type

Journal article

Source Publication

Journal of Interactive Marketing

Publication Date

2-1-2015

Volume

29

Issue

1

First Page

27

Last Page

37

Abstract

To maximize sales or profit given a fixed budget, direct marketing targets a preset top percentage of consumers who are the most likely to respond and purchase a greater amount. Existing forecasting models, however, largely ignore the resource constraint and render sup-optimal performance in maximizing profit given the budget constraint. This study proposes a model of partial order constrained optimization (POCO) using a penalty weight that represents the marginal penalty for selecting one more customer. Genetic algorithms as a tool of stochastic optimization help to select models that maximize the total sales at the top deciles of a customer list. The results of cross-validation with a direct marketing dataset indicate that the POCO model outperforms the competing methods in maximizing sales under the resource constraint and has distinctive advantages in augmenting the profitability of direct marketing.

DOI

10.1016/j.intmar.2014.09.001

Print ISSN

10949968

E-ISSN

15206653

Publisher Statement

Copyright © 2014 Published by Elsevier Inc

Access to external full text or publisher's version may require subscription.

Full-text Version

Publisher’s Version

Recommended Citation

Cui, G., Wong, M. L., & Wan, X. (2015). Targeting high value customers while under resource constraint: Partial order constrained optimization with genetic algorithm. Journal of Interactive Marketing, 29(1), 27-37. doi: 10.1016/j.intmar.2014.09.001