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
Language
English
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