Targeting High Value Customers While under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm. Issue 1 (February 2015)

• Record Type: Journal Article
• Authors: Cui, Geng; Wong, Man Leung; Wan, Xiang
• Is Part Of: Journal of interactive marketing. Volume 29:Issue 1(2015)
• Page Start: 27
• Page End: 37
• Publication Date: 2015-02
• Subjects: Direct marketing -- Profit maximization -- Partial order function -- Constrained optimization -- Genetic algorithms -- Return on investment; Direct marketing -- Periodicals; Internet marketing -- Periodicals; 658.87
• DOI: 10.1016/j.intmar.2014.09.001
• Languages: English
• ISSNs: 1094-9968

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.