Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm

Geng Cui*, Man Leung Wong, Xiang WAN

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)27-37
Number of pages11
JournalJournal of Interactive Marketing
Volume29
Issue numberC
DOIs
Publication statusPublished - 2015

Scopus Subject Areas

  • Business and International Management
  • Marketing

User-Defined Keywords

  • Constrained optimization
  • Direct marketing
  • Genetic algorithms
  • Partial order function
  • Profit maximization
  • Return on investment

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