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

Research output: Journal PublicationsJournal Article (refereed)

10 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 number1
Early online date11 Feb 2015
DOIs
Publication statusPublished - Feb 2015

Fingerprint

Targeting
Resource constraints
Direct marketing
Genetic algorithm
Constrained optimization
Customer value
Partial order
Profit
Penalty
Optimization model
Budget constraint
Profitability
Stochastic optimization
Cross-validation
Purchase

Keywords

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

Cite this

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title = "Targeting high value customers while under resource constraint : partial order constrained optimization with genetic algorithm",
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.",
keywords = "Direct marketing, Profit maximization, Partial order function, Constrained optimization, Genetic algorithms, Return on investment",
author = "Geng CUI and WONG, {Man Leung} and Xiang WAN",
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Targeting high value customers while under resource constraint : partial order constrained optimization with genetic algorithm. / CUI, Geng; WONG, Man Leung; WAN, Xiang.

In: Journal of Interactive Marketing, Vol. 29, No. 1, 02.2015, p. 27-37.

Research output: Journal PublicationsJournal Article (refereed)

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