Dynamic Marketing Resource Allocation with Two-Stage Decisions

Siyu Zhang, Peng Liao, Heng Qing Ye, Zhili Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In the precision marketing of a new product, it is a challenge to allocate limited resources to the target customer groups with different characteristics. We presented a framework using the distance-based algorithm, K-nearest neighbors, and support vector machine to capture customers’ preferences toward promotion channels. Additionally, online learning programming was combined with machine learning strategies to fit a dynamic environment, evaluating its performance through a parsimonious model of minimum regret. A resource optimization model was proposed using classification results as input. In particular, we collected data from an institution that provides financial credit products to capital-constrained small businesses. Our sample contained 525,919 customers who will be introduced to a new product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected borrowers when KNN was performed with an optimal resource allocation strategy. Our results also show that KNN is the most stable method to perform classification and that the distance-based algorithm has the most efficient adoption with online learning.

Original languageEnglish
Pages (from-to)327-344
Number of pages18
JournalJournal of Theoretical and Applied Electronic Commerce Research
Volume17
Issue number1
DOIs
Publication statusPublished - Mar 2022

Keywords

  • machine learning
  • marketing strategy
  • online learning
  • resource allocation
  • small businesses

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Computer Science Applications

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