Multiple resource allocation for precision marketing

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

Research output: Journal article publicationConference articleAcademic researchpeer-review

10 Citations (Scopus)


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 distance-based algorithm, K-Nearest-Neighbour, and support vector machine to capture customers' preference towards promotion channel. Additionally, on-line 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 a loan agency that offers loans to small business merchants. Our sample contained 525,919 customers who will be introduced to a new financial product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected merchants when K-NN was performed with optimal resource allocation strategy. Our results also show that K-NN is the most stable method to perform classification, and that distance-based algorithm has the most efficient adoption with on-line learning.

Original languageEnglish
Article number012034
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 18 Sept 2020
Event3rd International Conference on Physics, Mathematics and Statistics, ICPMS 2020 - Kunming, China
Duration: 20 May 202022 May 2020

ASJC Scopus subject areas

  • General Physics and Astronomy


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