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http://hdl.handle.net/10397/89020
Title: | Multiple resource allocation for precision marketing | Authors: | Zhang, S Liao, P Ye, HQ Zhou, Z |
Issue Date: | 2020 | Source: | Journal of physics. Conference series, 2020, v. 1592, no. 1, 12034, p. 1-15 | 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 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. | Publisher: | Institute of Physics Publishing | Journal: | Journal of physics. Conference series | ISSN: | 1742-6588 | EISSN: | 1742-6596 | DOI: | 10.1088/1742-6596/1592/1/012034 | Description: | 3rd International Conference on Physics, Mathematics and Statistics, ICPMS 2020, 20-22 May 2020 | Rights: | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. The following publication Siyu Zhang et al 2020 J. Phys.: Conf. Ser. 1592 012034 is available at https://dx.doi.org/10.1088/1742-6596/1592/1/012034 |
Appears in Collections: | Conference Paper |
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