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Dynamic Marketing Resource Allocation with Two-Stage Decisions
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Siyu Liao, Peng Ye, Heng-Qing Zhou, Zhili |
| Copyright Year | 2022 |
| Description | 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. |
| Ending Page | 344 |
| Page Count | 18 |
| Starting Page | 327 |
| e-ISSN | 07181876 |
| DOI | 10.3390/jtaer17010017 |
| Journal | Journal of theoretical and applied electronic commerce research |
| Issue Number | 1 |
| Volume Number | 17 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-09 |
| Access Restriction | Open |
| Subject Keyword | Journal of theoretical and applied electronic commerce research Journal of Theoretical and Applied Electronic Commerce Research Information and Library Science Marketing Strategy Resource Allocation Machine Learning Small Businesses Online Learning |
| Content Type | Text |
| Resource Type | Article |
