Optimizing Item-based Marketing Promotion Efficiency in C2C Marketplace with Dynamic Sequential Coupon Allocation Framework
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917775145959424 |
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| author | Yang, Jie Sekhar, Padunna Valappil Krishnaraj Sekine, Sho Li, Yilin |
| author_facet | Yang, Jie Sekhar, Padunna Valappil Krishnaraj Sekine, Sho Li, Yilin |
| contents | In e-commerce platforms, coupons play a crucial role in boosting transactions. In the customer-to-customer (C2C) marketplace, ensuring the satisfaction of both buyers and sellers is essential. While buyer-focused marketing strategies often receive more attention, addressing the needs of sellers is equally important. Additionally, the existing strategies tend to optimize each promotion independently, resulting in a lack of continuity between promotions and unnecessary costs in the pursuit of short-term impact within each promotion period.
We introduce a Dynamic Sequential Coupon Allocation Framework (DSCAF) to optimize item coupon allocation strategies across a series of promotions. DSCAF provides sequential recommendations for coupon configurations and timing to target items. In cases where initial suggestions do not lead to sales, it dynamically adjusts the strategy and offers subsequent solutions. It integrates two predictors for estimating the sale propensity in the current and subsequent rounds of coupon allocation, and a decision-making process to determine the coupon allocation solution. It runs iteratively until the item is sold. The goal of the framework is to maximize Return on Investment (ROI) while ensuring lift Sell-through Rate (STR) remains above a specified threshold. DSCAF aims to optimize sequential coupon efficiency with a long-term perspective rather than solely focusing on the lift achieved in each individual promotion. It has been applied for item coupon allocation in Mercari. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Item-based Marketing Promotion Efficiency in C2C Marketplace with Dynamic Sequential Coupon Allocation Framework Yang, Jie Sekhar, Padunna Valappil Krishnaraj Sekine, Sho Li, Yilin Machine Learning In e-commerce platforms, coupons play a crucial role in boosting transactions. In the customer-to-customer (C2C) marketplace, ensuring the satisfaction of both buyers and sellers is essential. While buyer-focused marketing strategies often receive more attention, addressing the needs of sellers is equally important. Additionally, the existing strategies tend to optimize each promotion independently, resulting in a lack of continuity between promotions and unnecessary costs in the pursuit of short-term impact within each promotion period. We introduce a Dynamic Sequential Coupon Allocation Framework (DSCAF) to optimize item coupon allocation strategies across a series of promotions. DSCAF provides sequential recommendations for coupon configurations and timing to target items. In cases where initial suggestions do not lead to sales, it dynamically adjusts the strategy and offers subsequent solutions. It integrates two predictors for estimating the sale propensity in the current and subsequent rounds of coupon allocation, and a decision-making process to determine the coupon allocation solution. It runs iteratively until the item is sold. The goal of the framework is to maximize Return on Investment (ROI) while ensuring lift Sell-through Rate (STR) remains above a specified threshold. DSCAF aims to optimize sequential coupon efficiency with a long-term perspective rather than solely focusing on the lift achieved in each individual promotion. It has been applied for item coupon allocation in Mercari. |
| title | Optimizing Item-based Marketing Promotion Efficiency in C2C Marketplace with Dynamic Sequential Coupon Allocation Framework |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.08609 |