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2339. Data-driven advertising policy on e-commerce platform with budget constraint
Invited abstract in session MD-59: Customer behaviour, stream Pricing and Revenue Management.
Monday, 14:30-16:00Room: S08 (building: 101)
Authors (first author is the speaker)
| 1. | Yunzhi Cao
|
| City University of Hong Kong | |
| 2. | Houmin Yan
|
| City University of Hong Kong | |
| 3. | Yedong Wang
|
| The Lab of AI Powered Fi nancial Technologies, Hong Kong |
Abstract
Advertising is one of the crucial tools for retailers on the e-commerce platform for promotion, such as Amazon. This study focuses on the joint bidding and pricing policy for retailers subject to budget limitation under multiple periods. The bidding is for the impression on the sponsored advertising list. The click-through rate and conversion rate functions with respect to bid and price are learned and estimated based on historical data. We formulate a stochastic model and construct a dynamic programming formulation. We prove the existence of the unique optimal solution and characterize the structure of an optimized bidding and pricing policy. Through theoretical analysis and a case study, we investigate the relationships among the optimal policy, value function, and key factors, and demonstrate the effectiveness of the optimal policy and the proposed dynamic model.
Keywords
- E-Commerce
- Programming, Stochastic
- Revenue Management and Pricing
Status: accepted
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