Summary of Bandit Profit-maximization For Targeted Marketing, by Joon Suk Huh et al.
Bandit Profit-maximization for Targeted Marketing
by Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); General Economics (econ.GN); General Finance (q-fin.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to optimize profits in a sequential market scenario where firms must balance price and ancillary variables like marketing expenditures. Specifically, it tackles the challenge of maximizing profit over multiple demand curves that depend on distinct ancillary variables while maintaining the same price across all markets. The problem is motivated by scenarios like targeted marketing, where firms invest different marketing efforts to acquire customers in various markets. The authors’ objective is to maximize gross profit, which is revenue minus marketing costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies a sequential profit-maximization problem that involves optimizing for both price and ancillary variables. It aims to maximize profit over multiple demand curves that depend on distinct ancillary variables while maintaining the same price across all markets. The authors consider scenarios like targeted marketing, where firms invest different marketing efforts to acquire customers in various markets. |
