Core Concepts
The authors present near-optimal algorithms for profit-maximization in targeted marketing, focusing on price and ancillary variables to maximize gross profit.
Abstract
The study explores sequential profit-maximization in targeted marketing, optimizing price and marketing expenditures. It introduces algorithms with regret bounds for different demand curve scenarios, providing insights into revenue maximization strategies.
The research delves into the challenges of non-anonymous pricing in multi-dimensional profit maximization problems. It highlights the impact of advertising elasticity on demand curves and proposes efficient algorithms for optimal pricing strategies. The study also addresses variants of the targeted marketing problem, such as subscription models and promotional credit scenarios.
By formalizing the bandit-learning model, the authors develop algorithms under monotonic and cost-concave demand assumptions. They prove regret bounds matching theoretical upper and lower limits, showcasing the effectiveness of their approach. The research emphasizes exploiting problem structures to optimize revenue while minimizing costs across multiple markets.
Stats
We prove a regret upper bound of O(nT^3/4) for monotonic demand curves.
A regret bound of O(nT^2/3) is established for cost-concave demands.
Quotes
"In reality, however, a firm will have only noisy, incomplete information about the demand curve."
"Moreover, different markets respond differently to advertising and/or price."