Dynamic Pricing and Advertising with Demand Learning

[2304.14385] Dynamic Pricing and Advertising with Demand Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Science and Game Theory arXiv:2304.14385 (cs) [Submitted on 27 Apr 2023 ( v1 ), last revised 15 Aug 2026 (this version, v4)] Title: Dynamic Pricing and Advertising with Demand Learning Authors: Shipra Agrawal , Yiding Feng , Wei Tang View a PDF of the paper titled Dynamic Pricing and Advertising with Demand Learning, by Shipra Agrawal and 2 other authors View PDF Abstract: We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product. We impose no structural restriction on the seller's feasible advertising strategies and allow her to advertise the product by disclosing or concealing any information. Following the information design literature, we model this fully flexible advertising as the seller choosing an arbitrary information policy that signals the product quality to customers. Customers observe the advertising signal and form a Bayesian posterior belief over the product quality. We investigate two questions in this work: (1) What is the value of advertising? To what extent can advertising enhance a seller's revenue? (2) Without any a priori knowledge of the customers' demand function, how can a seller adaptively learn and optimize both pricing and advertising strategies using past purchase responses? To study the first question, we quantify the value of advertising by comparing the optimal revenue from jointly designing advertising and a single posted price with the optimal revenue from pricing alone. We show that advertising can increase revenue by at most a factor of two, and this bound is tight. For the second question, we study the seller's dynamic pricing and advertising problem under demand uncertainty. Our main result for this question is a computationally efficient online algorithm that achieves the optimal $O(T^{2/3} (m\log T)^{1/3})$ regret rate when the valuation function is linear in the product quality. Here, $m$ is the cardinality of the discrete product quality domain and $T$ is the time horizon. Comments: Updated new versions, include more results Subjects: Computer Science and Game Theory (cs.GT) ; Machine Learning (cs.LG) Cite as: arXiv:2304.14385 [cs.GT] (or arXiv:2304.14385v4 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2304.14385 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wei Tang [ view email ] [v1] Thu, 27 Apr 2023 17:52:06 UTC (99 KB) [v2] Sun, 10 Dec 2023 22:39:36 UTC (107 KB) [v3] Wed, 11 Dec 2024 15:22:52 UTC (1,701 KB) [v4] Sat, 15 Aug 2026 01:25:52 UTC (2,170 KB) Full-text links: Access Paper: View a PDF of the paper titled Dynamic Pricing and Advertising with Demand Learning, by Shipra Agrawal and 2 other authors View PDF TeX Source view license Current browse context: cs.GT < prev | next > new | recent | 2023-04 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising#665519
• Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising#664019
• Ad-verse Effects: Pharmaceutical Advertising Shifts Drug Recommendations by Consumer-Facing AI#189378
• The Dimensions of Reputation in Electronic Markets#941646
• Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search#822067