Dynamic Pricing and Advertising with Demand Learning

• URL: http://arxiv.org/abs/2304.14385v3
• Date: Wed, 11 Dec 2024 15:22:52 GMT
• Title: Dynamic Pricing and Advertising with Demand Learning
• Authors: Shipra Agrawal, Yiding Feng, Wei Tang,
• Abstract summary: We consider a novel pricing and advertising framework, where a seller not only sets product price but also designs flexible'advertising schemes'<n>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.<n>Customers observe the advertising signal and infer a Bayesian belief over the products.
• Score: 16.54088382906195
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We consider a novel pricing and advertising framework, where a seller not only sets product price but also designs flexible 'advertising schemes' to influence customers' valuation 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 literature in information design, this fully flexible advertising can be modeled as the seller being able to choose any information policy that signals the product quality/characteristic to the customers. Customers observe the advertising signal and infer a Bayesian belief over the products. We aim to 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 apriori 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 introduce and study the value of advertising - a revenue gap between using advertising vs not advertising, and we provide a crisp tight characterization for this notion for a broad family of problems. For the second question, we study the seller's dynamic pricing and advertising problem with demand uncertainty. Our main result for this question is a computationally efficient online algorithm that achieves an 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. This result requires some mild regularity assumptions on the valuation function, but no Lipschitz or smoothness assumption on the customers' demand function. We also obtain several improved results for the widely considered special case of additive valuations.

Related papers

• Procurement Auctions via Approximately Optimal Submodular Optimization [53.93943270902349]
  We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. Our goal is to design computationally efficient auctions that maximize the difference between the quality of the acquired services and the total cost of the sellers.
  arXiv  Detail & Related papers  (2024-11-20T18:06:55Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Improved Algorithms for Contextual Dynamic Pricing [24.530341596901476]
  In contextual dynamic pricing, a seller sequentially prices goods based on contextual information. Our algorithm achieves an optimal regret bound of $tildemathcalO(T2/3)$, improving the existing results. For this model, our algorithm obtains a regret $tildemathcalO(Td+2beta/d+3beta)$, where $d$ is the dimension of the context space.
  arXiv  Detail & Related papers  (2024-06-17T08:26:51Z)
• Dynamic Pricing and Learning with Long-term Reference Effects [16.07344044662994]
  We study a simple and novel reference price mechanism where reference price is the average of the past prices offered by the seller. We show that under this mechanism, a markdown policy is near-optimal irrespective of the parameters of the model. We then consider a more challenging dynamic pricing and learning problem, where the demand model parameters are apriori unknown.
  arXiv  Detail & Related papers  (2024-02-19T21:36:54Z)
• Pricing with Contextual Elasticity and Heteroscedastic Valuation [23.96777734246062]
  We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity. Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.
  arXiv  Detail & Related papers  (2023-12-26T11:07:37Z)
• A Multimodal Analysis of Influencer Content on Twitter [40.41635575764701]
  Line between personal opinions and commercial content promotion is frequently blurred. This makes automatic detection of regulatory compliance breaches related to influencer advertising difficult. We introduce a new Twitter (now X) dataset consisting of 15,998 influencer posts mapped into commercial and non-commercial categories.
  arXiv  Detail & Related papers  (2023-09-06T15:07:23Z)
• Contextual Dynamic Pricing with Strategic Buyers [93.97401997137564]
  We study the contextual dynamic pricing problem with strategic buyers. Seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. We propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue.
  arXiv  Detail & Related papers  (2023-07-08T23:06:42Z)
• Autoregressive Bandits [58.46584210388307]
  We propose a novel online learning setting, Autoregressive Bandits, in which the observed reward is governed by an autoregressive process of order $k$. We show that, under mild assumptions on the reward process, the optimal policy can be conveniently computed. We then devise a new optimistic regret minimization algorithm, namely, AutoRegressive Upper Confidence Bound (AR-UCB), that suffers sublinear regret of order $widetildemathcalO left( frac(k+1)3/2sqrtnT (1-G
  arXiv  Detail & Related papers  (2022-12-12T21:37:36Z)
• Phase Transitions in Learning and Earning under Price Protection Guarantee [4.683806391173103]
  We study the impact of such policy on the design of online learning algorithm for data-driven dynamic pricing. We show that the optimal regret is $tildeTheta(sqrtT+minM,,T2/3)$ by first establishing a fundamental impossible regime. We propose LEAP, a phased exploration type algorithm for underlineLearning and underlineEArning under underlinePrice Protection.
  arXiv  Detail & Related papers  (2022-11-03T13:36:00Z)
• A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design [158.0041488194202]
  We study reserve price optimization in multi-phase second price auctions. From the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders. Third, the seller's per-step revenue is unknown, nonlinear, and cannot even be directly observed from the environment.
  arXiv  Detail & Related papers  (2022-10-19T03:49:05Z)
• Incrementality Bidding and Attribution [0.4511923587827302]
  In digital advertising three major puzzle pieces are central to quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. We propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution.
  arXiv  Detail & Related papers  (2022-08-25T18:33:08Z)
• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
  We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
  arXiv  Detail & Related papers  (2022-07-25T14:37:18Z)
• Aspect-based Analysis of Advertising Appeals for Search Engine Advertising [37.85305426549587]
  We focus on exploring the effective A$3$ for different industries with the aim of assisting the ad creation process. Our experiments demonstrated that different industries have their own effective A$3$ and that the identification of the A$3$ contributes to the estimation of advertising performance.
  arXiv  Detail & Related papers  (2022-04-25T05:31:07Z)
• Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise [16.871660060209674]
  We show an algorithm that achieves an $tildeO(Tfrac34)$ regret, and improve the best-known lower bound from $Omega(Tfrac35)$ to $tildeOmega(Tfrac23)$. Results demonstrate that no-regret learning is possible for feature-based dynamic pricing under weak assumptions.
  arXiv  Detail & Related papers  (2022-01-27T06:40:03Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Online-to-Offline Advertisements as Field Experiments [0.17877823660518105]
  We study the difference in offline behavior between customers who received online advertisements and regular customers. We then find a long-run effect of this externality of advertising that a certain portion of the customers invited to the offline shops revisit these shops.
  arXiv  Detail & Related papers  (2020-10-18T22:04:56Z)
• Learning to Infer User Hidden States for Online Sequential Advertising [52.169666997331724]
  We propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states)
  arXiv  Detail & Related papers  (2020-09-03T05:12:26Z)
• A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction [41.000912016821246]
  We propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. Our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment.
  arXiv  Detail & Related papers  (2020-08-20T15:08:50Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)
• Do Interruptions Pay Off? Effects of Interruptive Ads on Consumers Willingness to Pay [79.9312329825761]
  We present the results of a study designed to measure the impact of interruptive advertising on consumers willingness to pay for products bearing the advertiser's brand. Our results contribute to the research on the economic impact of advertising, and introduce a method of measuring actual (as opposed to self-reported) willingness to pay in experimental marketing research.
  arXiv  Detail & Related papers  (2020-05-14T09:26:57Z)
• Online Learning with Imperfect Hints [72.4277628722419]
  We develop algorithms and nearly matching lower bounds for online learning with imperfect directional hints. Our algorithms are oblivious to the quality of the hints, and the regret bounds interpolate between the always-correlated hints case and the no-hints case.
  arXiv  Detail & Related papers  (2020-02-11T23:06:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.