Optimal Price Targeting

Smith, Adam N.; Seiler, Stephan; Aggarwal, Ishant

doi:10.1287/mksc.2022.1387

Cited by 19 publications

(4 citation statements)

References 54 publications

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“…1. Note that modeling the action A as a binary variable is consistent with previous literature (e.g., Kallus 2018, Kallus and Zhou 2018a, Athey and Wager 2021 and is common for decision-making in a wide range of practical applications such as, e.g., automated hiring, credit lending, and ad targeting (e.g., Smith et al 2022, Yoganarasimhan et al 2022, Kozodoi et al 2022.…”

Section: Standard Off-policy Learningsupporting

confidence: 72%

“…First, our work connects to off-policy learning (e.g.,Kallus 2018, Athey andWager 2021). While there is a growing body of literature that uses off-policy learning for managerial decision-making such as for pricing and ad targeting (e.g.,Smith et al 2022, Yoganarasimhan et al 2022, Yang et al 2023, we add by offering a new framework for off-policy learning with fairness guarantees. In particular, our work fills an important gap in the literature in that we are able to learn fair policies from biased observational data.…”

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confidence: 99%

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Fair Off-Policy Learning from Observational Data

Frauen¹,

Melnychuk²,

Feuerriegel³

2023

Preprint

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Businesses and organizations must ensure that their algorithmic decision-making is fair in order to meet legislative, ethical, and societal demands. For example, decision-making in automated hiring must not discriminate with respect to gender or race. To achieve this, prior research has contributed approaches that ensure algorithmic fairness in machine learning predictions, while comparatively little effort has focused on algorithmic fairness in decision models, specifically off-policy learning. In this paper, we propose a novel framework for fair off-policy learning: we learn decision rules from observational data under different notions of fairness, where we explicitly assume that observational data were collected under a different -potentially biased -behavioral policy. For this, we first formalize different fairness notions for off-policy learning. We then propose a machine learning approach to learn optimal policies under these fairness notions. Specifically, we reformulate the fairness notions into unconstrained learning objectives that can be estimated from finite samples. Here, we leverage machine learning to minimize the objective constrained on a fair representation of the data, so that the resulting policies satisfy our fairness notions. We further provide theoretical guarantees in form of generalization bounds for the finite-sample version of our framework. We demonstrate the effectiveness of our framework through extensive numerical experiments using both simulated and real-world data. As a result, our work enables algorithmic decision-making in a wide array of practical applications where fairness must ensured.

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Section: Standard Off-policy Learningsupporting

confidence: 72%

mentioning

confidence: 99%

Fair Off-Policy Learning from Observational Data

Frauen¹,

Melnychuk²,

Feuerriegel³

2023

Preprint

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show abstract

“…This practice enables the better extraction of consumer surplus (Wagner and Eidenmüller, 2019). Notably, recent studies by Shiller (2020), Dubé and Misra (2023), and Smith et al (2023) have provided quantitative evidence supporting the effectiveness of such practices. In this technology-driven market landscape, it is important to understand how these technologies and pricing schemes interact and their impact on welfare…”

Section: Introductionmentioning

confidence: 97%

Welfare Implications of Personalized Pricing in Competitive Platform Markets: The Role of Network Effects

Lu,

Matsushima,

Shekhar

2024

SSRN Journal

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“…For example, data analytics can be used to identify the most efficient way to transport food products from farms to processing plants to retail stores. Data analytics can be used to develop more targeted pricing strategies (Smith, Seiler, andAggarwal, 2023, Li andLi, 2023), such as personalized pricing and dynamic pricing. Personalized pricing involves setting different prices for different customers based on their individual characteristics, such as their purchase history and their loyalty status.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of the Role of Data Analytics in Shaping Food Pricing Strategies in the United States: Historical Perspectives, Current Trends, and Future Projections

Blessing Otohan Irabor,

Adekunle Abiola Abdul,

Bankole Ibrahim Ashiwaju

et al. 2023

Comput. sci. IT res. j.

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This topic is designed as an extensive review that charts the historical evolution, current state, and future potential of data analytics in influencing food pricing strategies within the United States. It will encompass a thorough examination of how data analytics has transformed from basic statistical models to advanced AI and machine learning algorithms in the context of food pricing. The review will include a critical analysis of various case studies and models that have been employed in the food industry, assessing their impact on both market dynamics and consumer behavior. Furthermore, it will explore the challenges and ethical considerations surrounding data usage in pricing strategies, such as privacy concerns and market fairness. The future section will speculate on emerging trends and technologies that could further shape this field. This topic is intended to provide a holistic and in-depth perspective on the intersection of data science and food economics, highlighting its significance in the contemporary economic landscape of the U.S. Keywords: Data Analytics, Food Pricing Strategies, Trends, Future Projections Food Demand, Customer Segmentation, Supply Chain Optimization, Personalized Pricing, Dynamic Pricing, Food Fraud, Food Safety.

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Optimal Price Targeting

Abstract: The paper compares the profitability of personalized pricing policies that are generated from different models of demand and using different data inputs.

Cited by 19 publications

References 54 publications

Fair Off-Policy Learning from Observational Data

Fair Off-Policy Learning from Observational Data

Welfare Implications of Personalized Pricing in Competitive Platform Markets: The Role of Network Effects

A Comprehensive Review of the Role of Data Analytics in Shaping Food Pricing Strategies in the United States: Historical Perspectives, Current Trends, and Future Projections

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