The way prices are set is changing: more accessible data, sophisticated algorithms, and ubiquitous online shopping have given retailers the ability to automatically tailor offers to customers in real-time or near-real-time based on increasing amounts of data about markets and consumers. A number of pricing strategies involving personal data, market data, and advanced machine learning—what this resource refers to collectively as “data-driven pricing”—have recently become common marketing practice. While data-driven pricing is often deployed to attract, retain, or reward customers, it can also provide retailers with insights that could be used to individualize prices in ways that average consumers might find unexpected or unfair, or that cause unintended disparities across groups. For these reasons, data-driven pricing has become the subject of increasing scrutiny from civil society, lawmakers, and enforcers in the United States.

This resource provides an overview of how data is used to inform pricing; contextualizes data-driven pricing in existing U.S. law, enforcement activity, and emerging legislation; and recommends a number of best practices for guiding retail and e-commerce platforms in using data responsibly when it affects pricing. These practical recommendations, developed in consultation with companies working to build trustworthy pricing practices, are aligned with how leading organizations have built robust, responsible AI Governance programs based on frameworks like National Institute of Standards and Technology (NIST)’s AI Risk Management Framework (AI RMF). 

Recommendations include:

• Map and track the collection and use of all data that informs consumer pricing over time, including data sources and provenance. 

• Rigorously test all relevant datasets and pricing algorithms for bias.

• Establish clear internal policies around what data types and uses of data are permitted for informing consumer prices, based on an analysis of fairness, context, and consumer expectations.

• Provide clear disclosures to consumers about how data informs pricing, and how personal data may inform personalized offers.

• Ensure that personalized discounts exist in relation to real “baseline” prices. 

• Implement stronger safeguards around data-driven pricing for essential products.

• Ensure alignment on data use policies when partnering with pricing algorithm vendors.