Data-Driven Pricing: Key Technologies, Business Practices, and Policy Implications

In the U.S., state lawmakers are seeking to regulate various pricing strategies that fall under the umbrella of “data-driven pricing”: practices that use personal and/or non-personal data to continuously inform decisions about the prices and products offered to consumers. Using a variety of terms—including “surveillance,” “algorithmic,” and “personalized” pricing—legislators are targeting a range of practices that often look different from one another, and carry different benefits and risks. Generally speaking, these practices fall under one of four categories:

• Reward or loyalty program: A company offers a discount, reward, or other incentive to repeat customers who sign up for the program. In return, the company receives additional customer data.
• Dynamic pricing: Rapidly changing the price of a particular product or service based on real-time analysis of market conditions and consumer behavior.
• Consumer segmentation or profiling: A profile is created for a customer based on their personal data, including behavior and/or characteristics, and they are placed within a particular audience segment. Based on the profile or segment, they receive particular advertisements, prices, or promotions.
• Search or product ranking: Altering the order in which search results or products appear, to give more prominence to certain results, based on general consumer data or specific customer behavioral data. This could potentially include changing the prominence of given products based on their price.

This resource distinguishes between these different pricing strategies in order to help lawmakers, businesses, and consumers better understand how these different practices work.