Secret Consumer Scores and Segmentations: Separating Consumer 'Haves' from 'Have-Nots'

Abstract

“Big Data” is big business. Data brokers profit by tracking consumers’ information and behavior both on- and offline and using this collected data to assign consumers evaluative scores and classify consumers into segments. Companies then use these consumer scores and segmentations for marketing and to determine what deals, offers, and remedies they provide to different individuals. These valuations and classifications are based on not only consumers’ financial histories and relevant interests, but also their race, gender, ZIP Code, social status, education, familial ties, and a wide range of additional data. Nonetheless, consumers are largely unaware of these scores and segmentations, and generally have no way to challenge their veracity because they usually fall outside the purview of the Fair Credit Reporting Act (FCRA). Moreover, companies’ use of these data devices may foster discrimination and augment preexisting power imbalances among consumers by funneling the best deals and remedies to the wealthiest and most sophisticated consumers. Use of these scores and segmentations increases the growing gap between powerful “haves” and vulnerable “have-nots.” This Article sheds light on these data devices and aims to spark adoption of data privacy regulations that protect all consumers regardless of their educational, economic, ethnic, or social status.

Keywords: contracts, consumer protection, e-contracts, consumer contracts, Big Data, data privacy, data protection, FCRA, debt, Fair Credit Reporting Act, discrimination, consumer rights, consumer law, privacy, marketing

JEL Classification: K12, K40, K41, K00, K29, K20, K30, K10, K39, M31, D11, O31, D40