Back in March, as part of its Spring Privacy Series, the Federal Trade Commission (FTC) held a workshop exploring alternative methods of scoring. DMA’s own Rachel Nyswander Thomas, vice president of government affairs, presented at the FTC workshop, aiming to educate and guide FTC regulators on how marketing and predictive analytics has been a core part of the marketing ecosystem – and adding value to consumers – for decades.

DMA today submitted comments to the FTC on alternative methods of scoring, making clear that the U.S. has a long history of predictive analytics in marketing; highlighting the value and beneficial uses of marketing predictive analytics; reinforcing the difference between marketing predictive analytics and “scoring;” and endorsing the current flexible framework of sectoral laws and industry self-regulation as protecting consumers while promoting innovation.
The following are highlights from DMA’s comments:

Marketers have engaged in the responsible collection and use of data for marketing purposes for more than 100 years. Marketers have long derived value from data using predictive analytics, a process that enables marketers to provide more relevant and interesting ads or offers to consumers. Predictive analytics are used to predict a consumer’s likelihood of being interested in a product or service, to develop new and innovative products and services, to enhance the consumer experience by delivering relevant content, and to prevent fraud and provide secure transactions.

The marketing uses of predictive analytics are wholly distinct from the way that “credit scores” are used to establish a consumer’s eligibility for purposes including credit worthiness, insurance, or employment. In contrast to these eligibility determinations, predictive analytics use propensity modeling to make the best guess about what consumers may be interested in. The results of predictive analytics are beneficial—the delivery of a marketing offer that has a greater chance of being relevant and interesting to a consumer. Another beneficial application of predictive analytics is in the context of security. Predictive analytics can help turn raw transactional and other data into useful information used to prevent fraud and promote increased consumer safety.

DMA commended the FTC for their continued efforts to identify concrete harms to consumers, including the scheduling of a public workshop focused on both the positive and negative impacts of predictive analytics on low income and underserved consumers. Only last week, the FTC announced two enforcement actions that illustrate the continued effectiveness of the current sectoral and self-regulatory framework. The Commission reached settlements with two data companies regarding potential violations of the Fair Credit Reporting Act, based on the potential harm their data sales practices may have caused to customers.

Given that a robust and successful framework of protections already govern the use of data for marketing purposes, DMA encouraged the FTC to continue the U.S. tradition of focusing on discernible, concrete harms to consumers when considering the use of consumer data in the commercial sphere. The remarkable growth of the Data Driven Marketing Economy (DDME) has been possible in part because a flexible framework of legal and self-regulatory protections currently governs the responsible use of data for marketing purposes. This robust framework of sectoral laws and self-regulatory codes protects consumers while allowing responsible data use to drive innovation, fuel the U.S. economy, create jobs, and deliver significant value to American consumers., The framework combines specific legal restrictions, which focus on concrete harms that can flow from the potential misuse of data, with enforceable industry self-regulation that responds to a rapidly changing business landscape. According to a recent study, the resulting DDME added $156 billion in revenue to the U.S. economy and fueled more than 675,000 jobs in 2012 alone.

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