It’s likely you’ve never heard of companies like Acxiom, Experian, or Datalogix. But they have heard about you.
These are the behind-the-scenes firms that collect disparate data about hundreds of millions of American consumers and sell that data to retailers, advertisers, credit agencies, and other businesses who want to know as much as possible about their target markets. They’ve operated with minimal scrutiny for years, but more recently have come under closer watch by Congress and federal regulators, especially as the multi-billion dollar industry is able to more easily harvest vast amounts of data by tracking people’s Internet and mobile activities.
“Beyond publicly available information such as home addresses and phone numbers, data brokers maintain data as specific as whether consumers view a high volume of YouTube videos, the type of car they drive, ailments they may have such as depression or diabetes, whether they are a hunter, what types of pets they have; or whether they have purchased a particular shampoo product in the last six months...”
For anyone who has followed the data tracking industry, this level of detail that anonymous firms aggregate may be disturbing, but it’s not actually all that shocking. In addition to actual data, they also sell modeled data--profiles of, say, whether someone is likely to own a SUV, based on all of what the firm knows about an individual and individuals similar to them.
What’s perhaps most concerning in the report is the way the firms then use this data to classify people into “buckets” and sell their profiles to third-parties.
Some buckets define the privileged and well-off, such as “Power Couples,” “Established Elite,” and “Just Sailing Along.” Others specifically are meant to target the financially and emotionally vulnerable, as you can see in the graphic above. Some examples include “Fragile Families,” “Zero Mobility,” “Ethnic Second-City Strugglers,” and “Living on Loans: Young Urban Single Parents.”
Experian, according to the Senate report, describes its category “Hard Times” as follows:
“Older, down-scale and ethnically-diverse singles typically concentrated in inner-city apartments...This is the bottom of the socioeconomic ladder, the poorest lifestyle segment in the nation. Hard Times are older singles in poor city neighborhoods. Nearly three-quarters of the adults are between the ages of 50 and 75; this is an underclass of the working poor and destitute seniors without family support….One-quarter of the households have at least one resident who is retired.”
The companies were generally vague or unresponsive about what is done with the data they sell, and the committee didn’t review whether these categories for financially vulnerable consumers were used in a harmful way. But they surmise that it’s a likely outcome. Precedent shows that “unscrupulous businesses” and companies that sell high-cost and payday loans often seek to take advantage of exactly these kinds of consumers.
Of course, the companies ensured that they had policies and contract clauses to exclude certain uses for the data. One specifically prohibited its use for “payday or short-term lending.” But for now, we’re mostly just taking their word for it. Acxiom wouldn’t get specific, but said its customers include 47 Fortune 100 clients, 12 of the top 15 credit card issuers, seven of the top 10 retailers, 11 of the top 14 automotive manufacturers, and five top life and health insurance providers, among others.
“Unfortunately, three of the largest companies--Acxiom, Experian, and Epsilon--to date have declined to disclose their customers to the Committee. As a result, the precise range and nature of their customer base remains unknown,” the report says.
All of this data-gathering may not be a big deal. But it might be hurting us if it’s inaccurate, contains private details we don't want to share, or is used unscrupulously. Government regulators are going to have to dig deeper, and this is one reason that privacy policies on the web and mobile need to change. People need to have a better idea of what they’re giving up when they check a box.
[Image: Abstract via Shutterstock]