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FTC Urged To Rule On Legality Of 'Secret Surveillance Scores' Used To Vary Prices By Each Online Shopper

Nobody wants to pay too much for a product. If you like online shopping, you may have been charged higher prices than your neighbors. Gizmodo reported:

"... researchers have documented and studied the use of so-called "surveillance scoring," the shadowy, but widely adopted practice of using computer algorithms that, in commerce, result in customers automatically paying different prices for the same product. The term also encompasses tactics used by employers and landlords to deny applicants jobs and housing, respectively, based on suggestions an algorithm spits out. Now experts allege that much of this surveillance scoring behavior is illegal, and they’re are asking the Federal Trade Commission (FTC) to investigate."

"In a 38-page petition filed last week, the Consumer Education Foundation (CEF), a California nonprofit with close ties to the group Consumer Watchdog, asked the FTC to explore whether the use of surveillance scores constitute “unfair or deceptive practices” under the Federal Trade Commission Act..."

The petition is part of a "Represent Consumers" (RC) program.

Many travelers have experienced dynamic pricing, where airlines vary fares based upon market conditions: when demand increases, prices go up; when demand decreases, prices go down. Similarly, when there are many unsold seats (e.g., plenty of excess supply), prices go down. But that dynamic pricing does not vary for each traveler.

Pricing by each person raises concerns of price discrimination. The legal definition of price discrimination in the United States:

"A seller charging competing buyers different prices for the same "commodity" or discriminating in the provision of "allowances" — compensation for advertising and other services — may be violating the Robinson-Patman Act... Price discriminations are generally lawful, particularly if they reflect the different costs of dealing with different buyers or are the result of a seller's attempts to meet a competitor's offering... There are two legal defenses to these types of alleged Robinson-Patman violations: (1) the price difference is justified by different costs in manufacture, sale, or delivery (e.g., volume discounts), or (2) the price concession was given in good faith to meet a competitor's price."

Airlines have wanted to extend dynamic pricing to each person, and "surveillance scores" seem perfectly suited for the task. The RC petition is packed with information which is instructive for consumers to learn about the extent of the business practices. First, the petition described the industry involved:

"Surveillance scoring starts with "analytics companies," the true number of which is unknown... these firms amass thousands or even tens of thousands of demographic and lifestyle data points about consumers, with the help of an estimated 121 data brokers and aggregators... The analytics firms use algorithms to categorize, grade, or assign a numerical value to a consumer based on the consumer’s estimated predicted behavior. That score then dictates how a company will treat a consumer. Consumers deemed to be less valuable are treated poorly, while consumers with better “grades” get preferential treatment..."

Second, the RC petition cited a study which identified 44 different types of proprietary surveillance scores used by industry participants to predict consumer behavior. Some of the score types (emphasis added):

"The Medication Adherence Score, which predicts whether a consumer is likely to follow a medication regimen; The Health Risk Score, which predicts how much a specific patient will cost an insurance company; The Consumer Profitability Score, which predicts which households may be profitable for a company and hence desirable customers; The Job Security Score, which predicts a person’s future income and ability to pay for things; The Churn Score, which predicts whether a consumer is likely to move her business to another company; The Discretionary Spending Index, which scores how much extra cash a particular consumer might be able to spend on non-necessities; The Invitation to Apply Score, which predicts how likely a consumer is to respond to a sales offer; The Charitable Donor Score, which predicts how likely a household is to make significant charitable donations; and The Pregnancy Predictor Score, which predicts the likelihood of someone getting pregnant."

It is important to note that the RC petition does not call for a halt in the collection of personal data about consumers. Rather, it asks the FTC, "to investigate and prohibit the targeting of consumers’ private data against them after it has been collected." Clarity is needed about what is, and is not, legal when consumers' personal data is used against them.

Third, the RC petition also cited published studies about pricing discrimination:

"An early seminal study of price discrimination published by researchers at Northeastern University in 2014 (Northeastern Price Discrimination Study) examined the pricing practices of e-commerce websites. The researchers developed a software-based methodology for measuring price discrimination and tested it with 300 real-world users who shopped on 16 popular e-commerce websites.37 Of ten different general retailers tested in 2014, only one –- Home Depot –- was confirmed to be engaging in price discrimination. Home Depot quoted prices to mobile-device users that were approximately $100 more than those quoted to desktop users.39 The researchers were unable to ascertain why... The Northeastern Price Discrimination Study also found that “human shoppers got worse bargains on a number of websites,”compared to an automated shopping browser that did not have any personal data trail associated with it,42 validating that Home Depot was considering shoppers’ personal data when setting prices online."

So, concerns about price discrimination aren't simply theory. Related to that, the RC petition cited its own research:

"... researchers at Northeastern University developed an online tool to “expose how websites personalize prices.” The Price Discrimination Tool (PDT) is a plug-in extension used on the Google Chrome browser that allows any Internet user to perform searches on five websites to see if the user is being charged a different price based on whatever information the companies have about that particular user. The PDT uses a remote computer server that is anonymous –- it has no personal data profile... The PDT then displays the price results from the human shopper’s search and those obtained by the remote anonymous computer server. Our own testing using the PDT revealed that Home Depot continues to offer different prices to human shoppers. For example, a search on Home Depot’s website for “white paint” reveals price discrimination. Of the 24 search results on the first page, Home Depot quoted us higher prices for six tubs of white paint than it quoted the anonymous computer... Our testing also revealed similar price discrimination on Home Depot’s website for light bulbs, toilet paper, toilet paper holders, caulk guns, halogen floor lamps and screw drivers... We also detected price discrimination on Walmart’s website using the PDT. Our testing revealed price discrimination on Walmart’s website for items such as paper towels, highlighters, pens, paint and toilet paper roll holders."

The RC petition listed examples: the Home Depot site quoted $59.87 for a five-gallon bucket of paint to the anonymous user, and $62.96 for the same product to a researcher. Another example: the site quoted $10.26 for a toilet-paper holder to the anonymous user, and $20.89 for the same product to a researcher -- double the price. Prices differences per person ranged from small to huge.

Besides concerns about price discrimination, the RC petition discussed "discriminatory customer service," and the data analytics firms allegedly involved:

"Zeta Global sells customer value scores that will determine, among other things, the quality of customer service a consumer receives from one of Zeta’s corporate clients. Zeta Global “has a database of more than 700 million people, with an average of over 2,500 pieces of data per person,” from which it creates the scores. The scores are based on data “such as the number of times a customer has dialed a call center and whether that person has browsed a competitor’s website or searched certain keywords in the past few days.” Based on that score, Zeta will recommend to its clients, which include wireless carriers, whether to respond to one customer more quickly than to others.

"Kustomer Inc.: Customer-service platform Kustomer Inc. uses customer value scores to enable retailers and other businesses to treat customer service inquiries differently..."

"Opera Solutions: describes itself as a “a global provider of advanced analytics software solutions that address the persistent problem of scaling Big Data analytics.” Opera Solutions generates customer value scores for its clients (including airlines, retailers and banks)..."

The petition cited examples of "discriminatory customer service," which include denied product returns, or customers shunted to less helpful customer service options. Plus, there are accuracy concerns:

"Considering that credit scores – the existence of which has been public since 1970 – are routinely based on credit reports found to contain errors that harm consumers’ financial standing,31 it is highly likely that Secret Surveillance Scores are based on inaccurate or outdated information. Since the score and the erroneous data upon which it relies are secret, there is no way to correct an error,32 assuming the consumer was aware of it."

Regular readers of this blog are already aware of errors in reports from credit reporting agencies. A copy of the RC petition is also available here (Adobe PDF, 3.2 Mbytes).

What immediately becomes clear while reading the petition is that massive amount of personal data collected about consumers to create several proprietary scores. Consumers have no way of knowing nor challenging the accuracy of the scores when they are used against them. So, not only has an industry risen which profits by acquiring and then selling, trading, analyzing, and/or using consumers' data; there is little to no accountability.

In other words, the playing field is heavily tilted for corporations and against consumers.

This is also a reminder why telecommunications companies fought hard for the repeal of broadband privacy and repeal of net neutrality, both of which the U.S. Federal Communications Commission (FCC) provided in 2017 under the leadership of FCC Chairman Ajit Pai, a Trump appointee. Repeal of the former consumer protection allows unrestricted collection of consumers' data, plus new revenue streams to sell the data collected to analytics firms, data brokers, and business partners.

Repeal of the second consumer protection allows internet and cable providers to price content using whatever criteria they choose. You see a rudimentary version of this pricing in a business practice called "zero rating." An example: streaming a movie via a provider's internet service counts against a data cap while the same movie viewed through the same provider's cable subscription does not. Yet, the exact same movie is delivered through the exact same cable (or fiber) internet connection.

Smart readers immediately realize that a possible next step includes zero ratings per-person. Streaming a movie might count against your data cap but not for your neighbor. Who would know? Oversight and consumer protections are needed.

What are your opinions of secret surveillance scores?

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