Many retail pharmacies, supermarkets and other retailers offer rewards cards that offer discounts on certain items to be purchased in the store. They give you the cards that contain a bar code in regular size, which looks like a credit or debit card, or a handy miniature one you can put on your key ring so it’s always available. In order to sign up for the rewards card, you usually have to give some personal information such as an email address, a residential address or a telephone number. Once you give them your information, you can use the card and shop away and get coupons and small discounts on items you purchase.
When you sign up for a rewards card, there are no limitations on what the store can do with the information you provide them, including the personal information you provided to get the card, and details about each purchase you make. Why is this a privacy concern?
For some, it’s not. They are happy for the store to know exactly what they are purchasing and to sell that information to third parties. Others, like me, would rather not provide my personal information to the store or have them track my purchases and sell my purchases and preferences to others. Why? Because when stores like pharmacies, supermarkets and retailers are selling my personal information and exactly what I purchase to large scale marketing firms and data aggregators, they are aggregating the data that they get from other sources such as credit card companies, which are freely allowed to; sell and use every purchase you make with your credit card; financing companies; banking and loan information; retail gasoline purchases; publicly available information, including home assessment, car loans and assessments, information from social media sites such as LinkedIn and Facebook, education, age and gender; and are profiling me and you and predicting many things about us.
What does profiling and predictive modeling mean? When data aggregators get all of the data from the sources mentioned above, they are easily able to match it to an individual (including you and me) and they are able to use technology to determine and predict a person’s income, age, date of birth, address, how many homes are owned and how much money is owed on each home, how many cars are in the garage and outstanding loans on the cars, how many children they have and who those children are, what schools they go to, what grade appliances are in the kitchen, where they work, what they do for work, where they travel, when they are home and when they aren’t, habits and hobbies, whether they smoke and/or drink and what brands they like, whether they engage in risky behavior such as owning a motorcycle or a sports car, like Nascar, football or beauty pageants, whether they filed for bankruptcy, what bars they go to, where they have dinner and how often, whether they are pregnant or not, whether they have a sexually transmitted disease, HIV/Aids or cancer. All under the guise of being able to market appropriate products and services to the individual.
Think about how much information one can get just from your credit card, pharmacy, supermarket and gas fill-ups. Last year, I spoke at a conference with a member of the FTC’s consumer protection unit. We talked a lot about predictive modeling, and this was her take away—she had done some research on how predictive modeling had made assumptions about her which included the fact that she owned cats, owned a motorcycle, was in her 50’s and was married. She laughed and said that she likes cats, but doesn’t own one, never had even been on a motorcycle, was in her 30’s and was single. How wrong could they be? The significance to her was that predictive modeling is being used to make assumptions and decisions about consumers, and those assumptions and decisions can be totally wrong. Wrong predictions can have real life consequences. The FTC is concerned that employers, banks, financing companies, insurers and others are and will make decisions about consumers based on predictive modeling to determine whether or not they are insurable (if one smokes, drinks and owns a motorcycle, or has cancer, is this person insurable and how much will the premium be?); employable (do we really want to hire an employee that is pregnant, has a sexually transmitted disease or cancer (that will put our health plan over the top) or is going to take a lot of smoking breaks?) or is creditworthy.
So again, whether you use your rewards cards or not is up to you. Just be aware of the information that you are providing, what is being done with your information, how it is being used and may be making erroneous assumptions and predictions about you, and make an educated choice.