We are living a pretty intense “data moment.” Data collection and usage practices that were formerly dismissed as technically impossible, or simply not done, are now feasible and openly posited as much in terms of proposed benefit as in terms of personal or business risk. Try this from case study of the retailer, Target, in the New York Times – How Companies Learn Your Secrets:
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“…The desire to collect information on customers is not new for Target or any other large retailer, of course. For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code — known internally as the Guest ID number — that keeps tabs on everything they buy. “If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID,” Pole said. “We want to know everything we can.”
Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what Web sites you visit. Target can buy data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own. (In a statement, Target declined to identify what demographic information it collects or purchases.) All that information is meaningless, however, without someone to analyze and make sense of it. That’s where Andrew Pole and the dozens of other members of Target’s Guest Marketing Analytics department come in.
Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a “predictive analytics” department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them. “But Target has always been one of the smartest at this,” says Eric Siegel, a consultant and the chairman of a conference called Predictive Analytics World. “We’re living through a golden age of behavioral research. It’s amazing how much we can figure out about how people think now.”
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Please read the full article. For our part at Markets For Good, this one got us thinking of how this big data scene maps to us in the social sector.
A few questions, observations, scenarios for your comment:
- How could nonprofits use consumer transaction data?
- Powerful steps forward are possible, even using a desktop computer (and a lot of will) as seen in the case of physician Jeffrey Brenner when he was given the data of hospitals in Camden, New Jersey.
- How are social sector organizations using their own transaction data to understand behavior and needs?
- Consumer data flows freely via longstanding infrastructure pieces: voluntary credit reporting by companies, social security numbers, data markets, etc. What does the infrastructure for social sector data look like in comparison? To what extend does the social sector participate in the data market?
- If data is the currency of our time, how can we treat it as a currency for social good?
- “Currency” in this sense is literal, giving context, for example, to the “data philanthropy” proposed by UN Global Pulse.
- Have you defined your personal and/or organizational position on how you will use sensitive data?
You get the feeling that anyone standing on the sideline right now will not just be left behind, but also be left to market forces not easily understood. For the social sector, this doesn’t mean running out and hiring data scientists for every organization, or even obsessing on big data. It’s just getting involved.
Getting involved, for some, means being aware of how the data works in your neighborhood, e.g. learning more about how retailers affect the lives of the people you serve. For others, it may be developing the kinds of infrastructural pieces that would enable the social sector to be a viable data partner, competitor, market participant when facing other sectors.
For all of us it’s time to keep a close eye on where to play and how to win. Share your thoughts with us on how you’re doing it, what you need, and how we can make the right connections between organizations.
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What do you think about the information infrastructure in the social sector?
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