Consumerism and Self
Michael O'Malley, Associate Professor of History and Art History, George Mason University
This exercise examines the recent phenomenon of preference tracking software. You will be asked to participate in several websites which seek to track your buying preferences. We will be investigating two websites which track personal information and try to predict consumer behavior.
The first web site is MyBestSegments, sponsored by an Arlington, Virginia, company called Claritas. Claritas takes information about consumer preference and compiles it into lifestyle "clusters." The company then cross-references the lifestyle information by zip code. Go to the Zip Code Look-Up. In the new window that appears, enter your zip code in the box, choose "prizm," and see if it accurately describes your home.
Companies like Claritas claim that people tend to live near people like themselves, and that they can predict what you watch, eat, wear and think by getting your zip code. Check the list of other communities similar to your zip code. Are they familiar? They also argue that the better their information, the more people will tend to "cluster" with like-minded people. Using this sort of preference tracking would insure that eventually, you would only get mail for things you really needed or wanted—no junk mail. The supermarket would only carry things you like, the local stores would only carry clothes or music you liked. Would such communities undermine the idea of national citizenship?
Claritas depends heavily on the zip code for its categories. Zip codes were invented in 1964, by the US Post Office. They were intended to make mail delivery easier and more efficient, especially since the arrival of computers in the late 50s and early 60s had drastically increased the amount of business mail. Many people objected to the zip code and saw it as a government intrusion and a loss of individualism. But by the 1980s, thanks to the tendency to "cluster," zip codes had emerged as a stylish marker of identity, as seen in the extremely popular television show of the time, Beverly Hills 90210.
Indeed, there have been many instances in the last ten years of communities trying to change their zip code, since so much—besides consumer choice, insurance rates, mortgage rates and home prices are also pegged to zip codes—depends on zip codes.
In 1983, the Post Office introduced ZipPlus4, which allows a single number to describe an office or apartment building, or a few blocks of a neighborhood. ZipPlus4 will allow marketers to focus their efforts much more tightly and precisely.
Some people initially attacked the zip code as a sign of an all powerful and intrusive government. But ironically, the private sector has used zip codes to find out far more about us than the government ever can. Knowing your zip code, marketers also know much about what you eat, drink, watch, wear, and believe. Is it possible that zip code preference tracking will move us towards something like Stephenson's "Burbclaves?"
First, you must establish a username and a password at Amazon.com. This exercise works best if you have already bought a book from Amazon. If you have, they will have recommendations for you at the top of the page. If not, after you have established an account, Amazon will step you through the process of refining your recommendations. Spend some time—at least an hour—refining your recommendations. How accurate have the recommendations become?
"Collaborative filtering"works with massive databases such as Amazon's—which has tens of millions of people in it. When you buy or rate a book, the computer instantly checks for other people who liked or bought the same book. What other books did those people also like or buy? It then returns these items to you. The more you use it, the more accurate it becomes. It turns out that in a database of 20 million people, there will be many people whose tastes in books and music are almost identical to yours. How many? That's not altogether clear.
Some firms have attempted to use collaborative filtering as a kind of dating service, arguing that it should be possible to literally find one's "soul mate" through collaborative filtering. Collaborative filtering is used when you purchase goods in stores or supermarkets. The data on your purchases is cross referenced with other people's purchases, so the store can compare you to people who like similar things and present you with you precise, targeted information on sales, or announcements of new products.
Questions to Consider:
- How does this process work?
- How accurate were the recommendations?
- Did they become more accurate as you used the system more?
- Could this system be applied to other things?
Consider these questions, and the issues raised on the previous page, in your web journal.
Updated | April 2004