Big Data? Let's Do Little Data Better, Too

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I get weird commercial snail mail.

Not "fun" weird mail, but mail that is so far off base, it's more of a waste of resources than normal mail.  A lot of it revolves around mailmerge programs or datasets that aren't being used in a smart way.  Tonight I got two examples:

  • A solicitation to apply for a credit card account, with my correct address, but "Gerald Walls" as the addressee.  There is no Gerald at this address; I don't even know a Gerald Walls. It looks suspiciously like an off-by-one error in someone's data. I can only imagine who might have received the envelope with my name and their address on it.  This happens surprisingly often.
  • My former gym in Dulles, VA, offering me a discount to re-join.  Addressed to me here, in New York City, 250 miles away. When I closed my account there, I even checked the box "moving out of area" in their survey.
Both of these vendors are wasting their resources on an audience that isn't there.  A credit card application with incorrect addressing doesn't really make me want to sign up. I can assume that both myself and the other individual were "pre-approved", and that both names were in a fairly homogenous dataset. It doesn't instill confidence in the institution which sent the mailing, or its ability to find competent subcontractors to send mail on the company's behalf.

The gym membership is a little different, in that the company is significantly smaller than a large multinational bank. It's even a chain; when I enter my zipcode in their locations tool, the page tells me there are no locations near me.  Yet they are spending money to send me mail.

Neither one of these cases would have a dataset even approaching what might be considered "big data". The bank probably ran a mailing targeting a few hundred thousand potential customers. The gym, maybe a few hundred. Datasets that don't need map reduce or inventive visualization, but are essential to businesses, are managed sloppily or neglected entirely.

I find banks are terrible at this. I get a lot of mail for weird people with my address.  Sometimes the items are like this one, were the name is obviously proximal to mine in an alphabetical list. Other times it's completely nonsensical.  

The gym probably manages data using common desktop office tools, one or two people are tasked with pulling the data from the membership database to use for mailings. It's possible that the tools aren't sophisticated enough to run "zipcodes for all former members" against "zipcodes in proximity to our locations", a change that would allow the company to increase the potential return on their mailings, by focusing on people who actually have a gym nearby. Maybe even people who, like me, moved, but, unlike me, into an area with a gym location and weren't aware of it.  

Maybe with the spotlight on big data, the message will get across that data of many sizes has value and can be used to help, or misused to hurt, the entity that holds it.

P.S. To say nothing of how many ways my last name gets spelled wrong in mail. "Walls" is not:

  • Wallace
  • Waller
  • Wallis
  • Willis
  • Wills
  • Wells
  • Malls
That's a whole other issue with fun things like transcription, and maybe my own handwriting. :D

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This page contains a single entry by Mandi Walls published on November 4, 2011 1:05 AM.

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