Posts Tagged ‘Information Barter’

How much do you care about healthcare?

Thursday, January 22nd, 2009

It has always been our conceit that for most people, if they are unwilling to provide sensitive personal information, it’s just because you haven’t asked in the right way.

Tell me that you need my social security number to access my money and I’ll recite it faithfully to an automated voice system.

Ask me for my email address as I’m checking out of a store and I won’t even give you my never-check-it-spam-hotmail address.

(There’s also the issue of trust and security, but I’ll gloss over that for now.)

Personal information, like it or not, has become a commodity in the modern era. And like any commodity, individuals make pragmatic decisions about when and to what extent they exchange personal information for another commodity or service.

Like other market transactions, valuations are subjective and fickle. I have a friend who is a sweepstakes junkie and will give out every last detail about herself for a one in a million shot at winning a shopping spree or a sports car she can’t drive.

Others balk at sending anything over the wire, unprotected, and end up encrypting even mundane reminders to “Please remember to pick up milk on the way home.”

Our challenge for this pilot is to figure out how to make it compelling for people to contribute information about themselves vis-a-vis their healthcare.

Amazon’s red and blue book-buying map

Wednesday, October 15th, 2008

Sorry, it’s another semi-political post!


We at the Common Data Project are definitely interested in more than politics, but this Amazon map of political book-buying state by state was too interesting not to blog about it. It illustrates so many things I believe in.

One: Information-sharing can be fun.

People love patterns, and even more, knowing where they fit into them. The Amazon customers who are most likely to be drawn to this map are those who have bought political books, books that fall into the red, blue, or purple categories. No one is likely to be outraged that his purchase of Thomas Friedman’s book in the last 60 days got counted in designing this map. Although there’s a lot of data collection that Amazon prefers to keep on the down-low, this kind of tracking is refreshingly open and explicit. We know it’s being collected, and most of all, we get something in return. We all get to enjoy the data as well.

Two: Data has limited value if there is limited context.

As pretty as this map is, it doesn’t really provide much information. Junk Charts lays out a lot of the deficiencies that limit our ability to draw any meaningful conclusions. Providing the map with just the states colored in, but without real sales numbers, doesn’t give you a real sense of which books are selling better, in the same way that the 2004 election red-blue maps with their wide swaths of red in the middle didn’t provide real information about population density and how close the election had actually been, nor how seemingly blue or red states actually contained significant pockets of people who had voted for the other guy. How many people in South Dakota bought a “red” book? Ten, twenty, or a hundred thousand?

The paucity of information on how books were rated red, blue or purple drove me crazy, too. Every place I clicked to “Learn more,” it took me to the same very short four paragraphs. It says that the categorization was based on the book’s own promotional materials and the tags readers added to them, but I still wonder who categorized these books and precisely how they did so. Would all the authors necessarily have labeled their books as blue or red?

And if they were categorizing books as purple, as neither obviously liberal or conservative, why didn’t they include them in the percentage calculations by state?

Three: Underlying data should always be available for alternative analyses.

A lot of people are wary of data; they’ve heard too many times how numbers can be twisted to serve any purpose. We at the Common Data Project make no promises that data = truth, only that when data is truly open and available, conclusions based on that data can then be prodded, tested, and possibly refuted.

In this case, I’m not quite sure if Amazon does have a conclusion to assert, but the decisions it made about which data to include and exclude have shaped the map presented. One conclusion you might draw from a cursory glance might be the same one drawn by one of the commenters to the Junk Charts post—that people only read books they’re likely to already agree with. Imagine now if we could test that conclusion, if we could count how many readers in each state bought both “red” and “blue” books, or if there were readers who would consider themselves “conservative” but bought “liberal” books. Maybe there’s a very active and large political book club in Wyoming buying books from across the spectrum!

It may very well be true that people who identify as conservative buy “red” books, while people who identify as liberal buy “blue” books, but the map as provided doesn’t provide enough information to truly test that conclusion or propose interesting hypotheses of why that’s happening.

Still, I had a good enough time playing around with the map that I was reminded me of a book I’ve been meaning to read, which is probably Amazon’s ultimate goal anyway!

FreshBooks Aligns Data Collection with its Customers’ Interests

Wednesday, October 11th, 2006

I think FreshBooks is attempting something very interesting.

[Freshbooks is geared toward small businesses and/or independent contractors. From their Manifesto: “Our mission is to deliver fast and simple invoicing and time tracking services that help you manage your business.”]

They are asking their users to optionally classify their profession/industry. In return, participants gain access to business metrics for their industry, based on aggregations of data collected from the Freshbooks user population.

The examples they give are

  • “What is the average invoice size for [your profession]?”
  • “How long does the average [your profession] take to get paid?”
  • “What is the average monthly revenue of other [your profession]?”

I would imagine this will raise many a small business eyebrow. However, they still feel thin and generic to me. I want to know:

  • “How many years of experience do other professionals in my industry have?”
  • “What are their industry credentials? Education? Training? Skill set? Work experience?”
  • “What is the quality of their clientèle?”
  • “Where is there operation based?”
  • “What kind of capital investments have they made?

Collecting data from users is not new. Collecting data from users to provide a service is not new (if you consider targeted advertising a user service). However, there is something unique about what Freshbooks is doing that differentiates it from the various other data collection efforts on the internet. They have figured out a way to provide data to their customers that provides tangible, monetary value to their users; value that their users would probably be willing to pay for, and value that is difficult (expensive!) if not impossible for them to get anywhere else.

Furthermore, Freshbooks’ model turns the tables on data collection and privacy. In place of a parasitic relationship where Internet Company as Big Brother spies on users in order to make big bucks selling Targeted Advertising, a symbiotic exchange is established where users happily provide personal data in exchange for a tangible good in return. Sounds too good to be true? It probably is in the immediate future.

It’s worth noting that

  1. Freshbooks is collecting data from a real service they provide (as opposed to polls and surveys). This minimizes the risk of collecting bogus data.
  2. Because FreshBooks implies they will only tell you about the industry you indicate (thereby encouraging you to provide an accurate categorization or be given useless data) data inaccuracies due to user information distortions should be minimal.
  3. Freshbooks is being at least semi-transparent about what they’re doing with the data they collect. As a result, Freshbooks is establishing a trust relationship with their users, which turns the data they collect from their users into a renewable resource, as opposed to one (advertising) that runs dry as soon as users find out they’re being spied on.I say semi-transparent because:3a. Freshbooks is not being completely forthright about who else they may or may not be selling this data to.3b. Implicit is the fact that Freshbooks can also use this data to optimize their own business and pricing strategies.
  4. Although they are not charging for this data yet, the information (to any given customer) would probably be valued at at least $100s/year. (How Freshbooks might choose to monetize that value is a different story.) By contrast, the dollars that Freshbooks might have been able to get from selling targeted advertising for that customer’s eyeballs is unlikely to approach $100/year.
  5. Freshbooks reassures its users that their data is only used in its “anonymous aggregate form”. However, the term ‘data aggregates’ is so vague as to be largely useless. Freshbooks still doesn’t have a complete story about how they will protect the individual identities of their users.
  6. I’m not clear on how this new program jibes with the FreshBooks privacy statement, which under the heading “Ownership of Data Submitted to Active FreshBooks Subscriptions” suggests that user data is owned by the user, not by FreshBooks. How then does Freshbooks have the right to aggregate and share your data with other users? Does Freshbooks only collect data from users who opt-in to share/view data? If so, that severely limits their data pool. I wonder how many of their 90,000+ users are considered active and will opt-in…?

I’m very interested to hear if this sticks, and if their users are able to jump over the hurdle of giving up a little bit of privacy for a little bit of information. The relevancy of the data will presumably be a factor in continued participation.

What they should be doing:

  • Providing context about what’s missing: It is as important to understand who isn’t participating in providing data, as it is to know who is.
  • Provide context about their users: It is as important to understand the demographics, circumstances and nature of the other participants as it is to know what they raw accounting numbers are. After all, do I, as an small-town consultant really care what the big boys are charging on Madison avenue?
  • Taking a lot of care with the aggregates such that some sort of data-release scandal doesn’t come and bite them.
  • Refrain from using their data for parasitic reasons which undermine the trust relationship they’re building with their users.
  • Provide a way for users to cleanly and completely end their participation in the data collection program.

While time will tell what happens with the execution of this effort, I am excited by the attempt: A business that collects data from their users and returns to them business intelligence, rather than handing over the customer relationships they built to the highest pay-per-click bidder.

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