Like most people, I tend to surround myself with like-minded folks. Most of my dinner party conversations turn into rousing debates on the future of web standards, or which company will unlock the true power of personal data on the web, or how can we mark our bits with emotional cues to make our web experiences more human. That sort of thing.
But every now and then, I reconnect with old friends and even meet new people who don’t find a conversation on data rousing at all. They have other things on their minds and they haven’t thought about cookies or the amount of data Facebook is collecting on us. The mere utterance of the phrase “silos of data” kills a perfectly lovely conversation.
The problem is that understanding our personal data is important for everyone — not just geeks. People spend an incredible amount of time on Facebook, Google, Amazon, Twitter and other websites, creating content and telling the world how we feel, what we consume, how we think, and what we care about. And none of this belongs to us. I usually rile up a bit of a reaction when I mention that all of that time and energy spent is sold to advertisers, but the reaction boils down to privacy issues rather understanding the value of that information.
As I’ve been building my own personal data collection startup, I’ve thought a great deal about how I could communicate the value of knowing and owning your own data to non-geeks. The answer came to me after making a list of all of the personal data collection applications I have signed up for. I looked at those I use religiously versus those I’ve abandoned. Those I use religiously include: RunKeeper, TripIt, Foursquare, Gowalla, Fitbit, Mint, Hashable, OKCupid, Last.fm and Foodspotting. Those that I love the idea of, but have since left behind, include: Hunch, Blippy, 23andMe, GoodReads, Plancast and Dopplr.
I know that others’ lists will be different, but the point is that this process allowed me to step back and really think about what sort of real-time value I was getting out of gathering my own data. I was able to boil the results down to three categories that, I believe, could be used to incentivize personal data collection for just about anybody. These categories are:
- Utility
- Serendipity
- Self-expression
In order to incentivize the continued use of any personal data collection application, you either have to really excel in one of these areas or cover all three. Let me explain.
(Disclosure: O’Reilly AlphaTech Ventures is an investor in Foursquare, RunKeeper, and TripIt.)
Where 2.0: 2011, being held April 19-21 in Santa Clara, Calif., will explore the intersection of location technologies and trends in software development, business strategies, and marketing.
1. Utility
Probably the best example of utility from collecting personal data is Mint.com. By merely hooking up your online bank accounts, you get a snapshot of where you’re spending your money, how much you have left, and you’re given suggestions on where you can improve your financial situation. Mint.com was so handy for so many people that they had to do very little marketing. Their users became rabid fans and told stories to everyone who would listen about how Mint saved them all kinds of money, exposed fees that they didn’t know they were paying, and helped them get savvier about their finances.
Utility in itself isn’t sexy, but if you make it incredibly beneficial and impossible to live without, people will pay for it. Utility includes things like:
- Tracking — How much you spend, where you’ve been, how much you’ve consumed, when you did that thing you used to do last, etc.
- Augmentation — Anything that can extrapolate from and add to the raw data is helpful.
- Organization — Ways to sort and make sense of the raw data.
- Visualization — A way to present the data so it is easy to interpret.
Utility is where TripIt proved more useful to me than Dopplr. I thought Dopplr’s lovely design and serendipity-driven features were going to win me over, but at the end of the day, it was the usefulness of TripIt’s easy itineraries, flight tracking, and augmentation through things like weather and maps, that led me to use it religiously while I almost completely forgot about Dopplr.
2. Serendipity
OKCupid does the same basic thing that Hunch does: it asks the user to answer endless questions about their personal tastes and preferences. However, OKCupid has something over Hunch to incentivize users to actually spend the time to answer those questions: serendipity. And it’s not just any serendipity, it’s serendipity at its finest: the promise of finding love.
Answering questions about myself is kind of a fun notion … once. People take personality quizzes all of the time online, but when we get the results, what is the first thing we do? We share it with friends. And once our friends have done the test and we all compare notes, that’s it. We don’t really go back. Where it gets interesting is if these results lead us to discover new friends, potential mates, cool stuff and ideas that could change our lives. And it isn’t enough to have this happen once. It has to uncover serendipitous moments over and over again.
Serendipity is also the core element that drives my usage of geo-location applications. A couple of months ago, I was in New York and checked into a pizza place near Union Square. I looked to see who had checked in recently and Mark Suster’s smiling face appeared. I had never met Mark, but I’d always wanted to and Foursquare allowed me to connect with him serendipitously in a place I never expected to. It’s moments like these that drive me to continue to check in even though it takes time and effort to do so.
Serendipity is, ultimately, how you use the data to connect people to people, people to things that may interest them, and people to opportunities.
3. Self-expression
There is definitely a utility in using an application like RunKeeper, but that’s not primarily why I use it. I’m pretty proud of my commitment to training and my progress with running. RunKeeper gives me that tool I need to strongly signal to everyone who follows me that I’m a runner. The more I log my runs, the more people express how impressed they are, and so the more I log my runs. It’s cyclical.
Other applications signal personal tastes as well. Foodspotting signals that you are cultured (if you take shots of a variety of ethnic foods), healthy (if you post organic, vegetarian or the like), indulgent (posting desserts, expensive meals, decadent burgers, etc.) or the like. Hashable signals you are a mover and a shaker without being accused of namedropping. Last.fm signals whether you have hipster or hip-hop leanings. I have to admit that I’ve been known turn off the Last.fm scrobbler when in a pop music mood. Why make the effort to stop the scrobbler and start the scrobbler again? Because I’m aware of the signals I’m sending.
The self-expression or taste signaling dimension of our personal data collection has the strongest potential for creating the ultimate personalized web experience. It’s yet to be completely explored. We are practically screaming who we are and what we like as we post our activity on social applications, yet most recommendation engines and data mining engines continue to put us in traditional demographic and psychographic boxes. The potential of all this will be unlocked when emotional/taste data is mapped to products, check-ins, and our activity across social applications.
I’m looking forward to the day that personal data collection is part of the popular vernacular. Until then, it is up to us — the geeks and developers of theses applications — to help people collect these moments so they receive real-time value.
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