Dis-Cord: Paying Our Data Forward

One of my favorite ReadWriteWeb authors, Dana Oshiro, recently asked in a post, “The larger industry-wide questions remains – will this year’s [2010] branded iPhone app be a recommendation app?” While I pondered this question, I began asking myself, yes I do but… aren’t we almost already there? Allow me to explain…

The palm-sized devices that we all carry have a strangely unique ability to sway protectionists to be more transparent, isolationists to interact in real-time, and can seduce the technologically impaired/fearful to experience the advantages of a modern life. There’s no doubt in my mind that the behavior that follows these little devices will also be absorbed by them and spat back out in the form of recommendations.

To me, it all makes sense: it’s rather time consuming to hunt down the cool in Austin and always be spot-on. To compensate for this lack of time, we’ll often access Web sites on-the-go and utilize them as short cuts to aide us in the recommendation process. Although these short cuts are extremely helpful, we often need to know more than just what to do today or this week; we need to know what to do right now.

So to over-simplify a complex space, there are two general buckets that satisfy this need for real-time recommendation: aggregators (which are computer algorithms, like Pandora) and platforms that can be used for social recommendation (which are human powered more-or-less, like Twitter).

Though Twitter is the most hyped, it’s not the only social recommendation tool out there; in fact, there’s lots of them! For example, Rec.fm is a charitable recommendation tool, and Superglued recommends concerts near you. Blippy’s “tool” is a bit more controversial, and Gowalla and Four Square’s apps allow location-based recommendations, while Twitter’s lists and hashtags, such #nowplaying, offer follwers insight into what and who’s popular at the moment.

In short, though some tools are more directly tied to actually “recommending”, ultimately any time you share an experience it can be transformed and interpreted as a form of recommendation. Slashdot, Digg, Hackernews, in their simplest form, are just recommendation engines and a means to an end – identify, interact, share, and in turn, recommend.

James Cameron’s new film, Avatar, is a visible example of mainstream recommendation by participation. There was a ton of buzz resulting from re-tweets (partly due to the astronomically large 18,000 follower-ship); Facebook was blowing up with status updates along the lines of “I just saw Avatar!” Avatar’s popularity/revenue was dramatically improved by the hype resulting from the never-ending activity of sharing, updating and retweeting. This stream of never-ending recommendation and reinforcement, combined with other marketing efforts, ignited the momentum behind film and truly made it a blockbuster.

In light of that, 2009 was most definitely a year full of recommendations in many forms. The answer to Dana’s question above is yes – I fully agree there’s ton of potential for vertical segmentation and I believe we’ll see lots more of the kind of stuff she mentions:

“…there’s opportunity to extend these recommendation-based applications to special-interest and location-based communities. Imagine investment communities trading and reviewing stock and news apps, or Oprah Winfrey’s community recommending shopping and reading apps, or New Yorkers sharing transportation and amenity apps.”

We’ll see all of these; companies can simply transition their products from the web to mobile apps and POOF – we’re there! In my mind, the next set of questions I think we’ll inevitably be asking is, what makes one recommendation app better than another and where is it getting its information to make us a better recommendation?

The apps Dana will be talking about six months from now will be the recommendation apps that have successfully captured the wealth of recommendations we have been unknowingly contributing to the Web since its conception; it will be the apps that have crunched these recs down to the raw data and re-wrapped it in the prettiest app UI that best pays us our own data forward.

These apps will be artificially intelligent, they’ll have to be aware of real-time trends, and most of all, they should be able to react to all environmental factors while delivering recommendations based off of a specific individual’s unique preferences.

Full Disclosure: Rec.fm and Superglued are clients of John Robert Reed’s.

Posted by John Robert Reed "John Robert" Reed writes the Dis-Cord column for Austin Startup covering everything from emerging mobile trends to on-the-go consumer-oriented technologies. He has special interests in the rapid evolution of the world around us as heavily influenced by augmented reality, mobile technologies, artificial intelligence, and the pursuit of the perfect UI/UX. When not perusing the Web, John Robert enjoys absorbing the live music scene, playing beach volleyball, swimming at Barton Springs, and exploring Austin’s wonderful cache of most excellent record stores. John Robert is a PR hacker by day and works with a highly skilled team to bring early-stage technology companies to market. To read more posts click here.