Where Did my Tastegraph Check In Tonight?

I need less help in navigating communications with my friends than I do in discovering excellent brands and products. Example: I’m in the market for a new subwoofer. I’m a minor-league audiophile and read up on these things now and then. There is no shortage of product reviews out there, but I find that the reviews might not be sufficient because I don’t know anything about the reviewer.  I began to talk about this in my previous post regarding restaurant reviews, and I would like to take it further here. When I read subwoofer reviews, I’d like to know if the reviewer’s standards unreasonably low or high. Does he buy things just for the label? Or just to be trendy? Is he a penny pincher or someone who spends too freely?

As a result of my previous post, I was introduced to Bizzy and LikeCube – who describe themselves as providing “recommendations based on similarity of product and similarity of user”. Services like these promise to provide me with the recommendations for me, not generic recommendations that are supposed to apply to everyone. We see this today, to a certain extent, with product recommendations on Amazon and other websites, but recommendation engines  in their current still don’t offer the nuance that most end-users would like to see. 

As the state of the art progresses, the best in class recommendation engines will be able to take inputs from services like Get Glue, Facebook Likes, a variety of geo-check in services, and more. Check-ins, in particular, looked poised to grow exponentially as mobile payment services begin to hit the mainstream via players like Isis, PayPal, Google, and (we assume) Apple. As they do, checkin during payment will simply become another option, and those details will add to individuals’ profiles and broadcast across social networks.

While the benefits of all of this are generally well-understood for consumers, brands, too, will have much to gain. As the data becomes available, such information will help brands to identify potential new partnerships, find local-influencers, and provide real-time special offers based on a combination of taste graph and location. To take advantage of this data will require evolving skill sets, but the best practices will remain the same: start small, fail fast, measure everything, analyze & repeat. 

I’m not abandoning the concept of socialgraphing or physical checkins via FourSquare, for example. There’s value in knowing who checked into which bar. But what I’d really like to know? What beer they are drinking once they get there…