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| Large network with multiple clusters: based on consumer choices |
The network graph above reveals clusters of similar books sold by Amazon. Today, Amazon knows how products they sell cluster, will they soon know how customers cluster, in non-obvious ways?
Amazon, with their public/private highlights/notes from Kindle readers is creating a knowledge & interests ecosystem that will aggregate what the world is interested in, and what the world finds important... and what the world wants to buy more of. It is not just the also-bought data that matters (which books bought by same customer), it is what we specifically find interesting and useful in those books that reveals deep similarities between people -- the hi-lites, bookmarks and the notes will be the connectors. With electronic hi-lites, bookmarks, and notes for Kindle books Amazon is raking in the data. Our choices reveal who we are, and who we are like!
It is what we specifically find interesting and useful in those books that reveals deep similarities between people. We will connect with each other via our similarities and profit from our differences... and so will Amazon!
Below is a network map(via social network analysis) of a very interesting book -- Too Big to Know [2B2K] by internet scholar David Weinberger. David's book is shown by the magenta node in the center of the network. Directly connected to his book are the books that Amazon mentions that customers also bought [green nodes], in addition to 2B2K. These books are probably more similar than different to 2B2K. The blue nodes are books that are 2 steps away from 2B2K, they are probably more different than 2B2K, but retain strong similarities. The arrows show the direction of the majority of also-bought activity. If you find 2B2K interesting, you will probably find a pleasant read in one of green books or possibly a blue book -- depending upon your desire for difference.
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Book Network Neighborhood for Too Big to Know |
With Kindle readers providing electronic hi-lites, notes and bookmarks, Amazon is getting much more data on individual interest graphs. Today, Amazon introduces you to similar books. Tomorrow, they will introduce you to similar readers?
Eh, not really that under the radar? Kindle.amazon has been recommending readers with similar profiles for quite some time.
ReplyDeleteBut more people take photos or have jobs than read books, so the scale will be less?
How's about the community network develops a web-based trading platform and uses a community issued e-currency to enable participants to make exchanges and store value.
ReplyDeleteImagine that the community could post it's needs up onto the platform to create demand for either goods or services. Imagine then that people became aware of this market demand and produced new goods and services to match what was demanded, and that the marketplace determined the price/value of the transaction.
We wouldn't need many Big Businesses then. We could do it all our selves, just like we used to do before the market place separated producers and consumers.
This is what is being developed in Wigan, Uk.
Anon,
ReplyDeleteNo, Amazon has been recommending books and other products. My point is that Amazon has some of the best data for matching up potential colleagues and friends.
If I wanted to hire you I would first skim over your resume on LinkedIn quickly and then do a much deeper compatibility analysis with your Amazon behavior data. Do you have the background? --> LinkedIN data, would you fit in? --> Amazon data.
Mike,
ReplyDeleteGood luck with your efforts in Wigan UK. I love to see bottom-up, self-organizing, emergent systems seed and grow. See this white paper for such a system (food network) in SE Ohio -- http://orgnet.com/BuildingNetworks.pdf
Enjoy!
Any company that has access to large amounts of preferential data has a chance to create something big. Right now Facebook is rightfully considered one of the companies with access to a lot of this data, but Amazon is another one that few people really consider because they have avoided most of the empty trappings of social thus far. I think that the idea of getting this structured data is what is so appealing to Google and why they're putting so many resources behind Google+. Without access to structured social data, Google can't continue to refine their search experience further. With access to this social data, Facebook has a legitimate shot of competing in search by using this social data to fuel their search experience. Facebook has captured the attention of the popular culture in addition to businesses: look at how many active users Facebook has, look at how many big brands are promoting their Facebook pages in their TV commercials, look at how many companies their are at BuyFacebookFansReviews that do nothing other than promote Facebook pages...Facebook is really on the right track here even if they deserve a bit of criticism over some of their features. I think that the reason that Facebook is worth well in excess of their $100 billion IPO is that they have the potential to dominate search, ecommerce, and other major fields. But this market is so big and valuable that I think there's room for other players to get involved here and take some risks and chances. Amazon has their toes dipped in some of these waters, but they're one of the few that can create the next big thing. Apple is another big player here. While Facebook has more raw data, the advantages that Amazon and Apple have is that they have access to peoples' credit card information. If they have a setup where they can take purchases easily and others can't, they can compete in some areas where it would be hard for Google and Facebook to compete. Interesting thoughts overall.
ReplyDeleteValdis - ...then I recommended myself to you.
ReplyDeleteIt took me a few minutes to convince myself that that figure was not representative of 'the books on brian's nightstand'...the more I learn the more I realize that we are barely scratching the surface on the impact of meta data...the value will be immense, but what about the risks - perhaps too early to tell? What do you see as the potential downside to this data revolution?
All the best,
@briansmcgowan