A little taken aback at such a far reaching question, I gave a decent, if long-winded, answer that touched on web optimization, analytics, behavioral modeling, suggestive selling, and (I’m not kidding) Kanye West. It made sense, at least in my mind, but if I had it to do over again, I’d say something far more succinct: the future is in creating (and using) more data.

What do I mean by this?

Well, as an example, take this shoe:

Adidas Y-3 Yohji Yamamoto

According to a prominent apparel E-Commerce site, it has the following attributes:

• Adidas
• Y-3 white leather ‘Y-3 Boxing’ sneakers
• RETAIL VALUE: $230.00
• Color: White / Ivory / Silver
• Supple grain leather upper
• Round toe with silver leather detail
• Lace-up vamp with logo embroidered patent detail at tongue
• Grosgrain strips at sides with printed logo
• Leather lining with padded insole
• Rubber sole
• Leather / Rubber; Imported; style#303564601

And that’s it, which, luckily, is usually good enough. The basics, combined with the photo, are informative enough for me to make a buy decision, especially if I already know that I want this particular shoe.

But what if I didn’t? What else would need to be in the product database in order for this website to help me find this shoe in a search, or better yet, recommend this shoe to me? Is this a performance shoe? Is it a hip shoe? Does it work with a suit? Is it made with organic materials? Is it part of a Japanese specialty line? (As it happens, it is.) Is it rare? (Fairly.)

Without shoe-geeking too much, suffice it to say that there are lots of data points that might be relevant to certain customers which this listing doesn’t include.

And that, in my opinion, is the future of E-Commerce.

For a peek at what one might do in that data rich future, I submit that you need look no further than Pandora, the internet radio service. As I previously reported, I recently had the opportunity to attend a talk given by Pandora’s founder Tim Westergren regarding the service and its underlying engine (more on that here). I was amazed to learn that its ability to make recommendations rested on a database of four hundred “genes” or elements for each song; things like tempo, instrumentation, and key changes.

Four hundred! How many data points are stored for the product above? Twenty or thirty, at best?

While Pandora is not a pure E-Commerce play (they do receive commissions when listeners click a buy link and purchase from either iTunes or Amazon), their approach is on the vanguard of the data intensive predictive selling that I believe will replace today’s “search, find, buy” systems.  90% of Pandora’s catalog of 750,000 tracks are played each month by at least one of its millions of listeners, which makes their recommendation engine perhaps one of the best on the web. All that additional meaningful data is what allows Pandora’s suggestion engine to succeed at introducing its consumers to related products much better than today’s “listeners that liked that also liked this” engine ever could.

Coming soon: A new generation of powerful E-Commerce platforms that will harness richer product data to draw deep inferences and make recommendations that astound customers with their relevance.

(How we get that data will be the subject of a future post…)

Pandora's Playlist Panel

At a recent talk, company founder Tim Westergren took attendees behind the curtain and explained how the system works. Like most people, I expected that underlying all of this brilliance would be some sort of semi-scentient HAL/The Matrix/SkyNet style server farm, churning away at terabytes of mp3s and determining through soulless computer analysis that John Coltrane should be followed by Miles Davis.

Instead, what I learned is that, at the heart of Pandora’s system are a staff of forty or so trained musicians that come to work everyday to listen to music. Really! Individual tracks are listened to and scored against four hundred attributes (they call them “genes”) like tempo, instrumentation, and key changes. Once  a track has been “gene sequenced” its unique collection of attributes can then be matched to other tracks with similar genes, and the seemingly “magical” playlists can be created.

The recommendations and pairings created by this method work pretty well on their own and are further augmented by feedback of the site’s listeners who, by clicking thumbs up or down buttons during songs, can help to refine the system’s understanding of which tracks should and shouldn’t be paired with which.

Apparently the engine is working exceedingly well. According to Westergren, 90% of Pandora’s catalog of 750,000 tracks are played each month by its millions of listeners. Given that in the 50 year history of Billboard’s charts, fewer than 100,000 songs have ever appeared, the exposure of that many tracks to such a large audience is an object lesson in the internet’s famous “long tail.” Moreover, because the engine is blind to pre-concieved ideas of what constitutes a match, unexpected and sometimes amusing relationships sometimes present themselves to the delight (and  occasional dread – AUDIO LINK) of consumers.

NOTE: This is the last in what has turned out to be a trio of posts about Pandora (I promise!) that came about as a result of a lecture Pandora founder Tim Westergren gave on the service and its underlying engine, the “Music Genome Project” (MGP) on March 21st, 2010, here in New York. Click here and here to see the related articles.

But, in a lecture and Q&A delivered last night here in New York City, Westergren said the company still had a ways to go on the social front:

“We still haven’t nailed it…” he said, noting that although the users’ Pandora page is meant to be a social feature, few use it or in many cases even know of it’s existence. The problem, as he describes it is that, while users enjoy sharing music with friends, many aren’t as intrigued at the idea of their friends knowing *everything* they listen to. (Is, say, Celine Dion talented? Sure. Do I want everyone to know I created a Pandora station for her? Uh… Well…)

Still, the Pandora team can rest easy because they certainly have nailed  one thing: the future of radio. Growing at a rate of 85,000 users A DAY, and already accounting for 1.25% of all radio listening in the country (the average is about 17 hours per person per month) the company is poised to get absolutely huge in the next year. In addition to the browser, Pandora can now be accessed from iPhones, Blackberrys, and Android phones, as well as several internet televisions and set top boxes. There’s even word of an integration with Ford’s cars next year.

In short, Pandora aims to be everywhere:

According to Westergren, “Of the 17 hours [users spend listening to radio] 96% of that is broadcast radio: Clear Channel, CBS, the big broadcast radio stations. Our growth, a lot of it is going to come from that…”

While they are careful not to pit Pandora against both broadcast radio and earlier digital pioneer Sirius, it is clear that the Pandora team’s success will come, in part from supplanting the current models. And, since most of their current users heard about the service through friends, Pandora is, in the truest sense, a social phenomenon even if they’re still working on taking maximum advantage of social media.

If you’re still not signed up, check out Pandora at: http://www.pandora.com

http://www.popsci.com/archives

If, like yours truly, you spent any part of your junior high school afternoons in the library reading (as opposed to, say, watching cartoons, playing sports, or, in the case of a few NYC natives I know, figuring out how to steal candy and beer from your local bodega), you’re probably already familiar with the mag, and will enjoy the trip down memory lane. If you’re not, well then I’d look at this as an opportunity to see just how far off base guesses about “the future” turned out to be. Just to get you started, here are some fun search topics to search:

• Moon Colony
• Jet Pack
• Aerocar

Ning CEO, Gina Bianchini

Through the course of the half hour discussion, Bianchini and Rose cover both Ning and its business model, and the larger environment in which the social platforms (twitter, facebook, linkedin, and even myspace) are growing and changing.

Check it out: Hulu – Charlie Rose: Tina Brown, the Daily Beast; Gina Bianchini, CEO of Ning – Watch the full episode now..

Facebook’s Point of No Return | Webmonkey | Wired.com.

At the time, FB was hovering around 10 million users and had just decided  to make the shift from limited access to college student  to opening up the system to all. In the posting the author wonders whether this is the right call and asks rhetorically  whether FB was risking alienating its base.

Of course we all know what happened: a (very) minor revolt from existing users, followed by meteoric rise, global media darling status, magazine covers and a 3400% increase in users in the last four years.

The post reminded me of two things:

1. “Open” is generally better, and sometimes un-friggin-believably better than “closed” (at least once critical mass has been achieved).
2. Sometimes you have to be willing to risk what you have to get at something infinitely better.

Especially in this economic climate, it can be difficult to risk what you’ve been working so hard to hold onto, but don’t be afraid of creative destruction: it may be your best move towards a better future.

Not so fast though: Luckily, those crazy kids at Facebook, not satisfied to rest on their laurels with the 350 million users they already have, have recently exposed their chat implementation to the outside world via Jabber. The upshot? It’s now possible to chat with all your online Facebook friends using iChat.

Check it out: http://www.facebook.com/sitetour/chat.php

The page contains instructions for iChat as well as Pidgin, Adium, and generic “Other” Jabber clients.