So anyone that’s been around the online advertising world will be familiar with the famous Google Page Rank algorithm. While maybe no one other than Larry and Sergey truly knows how it works, there are literally small armies of SEM and SEO experts that wake up in the middle of the night in a sweat wondering if they left out an important keyword, or whether they need to pay for links to get a boost for their clients.  It’s a fascinating micro economy that has developed almost exclusively around servicing customers and their interaction with Internet search providers, particularly Google and its $20B in annual revenue.

I haven’t bought search in well over a decade, before Google existed, and am by no means an expert in search, let alone local search, but if you’re looking for more information I’d suggest starting out by reading SEOmoz or Greg Sterlings Skreenwerk blog or reaching out to a local search SEO specialist like David Mihm or Mike Blumenthal who are frequent speakers on those circuits and regularly share some invaluable experiences on their blogs at Mihmorandum and Blumentahals.

But to greatly over simplify, fundamentally there are two main components in play for Google on the web, and how well they translate into a true mobile location aware search is fuzzy at best.  So for the current Google web search here are two key factors being looked at:

Continue Reading on LBX Journal

Per John Hanke, VP of Google Earth, Maps, and Local from a recent TechCrunch article : ”PlaceRank is like PageRank for places, it tries to figure out how prominent a place is based on factors such as references on the Web, reviews, photos, how many people know about it, how long its been around.”

By the way I think it’s notable that the thing being “figured out” here is “prominence”.

Now I understand that you’ve got to start somewhere, but I would argue that the tactics used in web search engines don’t really apply to spatial search much and it should be treated as a completely separate animal.  Web pages are about text and the authors of that text linking to (and as a result voting on) other web pages, in order to determine a pages’ ‘prominence’. 

I think there are a few key differences when looking at location and spatial oriented search:

• The “prominence” of a search result is relative to things like distance and the convenience of alternatives in local/spatial search, versus something more absolute in web search where you’re simply clicking on a link to ‘get there’
• Determining “prominence” is very important when parsing through 1 trillion pages of “always available” information, but in the more dynamic yet much more limited options of local search something as simple as solving for “highest prominence” may not be the right answer
• The true “linking” happening to a physical place is not happening on a website, but through foot traffic and phone calls… and the traffic links between places is not captured on a webpage at all, but on a handset or a carriers’ back end logs
•  The stuff being searched for could and should exist in a variety of mediums, not just html on webservers… find a person from their mobile device, find an item from an inventory system, find a bus from a location sensor.

I suspect  that there will be some incremental improvements over time with matching online information to offline stuff, but I also suspect that we’d be better off by blowing up the existing search model and starting over from the ground up with a model designed purely around location specific spatial search, that merely taps into the vast reservoir of online content only when necessary… rather than serving as the foundation.

I’ll be honest I don’t know very much about search engines, a few years ago I printed out a copy of Larry and Serge’s Stanford paper on The Anatomy of a Large-Scale Hypertextual Web Search Engine and read it on a cross country flight for a job interview on a related subject… needless to say I didn’t get the job and I pretty much dropped any more digging on the subject.

But after attending last weeks MetaPlaces conference and listening to the various speakers, including one on the opportunities in mobile search and also a refresher on Sense Networks, the subject is back top of mind again.

The consensus at the conference seemed to be that the existing web search leaders will continue to be the leaders in mobile and there may only be some crumbs of vertical specialty mobile search left up for grabs for new upstarts. And the existing leaders certainly do seem to be innovative as heck in term of trying to infer local intent… I think if you go give Bing Mobile or Google Mobile a trial run for a local business related search, you may be pleasantly surprised.

Here is a quick chart with some of what I think are particularly significant differences between pointing an algorithm at a stack of static text web pages versus the more fluid movement happening between geographic spaces:

As always, comments and discussion are welcomed!

From a Google Job Description in their Geo/Core Content Group.. I thought that the inclusion of “retail product inventories” was particularly interesting, particularly as it relates to where Google may be going with Latitude… bye bye searching for bits, hello searching for atoms? The relevant part:

“Google’s mission is to organize the world’s information – our team’s job is to help acquire all the world’s content… The full breadth of content acquired by the team almost defies description; scope includes multiple forms of media in categories as diverse as: business addresses, event descriptions, restaurant reviews, retail product inventories, real estate listings, government data, and travel-related information.”

Well, one solution is to grow the whole pie by growing the gross number of searches conducted. So how do you do that? For starters you make it super easy and convenient to run a search query, like search on a mobile phone (Google Mobile) while you’re out and about and while at your desk through a browser toolbars that is always there, ready and waiting (Google Toolbar). Second you try to get more and more of the world’s information into the index to make it findable, for example by scanning book or making it easy for any Joe Schmo to create new information say like in a blog on Blogger (acquired 2003).

In fact, the worldwide web has grown like wildfire over the past decade with the Google index growing from 26 million pages in 1998 to 1 billion pages in 2000 and an estimated 1 trillion today. So needless to say there is a lot of information out there to be found, and Google’s success has been built on developing the best mousetrap to help folks sift through those 1 trillion pages and find the information they’re looking for, largely relying on that popularity contest dressed up as a math equation called PageRank to help us uncover the needles among those 1 trillion straws of hay.