There is a class of web search engines that can prove even more useful than Google within a certain context. I'm talking, of course, about Vertical Search engines - the writer and tech strategist Sramana Mitra considers them Google's Achilles heel and Profy.com's Cyndy Aleo-Carreira seems to agree. This blog also has long held the position that vertical search represents a powerful mechanism to find information on the web, and is a key category to watch in the search wars of the future. [see: The rise of Vertical Search Engines from Aug 2006].
Another way of achieving a similar focus, in order to improve the relevance of search results, is by segmenting by location rather than by industry vertical - i.e. create a hyperlocal search engine that limits its search results to a given geographical area.
One such alternate search engine is Zvents, which is relentlessly focused on local information, of any sort. This company, which has been around since early 2005, has just introduced an advanced feature called Federated Local search - basically, its own version of Universal Search (recall that Google introduced its Universal Search feature with much fanfare last May).
Federated Local Search: Multi-Dimensional Results for Local Information
What does Universal Search mean, for a local search engine? Initially this was not very clear to me; an email discussion with Paul O'Brien, Director of Marketing at Zvents, inspired me to draw the following diagram:
The basic idea is to enable the user to implement a general-purpose search within a local context. This allows the user to find local information about a given topic, across many different dimensions. For example, a sports fan living in San Jose, CA who tries a local search for the term "hockey", would get the following different types of results:
- Upcoming games for the San Jose Sharks, the local hockey team
- The location of Roosevelt Park Roller Hockey Rink
- The description and link for a local "Hockey Night" event
- Results about relevant personalities (what Zvents calls "performers")
- And other related links ...
Zvents has already partially implemented this vision, although some of the lower-ranked results could provide a better match. Hopefully these will improve in the future as the search index grows and the algorithm improves. A screen shot of this local Hockey search in Zvents is given below.
Similarly, here's a search for the term "Web 2.0" for Cupertino, CA:
Outcome: Relevance
The big advantage of this type of search, over a general-purpose Google or Yahoo! search, is that the user can obtain the benefits of a broad cross-section of results, while still constraining the search to a limited geographical area.
This is not a significant issue in highly developed, urban, technologically advanced areas like Silicon Valley, Boston or New York; but it could one day make a big difference for someone living in David Letterman's "home office" of Wahoo, NE , or even more important, someone trying to find the Boston Public School located in Boston, Ontario - as we've seen before, highly popular keywords tend to swamp nearby long-tail keywords in the search results for major search engines.
From a business model perspective, hyperlocal searches tend to provide highly qualified prospects for local merchants, so I would guess that this type of search is very easily monetizable in the long run.
From a user interface point-of-view, the NLP-like implementation of time period for the search engine ("when: tonight, this weekend, ...") is a nice touch; I tried different possibilities ("next month"), and it seemed to work just fine.
On a more technical note, Zvents has been making waves with the release of its open-source Bigtable clone called Hypertable, which adds a C++ option for this project.
Going forward, it will be interesting to see how Zvents scales to additional locations, and to additional dimensions within each locality. Will it make inroads into the market share for any of the major search engines, or into that of other locally-focused web sites like topix.com and craigslist?
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