Vertical search is the term people sometimes use for specialty or niche search engines that focus on a limited data set (as already mentioned, Google calls them onebox results). Examples of vertical search solutions provided by the major search engines are image, video, news, and blog searches. These may be standard offerings from these vendors, but they are distinct from the engines’ general web search functions.
Vertical search results can provide significant opportunities for the SEO practitioner. High placement in these vertical search results can equate to high placement in the web search results, often above the traditional 10 blue links presented by the search engines.
The big three search engines offer a wide variety of vertical search products. Here is a partial list:
Google Maps, Google Images, Google Product Search, Google Blog Search, Google Video, Google News, Google Custom Search Engine, Google Book Search, Google US Gov’t Search, etc.
Yahoo! News, Yahoo! Local, Yahoo! Images, Yahoo! Video, Yahoo! Shopping, Yahoo! Audio Search, etc.
Bing Image, Bing Video, Bing News, Bing Maps, Bing Health, Bing Products, etc.
All three search engines offer image search capability. Basically, image search engines limit the data that they crawl, search, and return in results to images. This means files that are in GIF, TIF, JPG, and other similar formats. Figure 2-29 shows the image search engine from Bing.
Image search engines get a surprisingly large number of searches performed on them. According to comScore, more than 1 billion image searches were performed in October 2008, or a little more than 8.3% of all searches performed in that month. Similar data from Nielsen Online shows image search comprised 6.0% of all search in January 2009. However, since an image is a binary file, it cannot be readily interpreted by a search engine crawler.
The search engine has to rely on text surrounding the image, the
alt
attribute within the img
tag, and the image filename. Optimizing
for image search is its own science, and we will discuss it in more
detail in Optimizing for Image Search in Chapter 8.
As with image search, video search engines focus on searching specific types of files on the Web, in this case video files, such as MPEG, AVI, and others. Figure 2-30 shows a quick peek at video search results from YouTube.
A very large number of searches are also performed in video search engines. Hitwise and comScore data shows approximately 125 million searches performed on video search on the major search engine properties (e.g., http://video.google.com, http://video.yahoo.com, and http://video.bing.com) in October 2008, and then this number balloons to 2.6 billion searches once you include YouTube (http://www.youtube.com), which has become the #2 search engine on the Web.
There is significant traffic to be gained by optimizing for video search engines and participating in them. Once again, these are binary files and the search engine cannot easily tell what is inside them.
This means optimization is constrained to data in the header of the video and on the surrounding web page. We will discuss video search optimization in more detail in Others: Mobile, Video/Multimedia Search in Chapter 8.
However, each search engine is investing in technology to analyze images and videos to extract as much information as possible. For example, OCR technology is being used to look for text within images, and other advanced technologies are being used to analyze video content. Flesh-tone analysis is also in use to detect porn or recognize facial features. The application of these technologies is in its infancy, and is likely to evolve rapidly over time.
News search is also unique. News search results operate on a different time schedule. News search results have to be very, very timely. Few people want to read the baseball scores from a week ago when several other games have been played since then.
News search engines must be able to retrieve information in real time and provide near instantaneous responses. Modern consumers tend to want their news information now. Figure 2-31 is a quick look at the results from a visit to Yahoo! News.
As with the other major verticals, there is a lot of search volume here as well. To have a chance of receiving this volume, you will need to become a news source. This means timely, topical news stories generated on a regular basis. There are other requirements as well, and we will discuss them further in Optimizing for News, Blog, and Feed Search in Chapter 8.
Next up in our hit parade of major search verticals is local search (a.k.a. map search). Local search engines search through databases of locally oriented information, such as the name, phone number, and location of local businesses around the world, or just provide a service, such as offering directions from one location to another. Figure 2-32 shows Google Maps local search results.
The integration of local search results into regular web search results has dramatically increased the potential traffic that can be obtained through local search. We will cover local search optimization in detail in Optimizing for Local Search in Chapter 8.
Google has implemented a search engine focused just on blog search called Google Blog Search (misnamed because it is an RSS feed engine and not a blog engine). This search engine will respond to queries, but only search blogs (more accurately, feeds) to determine the results. Figure 2-33 is an example search result for the search phrase barack obama.
We explore the subject of optimizing for Google Blog Search in Optimizing for News, Blog, and Feed Search in Chapter 8.
The major search engines also offer a number of specialized offerings. One highly vertical search engine is Google Book Search, which specifically searches only content found within books, as shown in Figure 2-34.
Yahoo! also has a number of vertical search products. Yahoo! hotjobs is an example of a product designed to allow people to search for jobs (see Figure 2-35).
Microsoft also has some unique vertical search properties. One of the more interesting ones is Celebrity xRank, which offers data on celebrity rankings and search trends, as shown in Figure 2-36.
Google made a big splash in 2007 when it announced Universal Search. This was the notion of integrating images, videos, and results from other vertical search properties directly into the main web search results.
This quickly became a huge driver of traffic to sites because of their images, videos, news, local search information, and more. The other search engines quickly followed suit and began offering vertical search integration before 2007 was over. People now refer to this general concept as Blended Search (since Universal Search is specifically associated with Google). A look at some Universal Search results from Google can help illustrate the concept (see Figure 2-37).
Note the news results, along with an image at the very top of the results, along with more image results farther down. This information is coming from Google’s news search index. If you look farther down in the search results, you will continue to see more vertical results, including video results and a timeline (see Figure 2-38).
A wide range of vertical data sets have been integrated into Google’s Universal Search, as well as into the Blended Search results of the other search engines. In addition to the preceding examples, you can also see images, videos, and local data integrated into the traditional web search results.
The advent of Blended Search has significantly increased the opportunity for publishers with matching vertical data sets (such as a rich music library) to gain significant additional traffic to their sites by optimizing these data sets for the appropriate vertical search.
Meta search engines are search engines that aggregate results from multiple search engines and present them to the user. The two most well-known ones are MetaCrawler and Dogpile. However, their cumulative search volume is quite small, and these do not factor into SEO strategies.
Vertical search can also come from third parties. Here are some examples:
Comparison shopping engines, such as PriceGrabber, Shopzilla, and NexTag
Travel search engines, such as Expedia, Travelocity, Kayak, and Uptake
Real estate search engines, such as Trulia and Zillow
People search engines, such as Spock and Wink
Job search engines, such as Indeed, CareerBuilder, and SimplyHired
Music search engines, such as iTunes Music Store
B2B search engines, such as Business.com, KnowledgeStorm, Kellysearch, and ThomasNet
In addition, some companies offer products that allow anyone to build his own search engine, such as Google’s Custom Search Engines, Eurekster, and Rollyo. Also, specialty search engines are offered by the major search engines not covered in this section.
There is an enormous array of different vertical search offerings from the major search engines, and from other companies as well. It is to be expected that this explosion of different vertical search properties will continue.
Effective search functionality on the Web is riddled with complexity and challenging problems. Being able to constrain the data types (to a specific type of file, a specific area of interest, a specific geography, or whatever) can significantly improve the quality of the results for users.
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