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Understanding the Long Tail of the Keyword Demand Curve :

It is wonderful to deal with keywords that have 5,000 searches per day, or even 500 searches per day, but in reality these “popular” search terms may actually comprise less than 30% of the overall searches performed on the Web. The remaining 70% lie in what’s commonly called the “long tail” of search (as published at http://moz.com/blog/rewriting-the-beginners-guide-part-v-keyword-research); The tail contains hundreds of millions of unique searches that might be conducted a few times in any given day, or even only once ever, but when assessed in aggregate they comprise the majority of the world’s demand for information through search engines.

Impact of Google Hummingbird:

In September 2013, Google announced, to coincide with its 15th birthday, a major change to their search algorithm called “Hummingbird” (http://insidesearch.blogspot.com/2013/09/fifteen-years-onand-were-justgetting.html). Since then, although Hummingbird has been much discussed, Google has not released any more official documentation on this update. Hummingbird is a major change to the way Google interprets searches and in how searches relate to one another. Hummingbird is intended to get at the heart of what the user wants, not just the exact keywords they search for. In large part, this is related to Google getting more prepared for mobile search. In mobile searches, users are less apt to type traditionally formatted queries, and in many cases, actually do voice searches resulting in conversational queries. When users use voice search, their queries may also be much more conversational in format, such as please find me the closest gas station. In addition, with Hummingbird, Google will use many other factors to determine the intent of the user, such as considering previous related searches by that user.  The Hummingbird algorithm attempts to determine the true meaning behind what a user is searching for, rather than simply returning results for the exact query they use. Indeed, in many cases, Google may simply relate the terms and consider them synonyms for the purposes of returning search results.  Let’s look at this hypothetical, yet fully-functional, example of a series of queries that a mobile user might ask Google:

“where is the empire state building?”

“who built it?”

“pictures”

“how tall is it?”

“restaurants”

“Italian restaurants”

“show me the second one”

“directions”

No longer is Google’s response to the query “where is the empire state building?” simply the web page that is most closely optimized to that search query. Google uses its knowledge of entities, which include notable buildings and monuments and their attributes and locations, nearby restaurants, average review ratings of those restaurants (partially from semantic markup), and so forth, to return meaningful responses to these queries, including actual Italian restaurant recommendations near the Empire State Building, rather than merely a match on the keywords searched.

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