What is Google Hummingbird?
Google Hummingbird is another significant Google update. Rolled out in 2013, Google Hummingbird is Google’s biggest change to their algorithm since 2001.
Previously, Google matched the keywords in a search query to the content that is displayed in the search results. With the hummingbird update, Google focuses on displaying results and content that matches the user’s intent. It was also an important update for voice search, as it enabled Google to better understand and respond to more conversational queries.
Despite being a significant update, it is now listed as a retired ranking system, which means that it has since been updated and replaced.
How Does It Work?
Let’s take a look at an example:
Let’s say you are searching for the “best pizza in Queensland”. Previously, Google would primarily focus on matching the keywords “best pizza” and “Queensland” in the search to the same keywords in the content of web pages.
As a result, it might have returned results for pages that simply listed pizza places in New York. While still an appropriate answer, Google did not consider the overall quality or reputation of those places.
With Hummingbird, Google’s algorithm started to understand the intent behind the search query. Instead of just matching keywords, it would consider the context and relationship between the words. So, for the same search, Hummingbird might prioritise results that:
- Were highly rated and reviewed: This would indicate that the pizza place is considered “best” by many people.
- Had a reputation for excellent pizza: The content might discuss the quality of ingredients, cooking methods, or unique toppings.
- Were located in popular tourist areas: This would suggest that the pizza place is well-known and often recommended.
Benefits Of Google Hummingbird
According to Matt Cutts, former Google Software Engineer, this update affected around 90% of search results. While these changes were subtle it still brought several important benefits:
- Improved understanding of intent in search queries
- Better understanding of the context and relationship between words
- More effective at understanding questions and long-tail keywords and queries
- Improved user experience by providing more precise and relevant results
- Incentivised more natural and informative content, instead of keyword stuffing
