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Google Hummingbird Update & Algorithm In SEO (Explained)

This guide explains everything you need to know about the Google Hummingbird Update.

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Below, you’ll get the background and history of the Google Hummingbird algorithm as well as details on how it works today. There’s also a set of Hummingbird SEO guidelines you can follow to conduct to make sure your website and its content meet the Hummingbird quality standards for search engine optimization.

Consider this your ultimate guide to Google Hummingbird, which is an important algorithm to understand for digital marketing and SEO campaigns.

Google Hummingbird

What Is Google Hummingbird?

Google Hummingbird is the codename given to a significant algorithm change in Google Search in September 2013. The Google Hummingbird Update represents the biggest algorithm shift in years because it was an overhaul to the existing core search algorithm.

When Was Google Hummingbird Update?

The Google Hummingbird Update was released on August 20, 2013 and announced one month later in September during Google’s 15th anniversary event. This Hummingbird Update marked the biggest overhaul of the core ranking algorithm in years.

The official announcement by Google to the public for the Hummingbird Update was on September 26, 2013. Google officials said that the algorithm’s codename comes from being precise and fast. You see how Hummingbird fits into the timeline of Google Algorithm Updates here.

Google Hummingbird Update

How Does Google Hummingbird Work?

Google Hummingbird works by matching query context to results by parsing intent. The Google Hummingbird algorithm uses the process of semantic search to consider context and meaning over individual keywords to understand the intent of the query and match relevant pages to the search.

“Hummingbird is paying more attention to each word in a query, ensuring that the whole query – the whole sentence or conversation or meaning – is taken into account, rather than particular words,” said Danny Sullivan, Founding Editor of Search Engine Land, who interviewed Google about the Hummingbird algorithm when it was released. “The goal is that pages matching the meaning do better, rather than pages matching just a few words.”

According to Matt Cutts, former Google Engineer, “Hummingbird is a rewrite of the core algorithm.” While updates like Panda and Penguin were add-ons to the old algorithm, Hummingbird was a completely rewritten code to do a better job of matching the user queries with documents on the Web.

Matt also said that the Hummingbird algorithm affected 90% of all searches but only to a small degree. Panda had a noticeable impact of 11.8% on search result rankings and Penguin affected 3.1%. However, Hummingbird did not send as big of a shockwave through the SEO community with major fluctuations in website rankings because it was just a readjustment of the core algorithm code that would work as a new foundation for future algorithm updates to build upon.

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Amit Singhal, Google Search Executive, said: “Hummingbird is not only a complete search system on its own, it’s the foundation for what we’ll build in the future.” The Hummingbird Update is especially necessary for natural language queries that have many words in them because sometimes individual words matter for the intent while other times they don’t. With the old Google algorithm, exact match keywords were very important for returning relevant documents to the users in Search. As were the quantity and quality of backlinks.

However, manipulative SEO practices like keyword stuffing and link schemes would trick the Google core algorithm to rank web pages that were not necessarily the best match for the intent of the searcher. Hence, the development of the Panda and Penguin Updates to tackle those issues and remove web pages like that from the search engine results pages (SERPs).

Now with the Hummingbird algorithm, Google can examine elements outside of exact match keywords and backlinks to determine the best pages for the user based on intent. For example, a user may search in Google for “what is the best place to eat Italian food in New York?” Hummingbird’s algorithm can detect that the word “restaurant” would be a better substitute for the word “place”. And, the documents returned in the SERPs do not need to have that exact keyword phrase on the page to rank. Bill Slawski explains more about substitution rules and co-occurring terms in this article on SEO By the Sea about the algorithm patent.

Google Hummingbird Algorithm representation

Google Hummingbird Algorithm and Semantic Search

The Google Hummingbird Update marked the beginning of Google’s algorithm shift to semantic search. (It was later improved upon by Google RankBrain in 2015 with machine learning.)

Previously, the Google algorithm relied on lexical search which is a data searching process that looks for literal matches of the query or close variants in documents without understanding the meaning of the terms. Semantic search, on the other hand, aims not only to find documents with keywords but also to determine the intent and contextual meaning of the words the person is searching for on Google.

You can see semantic search in action by searching for “potato salad” on Google. Without any other words added to the query, Hummingbird’s algorithm can extrapolate that you want to be served up results that are recipes for potato salad and not necessarily the definition or history of this food. There are billions of web pages on the Internet that contain the phrase “potato salad” and Google’s Humminbird algorithm can determine the best results for that query based on user intent.

It’s also important to point out that the Hummingbird Update also helped the core ranking algorithm understand real-world entities and their relationships to each other. This improved the data that is shown in the Knowledge Graph (a feature that was released one year prior to Hummingbird) which contains a database of billions of facts about people, places, and things. Without the Hummingbird algorithm, Google would not be able to apply the meaning behind certain words or entities and their connected relationship to each other in the Knowledge Graph as accurately as it does now.

Google Hummingbird SEO Guidelines

The following Google Hummingbird SEO guidelines can be helpful for improving your content for search engine optimization. As mentioned previously, the Hummingbird algorithm was later improved upon by RankBrain. You can think of Hummingbird as the “memory” and RankBrain as the “thinking” behind Google’s core ranking algorithm. By following these SEO tips below, you can have a better chance of ranking your content in the SERPs.

  • Ensure that natural language is reflected in the content. Make it readable and understandable by humans.
  • Use best practices for on-page SEO to include your target keywords on the page without keyword stuffing. See this related guide on how to add keywords to a website for SEO.
  • Use synonyms for important keywords in the content so you’re not just repeating the same word or phrase continuously throughout the page (i.e., over-optimization).
  • Included entities on the page that are closely related to the main topic. For example, if you’re writing about air conditioners, then you should also make sure to use words like “thermostat”, “compressor”, “temperature”, etc. If you’re writing about a food recipe versus a definition for a food item, then include words like “recipe”, “ingredients”, “oven”, etc in the content. Otherwise, Google’s algorithm may not detect the page is about that subject matter.
  • PageRank and inbound links continue to be important for the Hummingbird algorithm, so make sure to build high-quality backlinks for SEO.

Google Hummingbird Update & Algorithm Summary

I hope you enjoyed this guide on the Google Hummingbird algorithm.

As you discovered, the Google Hummingbird Update was a significant change to the core ranking algorithm because it was an overhaul to how Google indexed and ranked web pages in the SERPs. The Hummingbird Update in SEO was important for shifting to semantic search where the intent and context behind the query were also considered for ranking relevant pages in the search engine.