Since the fall of 2013, the Google Hummingbird algorithm has been leaving its imprint on search results for billions of queries and providing end users with another small step toward a more intimate, personal search engine results page.
It’s easy to forget that the current days of micro-moments and having Google answer contextual questions are a massive change from the first 20 years of search, where specific keywords dictated the results rather than having the entire query be taken into consideration.
To fully grasp the significance of the Hummingbird update, let’s cover what search was like before the release, what Hummingbird was designed to do, and why it truly changed our lives.
What Search Was Like Before Hummingbird
Back in the summer of 2013, the basics of SEO were still more or less the same as they are today.
We were still living in a time of “great content” and espousing the earning of links (rather than buying or scheming for them), and being able to answer questions that people cared about was still the goal.
However, the search results landscape was drastically different, even just one year before Hummingbird’s launch.
2012: The Infancy of the Knowledge Graph
One year before Hummingbird, search results gave you exactly what you put in – be it a single word (“games”), a long-tail string (“who is the mayor of Burlingame, CA?”), or even a well-known abbreviation (“NBA”).
This came with a catch. The results would often be on-the-nose, not providing any in-depth answers or resources about the query.
Being able to parse intent was still a challenge that Google faced, especially when it came to being able to distinguish between two similar-but-different queries.
One great example involves music and theater. If I ask Google about “the Globe” now, I get information about the famed Globe Theatre associated with William Shakespeare.
If this had been searched in 2010, perhaps you would have received information about globes of the Earth, or the home page of the Global Learning and Observation to Benefit the Environment (GLOBE).
Fortunately, this is what search looks like today.
But let’s say that I’m not much of a fan of actors, and what I’m truly looking for is information about a song from 1991. Today, I can simply fine-tune my query and Google will retrieve information that answers my question.
If this had been five years ago, there’s a good chance that misclicks and additional queries may have been needed to yield the correct results centered around Big Audio Dynamite II.
Better yet, this great leap in understanding context meant that when you were looking for a precise answer, Google gave it to you.
Just don’t ask it something silly like “What car does Jesus drive?” or you might receive the wrong answer (and a lesson in anachronisms).
When it was announced in May of 2012, the general idea behind the Knowledge Graph was that by providing information with context “Paris Hilton Lodging”, you would receive availability info about Hilton Hotels on the Seine (rather than results centered around the heiress of a similar name).
What makes the Knowledge Graph notable is that it serves as a harbinger of Hummingbird’s desire to answer questions in a “things, not strings” manner, the next logical step in a long line of updates from the search giant.
2011-2014: Google Authorship
During the early days of Hummingbird, queries were answered to the best of their abilities given the content that was available.
While the Knowledge Graph had been slowly rolling out for over a year prior to Hummingbird, it was also coming during a time that Google experimented with pointing search results toward content written by trusted sources based on its 2009 Agent Rank patent, which coupled with structured data markup, gave us a brief program known as Google Authorship.
Google’s authorship update went live during the same timeframe as Google+, allowing for content creators to use an internal system to connect publishers with their publications. Being able to identify associations based on Google+ profiles and rel=”author” markup meant that attribution could be identified easier, and those who produced content that gave way to increased engagement by way of trust would gain as well.
To go into why authorship gradually fell out of service during 2014 would require an article all on its own. Most industry experts and Google insiders point to low adoption rates, marginal changes in end-user behavior on the SERPs, and the fact that Google is always testing as their reasons why authorship faded away.
What Were the Goals of the Google Hummingbird Update?
The Hummingbird update – which was announced September 26, 2013, but had actually rolled out the prior month – was the next evolutionary step after 2010’s Caffeine update and other significant changes that influenced how users respond and engage with search results.
Hummingbird marked a huge advancement in Google’s search technology. It impacted about 90 percent of searches worldwide.
Hummingbird’s focus centered around three key components:
1. Conversational Search
By being able to use natural language processing, search results would be able to retrieve niche results for queries both at the head and long-tail level.
By being able to gauge intent in a semantic manner, Hummingbird sought to allow users the ability to confidently search for topics and sub-topics rather than having to engineer queries using Google-fu.
2. Human Search
If you used a search engine during the early days of the Internet, you’ll recall how it could be difficult to find what you’re looking for when your knowledge of a particular subject was lacking.
Hummingbird sought to solve this by focusing on synonyms and theme-related topics.
Of course, England doesn’t have a President, but they do have both a Head of State (Monarch) and a Head of Government (Prime Minister), similar to other nations (France has both a Presidential Head of State and a Prime Minister as Head of Government) but dissimilar to the United States, where the President occupies both roles.
By being able to allow for people to know what they don’t know and curate results that help users find what they’re looking for, Hummingbird helped inch Google one step closer to becoming a bit more human.
3. The Foundations of Voice Search
As Hummingbird used context and intent to deliver results that matched the needs of the user, local results became more precise.
If you were looking to find a great Vietnamese restaurant in a U.S. city during the old days, you’d have to carefully choose your nouns and avoid colloquialisms, or else you’d receive results for Ho Chi Minh City and Hanoi!
At face value, those are great cities for Vietnamese food by default, but is that what you were looking for in the first place? Of course not! Because “food” is served at a “restaurant”.
The combination of conversational language processing and understanding human intent based on location data meant that what we take for granted today is the result of years of turning the dream of semantic search into a reality.
Improving Local Search
By using semantic search and natural language processing that take into account how humans think, desire, and use search engines to find what they’re looking for, the old practice of speaking to the keywords in a local business website’s title and meta descriptions became a bit less important.
This meant that the long-held practice of spamming a page with keywords and fake business names was weakened yet again, although this is still a practice that many use (often successfully) to this day.
For the pessimists, this was a feature that was seen as a bug, which placed blame at the feet of the algorithm for punishing businesses that utilized face-value tactics for years. For the optimists, it meant a chance at using more words in the dictionary, outlining Who/What/Why/Where/How – just like humans do.
The Hummingbird update holds a reputation as the biggest game-changing move that Google had made upon its release.
But was it?
It depends on how you look at it, and what it actually did for users and the world of organic search.
The update itself didn’t cause havoc on the SERPs like its Panda and Penguin predecessors did, nor was it the first step in using a data-linked approach to providing trusted results for queries. It wasn’t even the first major step in improving the indexation of information.
Hummingbird might not have been the sea change many have built it up to be, but that doesn’t mean that its significance should be diminished.
Hummingbird stood on the shoulders of Caffeine, the Knowledge Graph update, and the now-defunct authorship push, resulting in what many cite as a major overhaul of Google’s core algorithm rather than an add-on.
Google didn’t replace the search engine’s air filter. It replaced the entire engine block.
Google’s focus on serving results that cater to the human side of user intent was felt across both head-level and long-tail queries, and to this day, when a user looks to capitalize on “micro-moments” that cover topics to Do/Know/Go, Hummingbird deserves its fair share of thanks.
Featured Image: Shutterstock, modified by Danny Goodwin.
Pre-Hummingbird Nutrition Screenshot by Bruce Clay. Taken December, 2012.
2013 Hummingbird Image Screenshot by StorEDGE. Taken March, 2014.
All Other Screenshots by Beau Pedraza. Taken November, 2017.
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