14 October 2016 | Team Tamar

Zoogle: Google’s Algorithm Menagerie #4 Hummingbird

Welcome to Zoogle, your five-part guide designed to help you navigate Google’s algorithm. We’re going to be taking you through the details of a few of Google’s major algorithm updates; from their purpose and use to their impact on SEO practices.

Fourth in the zoo of Google’s algorithm updates is Hummingbird. We’ll be telling you everything you need to know about the use and objective of the update.

What is Hummingbird?

hummingbird

Whereas other animals in the Zoogle are algorithm updates, Hummingbird is a whole new way to search. If the other algorithm updates – Pigeon, Panda etc – are engine parts, then Hummingbird is a whole new engine.

Hummingbird was launched in August 2013. Hummingbird is notable for being the first step towards Google’s later ability to look for user intent in search queries. Google said the name came from the nature of the algorithm designed to be ‘precise and fast’.

Google has always had the ability to match synonyms – through latent semantic indexing – but Hummingbird was more focused on longer query strings and working out what impact they had, something that would later be built upon and integrated into Google’s machine learning software RankBrain.

latent-semantic-indexing

Hummingbird was designed to pay attention to each word in the query and make sure that the whole query, rather than just the individual words, was taken into account.

How Hummingbird changed SEO

user intent

Hummingbird was the first step towards user intent, some would say the first step towards RankBrain’s launch. It started to change content towards being more conversational and user intent focused, rather than the traditional keyword-focused content that was prior to it.

Hummingbird provided a well-needed update to voice search. Voice search continues to increase, even now in 2016, and voice searches are very conversational, where we speak the entire query rather than saying just the keywords. So Hummingbird helped to parse natural language into a usable search query, using the very latent semantic indexing technology discussed previously.

As Hummingbird added more weight to longer-tailed queries, content based around these long tail queries started to rank higher. This made query-based content, e.g. any content based around who, what, where, why, when, how etc, more relevant and easier to rank.

big data

Hummingbird also meant that searches did not look for the traditional exact match keywords, and would rather show results that were based around the same ‘theme’ of the search.

How Hummingbird affects us now

Hummingbird can be considered the first iteration of RankBrain, so it was the first step towards user intent. Consequently, we don’t now consider Hummingbird when creating content – RankBrain is the new judge for user intent.

Want to know more about how to optimise for RankBrain? Check out everything you need to know here.

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  • http://www.niksto.com/ Emmerey Rose

    Interesting read. Thanks for sharing. This is very informative. But I was wondering, do you have any tools to recommend? 🙂