{"id":14533,"date":"2019-11-12T13:13:44","date_gmt":"2019-11-12T13:13:44","guid":{"rendered":"https:\/\/trafficradius.com.au\/?p=2559"},"modified":"2024-03-21T11:38:50","modified_gmt":"2024-03-21T11:38:50","slug":"google-hummingbird-search-engine-algorithm","status":"publish","type":"post","link":"https:\/\/trdemo.com.au\/demo-traffic\/blog\/seo\/google-hummingbird-search-engine-algorithm\/","title":{"rendered":"Google Hummingbird \u2013 The Core of Modern Search Engine Algorithm"},"content":{"rendered":"\r\n

Unlike the previous\u00a0Panda<\/a>\u00a0and\u00a0Penguin updates<\/a>\u00a0which were initially released as add-ons to the existing algorithm, Google Hummingbird has been cited as\u00a0a complete overhaul of the core algorithm<\/a>.<\/p>\r\n\r\n\r\n\r\n

While it\u2019s believed that many preexisting components of the core algorithm remained intact, Google Hummingbird signaled its commitment to an increasingly sophisticated understanding of the\u00a0intent\u00a0of searchers\u2019 queries with the goal of matching them to more relevant results.<\/p>\r\n\r\n\r\n\r\n

Google announced Hummingbird on September 26, 2013<\/a>, but it had actually already been in place about a month prior.<\/p>\r\n\r\n\r\n\r\n

Whereas previous algorithm updates like Panda and Penguin sparked significant reporting of lost traffic and rankings, Google Hummingbird did not appear to have drastic negative impacts on the general web.<\/p>\r\n\r\n\r\n\r\n

It was largely understood as having a positive influence on the accuracy of Google\u2019s knowledge base known as the \u201cknowledge graph<\/a>.\u201d<\/p>\r\n\r\n\r\n\r\n

However, the local SEO community theorized that\u00a0documented effects had been felt<\/a>\u00a0in the local search engine results.<\/p>\r\n\r\n\r\n\r\n

The Power of Semantic Search Unleashed With Google Hummingbird<\/h2>\r\n\r\n\r\n\r\n

In order to fully understand the purpose behind Hummingbird, it\u2019s important to first be familiar with the search engine features it most heavily impacted: semantic search and the knowledge graph.<\/p>\r\n\r\n\r\n\r\n

A year prior to the release of Hummingbird,\u00a0Google launched its knowledge graph<\/a>:\u2002not an actual graph, but rather<\/a>\u2002a\u00a0set of SERP features<\/a>\u00a0designed to provide quick, accurate answers to users\u2019 queries about people, places and things.<\/p>\r\n\r\n\r\n\r\n

You can see the knowledge graph in action on the results page yielded upon searching for \u201cchocolate chip cookies.\u201d The SERP does contain standard organic results and links to suitable websites, but it also contains a rich set of knowledge graph data, including an answer box with a recipe, a right-hand knowledge panel featuring nutritional facts about this dessert, and suggestions for related search subjects.<\/p>\r\n\r\n\r\n\r\n

How Does Google Understand the Search Intent of the User and Present the Relevant Results?<\/h2>\r\n\r\n\r\n\r\n

This is where semantic search comes in.<\/p>\r\n\r\n\r\n\r\n

Semantic search<\/a>\u00a0attempts to match appropriate SERP results to the language of Internet users\u2019 queries\u00a0beyond the meanings of individual keywords, taking a broader context into account, even when the intent of the user is implicit rather than explicit.<\/p>\r\n\r\n\r\n\r\n

To see semantic search in action, take a look back at the \u201cchocolate chip cookies\u201d SERP image above.<\/p>\r\n\r\n\r\n\r\n

Even though the query was for the cookies themselves, semantic search extrapolated that despite the words used in the search query (which, taken literally, could refer to images or a definition of the cookies), the\u00a0intent\u00a0behind the search differed than the language used.<\/p>\r\n\r\n\r\n\r\n

Google then served up results that match the searcher\u00a0intent\u00a0rather than searcher\u00a0language.\u00a0As a result, we see a SERP full of recipes, nutritional facts, and other topics related to\u00a0making\u00a0or\u00a0eating\u00a0the cookies.\u00a0<\/p>\r\n\r\n\r\n\r\n

Google\u2019s increasing mastery of semantic search enables them, in their own words, to understand \u201creal-world entities and their relationships to one another.<\/p>\r\n\r\n\r\n\r\n

Hummingbird\u2019s focus on matching query context to results relies on the intelligence of Google\u2019s technology \u2014\u00a0the ability to parse intent.<\/p>\r\n\r\n\r\n\r\n

Semantic search is a complex topic and you can read all about it on\u00a0another blog of mine<\/a>.<\/p>\r\n\r\n\r\n\r\n

Here\u2019s another example of how this works on the web.<\/p>\r\n\r\n\r\n\r\n

Imagine that a Google user types or speaks this search engine query:\u00a0best place for Chinese.<\/p>\r\n\r\n\r\n\r\n

It\u2019s only by understanding the\u00a0intent\u00a0and\u00a0context\u00a0of such a query that Google can identify \u201cplace\u201d as signifying a restaurant and \u201cChinese\u201d as signifying a specific type of restaurant food.<\/p>\r\n\r\n\r\n\r\n

How Google Hummingbird Scores Brownie Points<\/h2>\r\n\r\n\r\n\r\n

One of Hummingbird\u2019s main purposes was to translate semantic search from a concept to a reality \u2014 and one that would ultimately become the search standard.<\/p>\r\n\r\n\r\n\r\n

It sought to better approximate the true intent of searches so that, for a query like the Chinese food example referenced above, the user receives a set of local Chinese restaurants to choose from, rather than results for the best places for people to live in China or other irrelevant data.<\/p>\r\n\r\n\r\n\r\n

Google Hummingbird and Voice Search<\/h2>\r\n\r\n\r\n\r\n

In retrospect, the Hummingbird update could be seen as a step on Google\u2019s path toward mastery of the inevitable rise of voice search.<\/p>\r\n\r\n\r\n\r\n

When Hummingbird was released in 2013, \u201cconversational search<\/a>\u201d was making waves in the SEO community.<\/p>\r\n\r\n\r\n\r\n

Fast-forward just a few years and the emergence of\u00a0voice search<\/a>\u00a0obliges Google to be equipped to understand fully natural language, using the spoken word for searches like, \u201cWhere\u2019s the cheapest place for Mexican food near me?\u201d or \u201cHow can I fix a leaky kitchen faucet?\u201d<\/p>\r\n\r\n\r\n\r\n

In sum, technology that is limited to understanding search language keyword-by-keyword isn\u2019t up to the task of gauging intent.<\/p>\r\n\r\n\r\n\r\n

Developments like semantic search and an update like Hummingbird are designed to operate effectively in a world of natural language.<\/p>\r\n\r\n\r\n\r\n

Google Hummingbird and Local Search<\/h2>\r\n\r\n\r\n\r\n

While the\u00a0organic SEO<\/a>\u00a0community strove to understand the effects of Hummingbird on the general web, the local SEO community dealt with some perceived fallout from the update.<\/p>\r\n\r\n\r\n\r\n

Initially,\u00a0Hummingbird appeared to populate numerous local results with unsatisfactory \u201cone-boxes<\/a>\u201d.<\/p>\r\n\r\n\r\n\r\n

For example, a query like \u201cMelbourne attorney\u201d would yield a single answer set apart in a box instead of the typical set of local businesses.<\/p>\r\n\r\n\r\n\r\n

As many of these one-boxes rewarded businesses that were spamming Google via the use of false business names (e.g. listing themselves as \u201cMelbourne attorney\u201d instead of their actual name of \u201cLaw Office of Jim Davis\u201d), Google was failing to deliver a relevant experience to users. This frustrating scenario was eventually resolved for the most part.<\/p>\r\n\r\n\r\n\r\n

Additionally, the local SEO community speculated that Hummingbird may have had the following effects on the local and local organic results:<\/p>\r\n\r\n\r\n\r\n