Google Hummingbird update is more oriented toward providing relevant answers to the users than punishing content publishers, as was the case with the Penguin and Panda updates. However, it will force content publishers to use SEO that optimizes content for people rather than for machines.
Google’s search looks at hundreds of things or signals and goes through thousands of evaluations before showing results on a search engine page. That it does all that in milliseconds never ceases to amaze me. Determining what the search engine calculates, in what priority or assigns how much weight to each evaluation for a given query is impossible to reverse engineer.
It is clear that Google wants to dissuade all rogue SEO practitioners who use unscrupulous methods to rank up the SERP. Its move to shut down the Keyword Tool and hide search keywords used by website visitors stand witness to this philosophy.
So until we figure out what really works and does not work after this new update, let’s hear what some of the SEO stalwarts have to say about this new change in the algorithm.
Expert views on Hummingbird
According to Amit Singhal, the new update is an effort to match the meaning of the search queries to web documents instead of matching keywords. He also estimates that this change in the algorithm will impact ninety percent of search queries. Here’s what he said about what Google is trying to do with its search tools.
Search Engine Land’s Danny Sullivan had a direct audience with Google executives at the Hummingbird announcement event. According to him, with the Hummingbird update,
- Most of the earlier SEO guidelines stay
- Authentic backlinks still matter and there is no change in their importance
- Human questions will be better answered now than earlier as Google tries to extract meaning out of the query
- Hummingbird will provide information from more relevant and from authoritative sources
It is almost a month since Google declared this change, and you must have already experienced the changes while looking for information. This meaning it derives will come from the structure of the query based on the rules of Natural Language Processing (NLP). For example in the search query “Baseball Bat” (without the quotes) the word baseball modifies the word bat and the intent here is either to get information about baseball bats or buy specifically a baseball bat. Here is the set of results that you get for this query.
However, if you change your query to “bat baseball” (without quotes), the query doesn’t make much sense syntactically. The intent of the query is not clear here, and the lack of structure makes it difficult to extract a meaning from the query. Therefore, for all purposes these are two words – baseball AND bat. As a result Google now ranks a completely different set of websites on its results page. You can see that the results include both baseball bats and cricket bats.
Google’s Scott Huffman says that Hummingbird is a step toward NLP as more people search verbally with their mobile devices and that verbal queries are more conversational than Boolean searches through the search interface.
Who will the Hummingbird update help? Users who articulate their queries in natural language format instead of Boolean queries. Almost everyone now uses a mobile device to surf the Internet. To support these users Google now supports speaking the search query into devices. With these voice enabled features, users can now hope to get relevant answers by simply asking a question like when talking to any other person. The Hummingbird update will help these users get more accurate results without formulating complex Boolean queries that make sense to the machines.
What about content publishers?
The places where Google looks to match queries to the web page may not have changed much. However, the keywords on the page will have to be long and answer specific questions and be semantically connected to the query rather than disjoint multiple occurrences.
Schema markup can also play an important role in this match-up. For example, marking up a baseball bat information page with the Article schema will match up better for the query “Baseball bat buying guide”, whereas the page marked with the Product Schema will match better for the query “Buy a baseball bat” or “where can I buy a baseball bat”. Here the intent in the first question is to get the information, but we can say that the intent of the other query is that the person wants to buy a baseball bat.
Applying the appropriate schema for the web page will establish context for the search engine and help it match the page to the appropriate search query.
Have you noticed any changes in the search results as a user? As a content publisher, do you think the update will affect the way you optimize your content for users and search engines?