We all know the power of Google and its search engine in terms of helping billions of people search what they want and get relevant answers. This is also the main reason why people make resources so that people get what they want. Having said that, we know Google is doing a lot where they don’t need to redirect users to the third party but rather have the information on their own search engine only. One of those features is the “people also ask” as well as “autocomplete suggestions” feature.
Now, we have always been intrigued as to how Google generates those autocomplete suggestions that are mostly accurate to what the user is searching for which means they have a higher chance of clicking on those suggestions. Google’s Danny Sullivan says that when generating autocomplete suggestions, they look for the following factors:
- Trending queries
- Language of the searcher
- Where the searcher is located
He also explains that long-tail queries might be different because they are far less common and an average user might not search for them on a regular basis.
These type of suggestions are generated differently. Danny explains it by giving an example. He says “For example, we might not see a lot of queries for “the name of the thing at the front” of some particular object. But we do see a lot of queries for “the front of a ship” or “the front of a boat” or “the front of a car.” That’s why we’re able to offer these predictions toward the end of what someone is typing.”
He also says that when autocomplete suggestions are not generated, it might be because of these two reasons:
- Automatically: Systems are designed to prevent potentially unhelpful and policy-violating predictions from appearing.
- Manually: If Google’s automated systems don’t catch predictions that violate its policies, there are enforcement teams that remove predictions in accordance with those policies.
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