Initially, Google’s core algorithms were focused on ranking its index of websites in an ordered list in relation to a user’s query. As the algorithms matured, Google incorporated artificial intelligence to try to better understand what a user is seeking and to help them search better.
(Tangential note: Google recently announced they have deployed a neural learning algorithm called BERT which is focused just on better query matching. )
With this goal in mind, Google uses a few very visible tools:
- “Did you mean” – When Google believes you meant to search for something other than what you typed, they will suggest another query. Depending on how certain they are of this other query, they might show the new query’s results by default or just give a clickable link to run that new query. This feature frequently comes up on misspells, but it will also be triggered by other signals like word combinations or location.
- Google suggest – As a user types a query, Google will be one step ahead of the user and try to determine what the user is seeking. Naturally, this will push users down certain query funnels that they might not have used if they were left to their own devices. Google suggest is constanty running and you can see how useful this is just by typing one letter into Google and not hitting enter. The engine for Google suggest comes from real time queries of other users and not simply a guess on what Google thinks users should be searching. This feature was recently dissected in a Wall Street Journal investigative report which claimed that Google scrubbed Suggest to push people down paths that they (Google) wants. In my opinion, this is highly unlikely, but nonetheless the power of this feature in directing search users.
- Related queries – Very similar to Suggest, Google helps people discover new queries that might better help them to find what they seek, but instead of doing it in real time, Google just links to other queries that will kick off a new search.
- People also ask – This is a new feature in Google’s results which both kicks off a new search and will also (many times) display a featured snippet response to the question. This is a particularly interesting feature in Google search and highlights the answering feature of search that Google might prefer.
In the early days of search and SEO, websites were very focused on ranking at the top of the results page on specific terms which were assumed to have high monthly search volume. Due to the immature (at the time) algorithms of search engines, users had been trained to only use those big head terms if they wanted to find useful results.
With all of Google’s features aimed at getting users to search better, I would argue that the entire idea of a head keyword is obsolete. Generally, super head terms like “hotel”, “car” “restaurant” and similar will yield such useless results that Google already modifies the results for these queries based on location. This means that no single website could rank nationally (or globally) on these terms for all searches.
Head search is a waste of time
Additionally, if a user were to search these terms, Google would push them down a more specific path that better matches what they are seeking. I have also noticed that all of these search suggestions are completely personalized based on my past search behavior.
There was a time when Google personalized search results based on specific users past searches but they deemed that to be unsuccessful. Instead Google uses past search behavior to help a user search better.
Here’s an example of personalized “People also ask”. If I search for things to do nearby on a rainy day, Google will help me to refine my query with locations I have actually been.
If I conduct the same query in an incognito window, my suggested questions are completely different
The same would also apply for Google suggest. Suggested queries will change based on time of day:
And past search behavior as I was just search food, the first suggestions are food related.
I have not seen related queries change that much, but that is likely because they are part of a query set. Once Google pushes a user into one query set the related queries are already relevant for that query and don’t need any further personalization.
What this all means, is that the idea of trying to rank on a single popular head term would likely not work out as intended by the website. Due to the non-specific nature of their search, the users that might click through on such results would just be tire kickers rather than actual buyers.
Rather than trying to rank on head terms, websites should focus on understanding their users just the same and target the keywords that they would search in reality. A novel concept for sure, targeting users instead of search engines.