User journey and the Google Query Log: What most SEOs overlook
There are countless tutorials written on how to do keyword research. Usually a seed keyword is used to retrieve all lexical keywords modifications. Combined with estimated monthly search volume, this data serves as a foundation for defining keyword clusters. These keyword clusters in turn form a topical map. In reality, the produced map is nothing more than a hierarchical concept map. It misses one thing, how real users use Google and navigate the Internet. What is their user journey?
There have been many debates over the importance of click data and search volume data, but there is one valuable source of data for Google, which is completely off the radar for most SEOs; the Google query log.
What is the query log?
The query log is a record of all queries performed on Google by different users. It will contain many different attributes: user identifier, session identifier, device, query issued etc. But why is this data valuable? By interogating it, Google can identify different behaviors common to many users. For example, if Google sees that some queries occur frequently together during one user session, it may start to think that these queries are related.
Google uses this data later for recommending related queries. It can also use this data for identifying query clusters.
How do we find related queries?
Although we don’t have access to the Google Query Log, there are several places where we can look for hints of related queries which people usually search together.
Related searches
In the original patent Google talked about selecting entities from the search result documents, and using these entities to provide query refinements.
Let's take a look at the query “people also ask”. We can see that Google returns not only refinements containing the “people also ask” term, but also related entities.
People Also Ask
Google’s motivation behind creating the People Also Ask feature was, perhaps, that while Googlers were analyzing query logs, they saw that a lot of broader queries are later followed by different question queries. That is what typically happens during the topic research phase. In order to improve usability and save users time, they came up with the idea of displaying short answers to these question queries under the PAA snippet box.
These questions are closely related to the initial seed keyword, and signal the additional information needs a user may have. They give a broad overview of a topic and may surface additional adjacent topics. The selection of these questions is tied to underlying topic sets (search queries associated with the search result documents), which is again an indicator of real user behavior and preferences.
Google Autocomplete using the previously submitted query
This Google feature was initially spotted we spotted at KeywordsPeopleUse during Google patent research, and then implemented in our innovative Semantic Keyword Research Tool.
This is the strongest indicator of co-occurring queries, or, in other words, the queries that people frequently search together.
Traditional Keyword Research vs Relevance Based Keyword Research
Traditional keyword research tools usually provide just a list of keywords all of which include some seed keyword. It makes them less useful for longer queries or more niche concepts. See the example below
On the other hand, all the aforementioned methods of mining related search queries combined together in a single Keyword Clustering Tool can yield far superior results.
The benefits of understanding the user journey concept
The main benefit of understanding the user journey concept is to better understand the minds and motivations of your customers and be able to craft a properly tailored topical map for them: identify the main topic of the site and its subtopics, identify broader topics which are related to the main topics of the site, and properly link between them. This is where the topics and connections between them are based not on lexical relationships between words, but on past user behavior reflected in query logs. Using the tools outlined in this article can facilitate the creation of such topical maps.
For example, for queries about keyword research we observed some other co-occurring queries related to AI website builders and AI video editing tools. These broader topics may well resonate with parts of your audience, and be a signal for creating an article, for example, about “Top AI website builders”, and then linking to “Best keyword research tools” from this article, thus creating a proper contextual transition.
Author:
Edd Dawson
Founder KeywordsPeopleUse
