Based on his analysis of 90M datapoints, Mark Williams-Cook, who promises more insights in his Core Updates Newsletter shared this valuable, charted insight into how Google sorts queries into one of these 8 categories.
- SHORT_FACT e.g: “how much does abiraterone cost in the uk”
- OTHER e.g. “what do chefs say about air fryers”
- COMPARISON e.g “curtain wall system vs window wall system”
- CONSEQUENCE e.g. “what happens to asparagus if you let it grow”
- REASON e.g. “why was abilify taken off the market”
- DEFINITION e.g. “what is a birthday costume”
- INSTRUCTION e.g. “what’s the best way to cook an artichoke”
- BOOL e.g. “can i become a agile coach with no experience” (Aka seeking a True/False answer)
SEOs like me want to jump right away into implications for content creation, but let’s just give the datapoints the isolation they deserve before jumping right into correlations, or action items.
A Warning About Jumping To Conclusions
My own initial question on the data was “Does addressing multiple intents in a single post outperform posts that narrow down to a single semantic angle of a query?” and here’s what Mark thought about that:
1/ Trying to apply this classification in a vacuum, 1 dimensional way to ranking is not even close to how ranking works or what Google is using that classification for
2/ It’s a query, not page classification. So “ranking” in this context means what? What if a page ranks for 100 queries and 99 of them are “how to” but 1 is “comparison”? Are you asking if you would rank better for a “how to” query because it is sometimes linked to a comparison query?
3/ Draws helpful thinking away from things it is used for, like various post-ranking feature gen