I was moderating a roundtable session at SES Chicago
last week when the conversation turned to Hummingbird and how –
according to the young lady I spoke to – it effectively means "Google is
simplifying the query from long tail to shorter terms."
I've heard this point of view a few times over the past few weeks, and fundamentally disagree.
To (hopefully) put a different lens on the simplification argument, I'm laying out a few concepts for discussion. "Simple Queryists" please feel free to slice, dice, dissect, and discuss below.
Do a search for [Where can I buy a Larry Bird shirt]. Simple Queryists think that Google will shorten the query to something like "Buy Larry Bird Shirt" and match a result accordingly.
I don't believe this is what's happening. Rather, Hummingbird is adding a layer of understanding to the query that acts more like an expansion of the query, so that its meaning is clearer.
In this example, I'm thinking Google would interpret the query as the following:
Let's break that down a little further, most importantly to see the implied connection between person and product.
With better meaning and context of understanding, I believe Google is connecting, via their Knowledge Graph, Larry Bird the basketball player with the context of the team he's most famous for, and the product that matches the shirt number he wore. The query is then more exact, and should return a more exact and relevant result. By using prior search data along with "big data," – other user behavior factors – the new more intelligent algorithm can predict the best match of content to a user's intent.
And that's a fundamental difference. Intelligence through experience and predictability is applied to improve results. And, if it's not exactly a match, real-time user behavior – clicks, query modification, dwell time and page interactions – can help finesse future results.
I've heard this point of view a few times over the past few weeks, and fundamentally disagree.
To (hopefully) put a different lens on the simplification argument, I'm laying out a few concepts for discussion. "Simple Queryists" please feel free to slice, dice, dissect, and discuss below.
Do a search for [Where can I buy a Larry Bird shirt]. Simple Queryists think that Google will shorten the query to something like "Buy Larry Bird Shirt" and match a result accordingly.
I don't believe this is what's happening. Rather, Hummingbird is adding a layer of understanding to the query that acts more like an expansion of the query, so that its meaning is clearer.
In this example, I'm thinking Google would interpret the query as the following:
Where (Place: User is located) can I buy
(Intent: Purchase) a Larry Bird (Person: basketball player) shirt
(Product: [via Association of Product to Player to Team] Boston Celtics
shirt #33)
Not exactly shorter.Let's break that down a little further, most importantly to see the implied connection between person and product.
With better meaning and context of understanding, I believe Google is connecting, via their Knowledge Graph, Larry Bird the basketball player with the context of the team he's most famous for, and the product that matches the shirt number he wore. The query is then more exact, and should return a more exact and relevant result. By using prior search data along with "big data," – other user behavior factors – the new more intelligent algorithm can predict the best match of content to a user's intent.
And that's a fundamental difference. Intelligence through experience and predictability is applied to improve results. And, if it's not exactly a match, real-time user behavior – clicks, query modification, dwell time and page interactions – can help finesse future results.
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