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Query Counting - The Simple Yet Powerful Method of “Page Weighting”

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Put keywords in h1, h2, h3, title, description, 28x in content

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But, tracking by a small fraction of served queries is generally less efficient

50 keywords tracked of…….

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So why don’t we look at the bigger picture?

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So, we decided when we built SEO stack to make ALL queries accessible

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But, seeing the aggregated total of queries alone just isn’t enough so we decided to visualise it

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This is a line showing the total number of queries a URL / Group was served in Google on any specific day over the reporting timeframe

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Impact analysis is MORE efficient when you aren’t tracking a fraction of the pages “queries”.

Track 50 fixed keywords or track total page weight

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Pages can often have THOUSANDS of queries, so tracking a narrow handful of them for impact analysis is LESS EFFICIENT

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So it’s no surprise that……….

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We use this process internally.

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To rank our own assets

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And my own stuff

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How can we use query counting in SEO?

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Understanding content devaluation = loss of perceived value in content & served query counts shrink

This is bad

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We can take a look and see which algo may have kick started decline or contributed

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Here we see PRE-update decline (this happens a lot more than you’d think)

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Here we can see the systemic decline of a URL that spans numerous updates, sometimes it can reveal different updates responsible for losses

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So, devaluation is where the perceived value in content is lost & subsequently the query pool for a URL shrinks.

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Subsequently, we can view content weighting over time - for example we can see content gain weight before clicks come.

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Query counting benefit based on SEO approach

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Deploy content

Get indexed

Tech enhancements

Build links

SEO testing

UXO / Engagement Optimisation

Deploy & Optimise Strategy/Adoption of Work

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Deploy content

Get indexed

Tech enhancements

Build links

SEO testing

UXO / Engagement Optimisation

Optimise & Deploy Strategy

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So in this instance, this URL was a DEPLOY and OPTIMISE strategy we see the initial GROWTH CURVE.��Query counting = impact curve analysis.

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We can observe the shift of queries into position groups - this can be used as a progress indicator BEFORE clicks come.

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We can see as position groups show query count growth we can look at the impact on clicks and impressions.

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Content (niche dependent) may take time to gain momentum i.e. in complex niches (YMYL)

Content uptake period

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As content gains establishment, perhaps long-tail traffic, behavioural data can be collected and used for onward assessment (HCU)

Behavioural Aggregation

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We may see at some point a URL / group reaches peak query serve saturation

Peak Saturation

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So, if we took a keyword i.e. bingo offers no deposit it could take a URL YEARS to gain rank.

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So there’s specific query latency (time to rank)

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And then there’s the overall pages weighting over time

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So there’s specific query latency (time to rank)

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And then there’s the overall pages weighting over time

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SO WHY IS THIS HELPFUL WITHIN SEO?

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It can help us give clients a visual representation of progress that’s not based on just tracking a narrow band of rankings

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We’re looking for a new seo agency, the last agency didn’t get any results in the first 3 months……

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We want to rank top 3 for best secured loans, our new agency should be able to demonstrate fast progress

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Well, I hate to break it to you but……….

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NAUGHTY people

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Yes sir�

3 months we’ll achieve top 3��We can absolutely do it (knowing they can’t)

GOOD people

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Evaluate SERP

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Educate client

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Foster trust

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ok thank you for your honesty we’ll be in touch

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BAD client

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Vanishes / goes elsewhere and follows desperation to rank

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Likely to go for the cheapest option / flashiest promises

GOOD client

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Understands challenge

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Accepts time factor

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Becomes a working partner

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Query counting was our way of better understanding performance & being able to convey to clients.

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DEVALUATION

VS

SLIPPAGE

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DEVALUATION

We see a systemic loss of queries over time (or abruptly)

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DEVALUATION

Loss of positions 1-3 and 4-10 lead to a loss of clicks.

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SLIPPAGE

Overall volume of served queries remains relatively consistent but clicks are lost

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SLIPPAGE

Can be caused by positions 1-3 dropping back into lower position groups i.e. 4-10

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SLIPPAGE

Or something more sinister - positions 1-3 here show fluctuation but aren’t trending down - so why are clicks?

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SLIPPAGE

Think we all know what’s happening here don’t we my friends.

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SLIPPAGE

Traditionally if we saw traffic lost we’d look straight at rankings and page performance

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SLIPPAGE

So we can compare periods, look at where losses originate

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SLIPPAGE

But when the data sets are limited & when we can’t visualise them at scale it can make it harder to understand.

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SLIPPAGE

Query counting at page and domain level makes it EASIER to assess things such as slippage, devaluation or CTR erosion

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Devaluation

Slippage

This is where a page/pages lose served queries. The content is no longer as valued and subsequently loses rank/clicks.

This is where content is still valued but rankings slip back slightly - so clicks are

lost but the content is still perceived to be

of value.

The purple line is a visual representation of how many queries a page/pages were served for in any 1 day.