Google gives us two sources of keyword data: Google Search Console and Google Keyword Planner.
Now, Search Console is likely the most accurate source of keyword data.
And it's a place where SEOs go to analyze their impressions, clicks, and keyword rankings.
But in order to see this data, you have to actually rank for these keywords.
And that's why SEOs often turn to Google Keyword Planner to do keyword research.
Now, like all SEO tools, Keyword Planner comes with some shortcomings like the fact that they
group keywords with similar meanings, and they round search volumes into buckets.
And these two things can sometimes make their search volume estimations a bit questionable.
Now, for whatever reason, a lot of marketers overlook these shortcomings and think that
Keyword Planner is the most accurate keyword research tool because the data comes directly
from Google.
Well, we looked at data for over 72,000 random keywords and compared impression data from
Google Search Console with search volume estimations in Google Keyword Planner.
And in this video, I'll share exactly how accurate or inaccurate Keyword Planner's search volumes are.
Stay tuned.
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So recently, we did a study to compare Search Console with Keyword Planner data.
And the way we did it was quite simple.
We took 72,635 random keywords with a monthly search volume between 1,000 and 10,000.
Then we compared the number of impressions in Google Search Console in June 2021, with
the search volume data from Google Keyword Planner in the same month.
Now, in order to come up with fair conclusions, we made sure that the pages had been ranking
for these keywords in the top 10 for that same month.
And this would make our impression data a better representation of that month's
search volume.
So how accurate are Google Keyword Planner's search volumes?
Well, here's the short version of the results.
We found that 91% of the time, search volumes in Keyword Planner were overestimations to
Search Console impression data.
Now, this might sound bad, but if they're overestimating keywords by a small percentage
of monthly searches, then it's really not a big deal.
So we had to come up with a way to categorize what we considered to be "roughly accurate" estimations.
And to do that, we basically gave keywords an over-under score.
So if Keyword Planner's data was 50% more or less than the total impressions from Search
Console, then we classified them as being "roughly accurate."
But anything below or above these thresholds, were considered to be drastically under or
over estimated.
For example, if a keyword had a search volume of 8,000 monthly searches and Search Console
reported 4,000 impressions, then it's still technically within this threshold of "roughly accurate."
So in my opinion, we were pretty generous.
Alright, so let's go back to our data.
We found that Keyword Planner's search volumes were drastically overestimated around 54%
of the time.
And they were roughly accurate around 45% of the time.
As for drastic underestimations, it was pretty rare at just 0.5% of the time.
Now, to give you a more granular picture of these ratios, we put together this chart.
And the most surprising thing here is the fact that Keyword Planner overestimated search
volume by at least 4 times the impressions in Search Console.
And this was for 14.8% of the keywords.
Now, let's dig into why Keyword Planner consistently overestimates search volumes as well as these
wildly overestimated keywords at 4 times the reported search volume.
Alright, so the main reason for overestimations is that Keyword Planner groups keywords with
"similar" and sometimes not so similar meanings.
According to our Search Console data, the keyword "ahrefs" got 25,859 US impressions in June 2021.
But if we look at the data from Keyword Planner for the same month, it shows a search volume
of 33,100.
Reason being, they group similar keywords like these misspellings.
But these aren't a huge problem because it's likely that the searcher wants to get to
the same place, right?
And the keyword still falls into our "roughly accurate" bucket.
But that isn't always the case.
For example, if we search for "bank of america," "american banks," and "banks in america," you'll
see that Keyword Planner groups these keywords because they think they're all similar.
But the intent of the keywords are actually quite different.
Bank of America is a major bank in the United States.
So when people search this, they'll want to actually find that bank's website.
Whereas "american banks" and "banks in america" are dominantly informational.
People want to get a list of banks in the United States.
Not go to the Bank of America's website.
So if you were doing keyword research in Keyword Planner for let's say, a personal
finance website, you might search for "american banks" and think that it's a great topic because
it has over 16 million monthly searches in the US.
But the true search volume of that query is significantly less.
In fact, if you compare these three queries in Google Trends, you'll see that "american banks"
and "banks in america" get significantly fewer searches than "bank of america."
While Keyword Planner will show you the same search volumes when inputting individual keywords
because of this grouping mechanism.
So by grouping hidden keywords and summing the true search volumes of all quote unquote
"similar" keywords, you're bound to get overestimations.
And this is a big problem because it can set false expectations, leading you to create
content and spend resources on topics that may have nearly no search demand.
At Ahrefs, we're actually able to "ungroup" these keywords and show distinct search volumes.
In addition, we show the traffic potential for each keyword, which is usually a better
metric to use than search volume.
And that's because pages don't rank for just a single keyword and clickthrough rate will
vary from keyword to keyword.
Alright, the last thing I want to talk about are these crazy overestimations of over 400%.
Now, the types of queries that had these drastic overestimations often had local intent.
Meaning, most sites would only get impressions if the searcher was local to them.
But since Keyword Planner shows country-wide search volumes, the spread between GSC and
GKP could potentially be huge.
Let's look at the query "golf courses" as an example.
So these are 3 SERPs for this query from different locations.
One from Nashville, another from Los Angeles, and the third is from Chicago.
And as you can see, nearly all of the websites in the SERPs are different from one another
based on the location the SERP was pulled from.
In fact, there are only two websites that appear in all SERPs from different locations.
And the only sites that might see "true" search volume for the query are these mega authority sites.
Now, after running the data, naturally, we wanted to see how Ahrefs' keyword data
compared against Keyword Planner.
And here are the results.
Our keyword data drastically underestimated search volumes 1.84% of the time, compared
to keyword planner's 0.5%.
Ahrefs data was roughly accurate 60.68% of the time, compared to keyword planner's 45.22%.
And we drastically overestimated search volumes 37.48% of the time, compared to GKP's 54.28%.
Now, the reason why our data had more "roughly accurate" search volumes than keyword planner
is most likely because we're able to "ungroup" clusters of similar keywords and report distinct
search volumes for each of them.
So how accurate is Google Keyword Planner's search volume data?
Well, it's better than guessing but you'll ideally want to use a third party tool that
ungroups keywords like Ahrefs.
And seeing as pages can rank for hundreds or even thousands of keywords, search volume
shouldn't necessarily be what dictates your content schedule.
Again, traffic potential is a much better metric to look at since it shows you an estimation of
how much traffic you could get if you were to rank in the top position for a keyword.
And at the end of the day, we all want traffic, not searches.
Now, if you enjoyed this video, then make sure to like, share, and subscribe for more actionable
SEO and marketing tutorials.
I'll see you in the next one.