Notice how the long-tail terms

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Google wants to start testing its machine-learning system with searches they have little to no data on – and 99 percent of pages have zero external links pointing to them. How is Google able to tell which pages should rank in these cases? By examining engagement and relevance. CTR is one of the best indicators of both. High-volume head terms, as far as we know, aren’t being exposed to RankBrain right now.

So by observing the differences between the organic search CTRs of Greece WhatsApp Number Data long-tail terms versus head terms, we should be able to spot the difference: google ctr versus organic ranking So here’s what we did: We looked at 1,000 keywords in the same keyword niche (to isolate external factors like Google shopping and other SERP features that can alter CTR characteristics). The keywords are all from my own I compared CTR versus rank for one- or two-word search terms, and did the same thing for long-tail keywords (search terms between 4 to 10 words).  get much higher average CTRs for a given position.



For example, in this data set, the head term in position 1 got an average CTR of 17.5 percent, whereas the long-tail term in position 1 had a remarkably high CTR, at an average of 33 percent. You’re probably thinking: “Well, that makes sense. You’d expect long-tail terms to have stronger query intent, thus higher CTRs.” That’s true, actually. But why is that long-tail keyword terms with high CTRs are so much more likely to be in top positions versus bottom-of-page organic positions? That’s a little weird, right? OK, let’s do an analysis of paid search queries in the same niche.