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Tanmoy Mukharjee
Jul 16, 2022
In Wellness Forum
Algorithms with own variables Each tool has its buy email database own developed algorithm that uses different variables to determine semantics and intent. For example, variables help to determine whether there is a semantic relationship buy email database and, if so, how strong, what intentions a keyword has and in which cluster shell or layer a keyword ends up. Some examples of variables could be: Semantics – Comparing the number of matching URLs in the top X of the SERP Semantics – The number of matching root domains in the top X of buy email database the SERP Intent – ​​The presence or absence of Google Ads Intent – ​​signaling certain words that release intent Intent – ​​identifying SERP features Shell. Merging optimal small clusters buy email database together into groups that have the most semantic overlap The disadvantage of clustering based on language Clustering on the basis of language remains a disadvantage. You cannot easily switch between languages ​​or countries. You also run into contradictions. For example: “buying a buy email database house” doesn't just mean that you want to buy a house right away. You also want information about the process of buying a house. Google SERP for the search "buy a house". When do you opt for automatic semantic clustering? The automatic semantic clustering of keywords stands or falls buy email database with the quality of the algorithm, the flexibility of the tool and the time savings you achieve with it. The flexibility to cluster a set of buy email database keywords per region or language, for example. This means that the clustering of your keywords is semantically grouped based on the local search results and the preset language. Or the buy email database convenience of clustering an entire set of keywords in 10 – 20 minutes. But automatic solutions for semantic clustering are especially useful if the clustering is of better quality than manual buy email database clustering. Time gain therefore partly depends on the clustering quality.
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Tanmoy Mukharjee

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