Using templates can provide up to 35% efficiency, and additional tools such as lemmatizers are required.
Grouping based on SERP similarity provides high accuracy and completeness of samples; as quickly as possible; provides automatic definition of business requests, and automatic linking of synonyms and reformulations. This is a complex multi-level algorithm, as only 87% of search query results are the same throughout the day. Using Brazil WhatsApp Number List this method, a large number of SERPs need to be parsed, and the quality of the sample directly depends on the quality of the search engine results. For example, due to Yandex's constant experiments, only 87% of the SERPs for the same query are the same in one day. Today, Yandex has at least 4 types of SERPs for the same query.
However, this way of collecting semantics is one of the most efficient:
An example of grouping based on SERP similarity might look like this:
For those who are still ready to seriously deal with semantic collections, Oleg Shestakov recommends: validating data; not overloading search engine servers; storing history of request frequency.
“In fact, after effectively implementing semantics on website pages, online store traffic increases, significantly. So, for a gadget store, it grew by 300% in 2 months; for a clothing store - 80 percent," the spokesman concluded.