Where can this be useful?

[jarinislamfatema]

First of all, from the obtained results, you can generate a list of masks, on the basis of which the initial core will be formed. This approach is useful when working in competitive niches with high traffic: the grouping of the compiled core can be carried out in such a way as to write more relevant texts and get priority in rates.

In addition, such mechanics allow us to find and clear third-party queries that do not fit our topic. For example, among queries concerning children's clothing, you can often come across porn queries.

Fixing word forms in Yandex Wordstat
Please note. In the screenshot, the selection of nested Taiwan cell phone number list queries occurs by word form, but when calculating translations, the algorithms ignore the "!" sign. Because of this, the key "pantyhose girl buy" actually has not 676 translations, but only 3.

By fixing the word form using “!”, you not only free yourself from unnecessary work, but also put a block on Yandex to select irrelevant synonyms.

Our experience shows that if you use a non-branded semantic core (there is no point in cleaning branded ones - there is quality traffic there) of 1000+ phrases, break it down into individual words and clean it from duplicates, including prepositions, - as a result you will get no more than fifty pieces. In addition, not everyone needs this kind of verification. The mechanism does not work in all niches, but it is still recommended to analyze the core in this way, especially since it does not require much time.