The History of LSI Copywriting
Posted: Sun Feb 02, 2025 9:14 am
The goal of the LSI ranking algorithm is to give the user the opportunity to get the most detailed answer to their question by following a link from the top of the search results (SERP). Therefore, the main factor is not just the presence of keys on the page in a certain quantity, but the answer to the question encrypted in the user's request.
Important! Some successful examples of LSI copywriting - articles that are well-written and well-optimized, but without exact matches of the keyword - reach the very top positions in the search results.
A specialist who has been ordered LSI ameriplan email leads copywriting (writing text optimized for the LSI algorithm) should refrain from using rare terminology known only to a small circle of professionals. And try to avoid long complex sentences. After all, they will not be analyzed by a living person, but by artificial intelligence, which is not yet very advanced.
Latent semantic analysis was first mentioned in search engines in connection with the Panda algorithm, developed by Google and launched in February 2011. The goal was to find and reduce the number of low-quality texts written solely for the purpose of increasing a site’s position in search engines. Already in 2012, the term “LSI copywriting” appeared.
The History of LSI Copywriting
Source: shutterstock.com
However, the requirements for the quality of articles were only fully defined by the beginning of 2013, when the next advanced ranking algorithm was introduced – “Hummingbird”, thanks to which the search engine began to recognize queries expressed in colloquial language and find materials not only mechanically, by the presence of keys, but also by semantic links.
A little later, in November 2016, Yandex joined in with its Palekh algorithm, which recognizes complex queries related to the “long tail” and low-frequency queries. The domestic search engine now has access to understanding queries in a conversational style (in total, up to 40% of the entire text volume is collected).
“Palekh,” which revolutionized domestic SEO copywriting, was based on machine learning and the use of neural networks. You can learn more about its methods and mechanics on the Habr portal in the Yandex blog.
Important! Some successful examples of LSI copywriting - articles that are well-written and well-optimized, but without exact matches of the keyword - reach the very top positions in the search results.
A specialist who has been ordered LSI ameriplan email leads copywriting (writing text optimized for the LSI algorithm) should refrain from using rare terminology known only to a small circle of professionals. And try to avoid long complex sentences. After all, they will not be analyzed by a living person, but by artificial intelligence, which is not yet very advanced.
Latent semantic analysis was first mentioned in search engines in connection with the Panda algorithm, developed by Google and launched in February 2011. The goal was to find and reduce the number of low-quality texts written solely for the purpose of increasing a site’s position in search engines. Already in 2012, the term “LSI copywriting” appeared.
The History of LSI Copywriting
Source: shutterstock.com
However, the requirements for the quality of articles were only fully defined by the beginning of 2013, when the next advanced ranking algorithm was introduced – “Hummingbird”, thanks to which the search engine began to recognize queries expressed in colloquial language and find materials not only mechanically, by the presence of keys, but also by semantic links.
A little later, in November 2016, Yandex joined in with its Palekh algorithm, which recognizes complex queries related to the “long tail” and low-frequency queries. The domestic search engine now has access to understanding queries in a conversational style (in total, up to 40% of the entire text volume is collected).
“Palekh,” which revolutionized domestic SEO copywriting, was based on machine learning and the use of neural networks. You can learn more about its methods and mechanics on the Habr portal in the Yandex blog.