Source: Deep Learning on Medium
By Katie Pollitt, Senior Summit Creator — RE•WORK
February 20, 2019
1. Emerging of Search Engines
Search engine systems were developed according to the growth in the number of materials on the internet. The more documents were found by search engines, the more complicated algorithms were used. First, AI search engines were merely designed to perform page search, then they solved simple tasks and now they replied on for in-depth assistance by users.
Search engines have passed through the following development stages:
- Naive search pattern — there was a word search also called “inverted index”. Also, users should take into account words frequency and range pages
- Referential ranking — with the increase in the number of pages a necessity to range pages arose and page importance ranking was attached to ranging systems. Page importance ranking depended on quality and quantity of references to these pages.
- Machine learning — first, the system called “Matrixnet” was used for Yandex. In 2017, Yandex started using a new system of machine learning called Cat Boost. Cat Boost gives a more precise ranking.
- Artificial Intelligence (AI)
2. Artificial Intelligence
AI is based on the developments of machine learning. Developments in this direction were known since 2013 when the first research in the area of semantic analysis and abilities of Word2Vec system was conducted. Google created a self-learning system with AI — Rank Brain — based on this program. The system was launched in 2015. The goal of this algorithm was to catch the meaning of texts by searching ties between separate words.
Rank Brain is a part of the Hummingbird algorithm in Google. When this system finds unfamiliar words, it searches for hints and synonyms according to a query. Analogies that were found become the basis for data filtering. Presently, Rank Brain is one of three the most essential criteria for page assessment together with references and text.
In 2016, Yandex announced the launching of the new algorithm “Palekh” based on neural links. This algorithm allows for page searching that matches both queries by keywords and by meaning. “Palekh” analyses page headings and retrieves hidden semantic ties.
Another algorithm “Korolev” was introduced in 2017. In contrast to “Palekh”, “Korolev” compares semantic vectors of queries and whole pages. Earlier, headings were used for this purpose. In addition, except for neural links, machine learning based on human behaviour is employed. In this way, millions of users act as assessors. All algorithms have a similar 1-task routine that is designed to improve understanding of complex wordy queries.
3. How SEO Optimization Has Changed
AI penetration fundamentally changed query outputs and SEO rules. Using AI is associated with certain advantages:
- Output precision on infrequent and low-frequency queries had increased — search engines understand simple human language;
- Higher quality resources prevail in output — spam and over-optimization by keywords are filtered;
- SEO-texts are not required — only users’ needs should be taken into account. LSI-copywriting is used to optimize texts according to users’ queries.
- One can perform search engine deoptimization to disassociate links that are associated with a certain term.
Despite its multiple advantages associated with AI, there are certain disadvantages as well:
- Fuzzy search results — a robot can’t define precisely needed context if the meaning is polysemantic. Therefore, it offers several options
- Non-transparent ranking system — a user can’t specify search area by picking up word combinations since search engines retrieve what they consider appropriate
- Non-subject resources in output — often the websites that don’t relate to the search topic appear in search results or low-quality content can be found in the output
5. AI Can Be Used for Optimizing Content Strategies
Content marketing managers face challenges related to deciding what type of content use to attract customers and how to encourage customers from a stage of getting familiar with a brand to making purchases. Once can develop detailed customer profiles and address needs of target audiences. Sometimes AI can explain what customers need even if customers can’t articulate their real needs. By analysing social media profiles and tracking discussions on thematic blogs (forums) AI can understand customer needs. Many prominent brands use these AI tools to meet customer expectations. Brands can develop customer personas by learning SEO results of target audiences using SEO monitoring tool. Also, AI helps solve these problems as it makes identification of buyer personas possible through traffic analysis, social media behaviour and email interactions.
6. New Glance at Content
AI allows for creating hyper-personalised content with reference to target audiences’ profiles. This will be a new age of content marketing as it offers powerful tools to manage customer satisfaction more effectively. This was not possible in the past. Old-fashioned tricks don’t work anymore and marketers should take advantage of new algorithms for creating awesome content and marketing strategies.
7. Final Thoughts
Having in mind recent changes in SEO approaches, marketers will be able to develop more detailed marketing strategies using multiple content management tools and devices. AI enables marketers to focus on customer needs based on ranking factors. AI is a great helper for marketers from multiple perspectives as it offers tools to create content customers expect.