Google Search Algorithm Updates
Every year, Google makes numerous algorithm updates. These are smaller and larger updates, some of which have far-reaching effects on the SERPs (Search Engine Result Pages). In our overview, we briefly explain the update such as, Panda, Penguin, Fred, BERT, and RankBrain. These are all considered as major Google updates and have an influence on your website’s SEO performance and results.
Google tries to improve the quality of the search results through regular updates of its search algorithm and regularly mixes up the SERPs. Numerous new updates are carried out every year in order to ban spam websites that use means such as keyword stuffing or cloaking from the search results.
There are over 200 Google ranking factors that influence search results. With a Google update, the weighting of these ranking factors is often changed or new factors are even introduced by Google to improve or enhance their search results.
Let’s understand one of each major Google search algorithm update in brief.
In 2011, Google introduced the Panda Updates, which should improve the quality of the search results and thus provide the most appropriate answers to the user’s search query. Did you just put senseless keywords in the text or did users find valuable information here? In this context, original or duplicate content is particularly important.
Unique content is one of the most important ranking factors. The search queries, which should be considered in the context of keyword research, can provide an indication of what the users are actually interested in.
So far, four major Panda updates have been rolled out, most recently in 2014. This in particular had a major impact on the SERPs. Panda was later incorporated into the Google Core algorithm and is therefore no longer carried out as a stand-alone update.
In 2012 came the first Penguin Update, which was rolled out five times within two years and had various rounds. It has now become part of the general search algorithm just like the Panda update.
Penguin prevents websites that were not built for the user but for the search engine from being listed high in the SERPs (search result pages). The main focus here is on adapting the search algorithm with regard to the unnatural increase in backlinks. If the backlink profile of a website shows that a particularly large number of money keywords are linked, this also has a negative effect on the ranking.
With regard to ranking improvement and links, it is also important not to build up inferior links from directories, comment areas or footers. These in turn would have a negative impact on the ranking in the SERPs. In order to avoid a penalty, it is worthwhile to analyse the link profile on a regular basis.
In March 2017, Google’s Fred Update became known, which initially thought it was a joke. In doing so, websites with little or less valuable content and a lot of AdSense advertising were punished. Barry Schwartz analyses the 100-website affected by Fred update, as per his analysis, the majority of the sites was having similar characteristics like content driven and placement of advertising aggressively.
Probably the most important update for a long time was the BERT update in December 2019. In the USA it was rolled out in October and 70 more languages followed in December. The aim of BERT is to better understand semantic relationships.
With the help of BERT, Google should now be able to answer long-tail search queries and search queries with prepositions more precisely and put them into context. This is an important point because this is how Google is getting ready for the increasing number of voice search searches.
The so-called RankBrain is also closely related to the search algorithm. This is an artificial intelligence (AI) by means of which the search engine continuously learns to assess the search queries with the underlying search intent and to deliver the appropriate search results.
The important thing about RankBrain is that many of these special search queries have never been made before and can still be evaluated. This is done by logically combining or comparing with other, similar search queries.