11 Years Of Yandex SERP Volatility [Data]

As SEOs / Webmasters we like to watch our rankings and traffic, and speculate when we see movement up and down, regardless if the search engine has come out and claimed it’s rolling out an update or a tweak.

It’s understandable why they don’t, as most modern search engines roll out updates daily based on ongoing algorithms and machine learning.

It’s also understandable why they don’t say exactly what has been changed, or how the different pieces of the puzzle fit together – as there will always be a corpus who look to game and exploit the system to benefit themselves, their clients, and not the end user.

That being said, there’s very rarely any smoke without fire and the more noticeable amends to the algorithms do get picked up and commented on by Webmasters and SERP volatility tools.

That means we can answer the question, how volatile has Yandex been over the past 11 years (2010-2020)?

The short answer, is more volatile in the past 4 years than previously – and very volatile in 2018.

The number of speculated major changes within Yandex, by month, by year.

So looking at this data in table format we can see that in 2018, tools and Webmasters commented on 152 separate days that there was significant volatility, versus relatively quieter years since.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2010 6 11 10 8 7 6 7 6 7 7 9 8
2011 8 5 8 12 11 5 13 7 12 9 9 9
2012 8 8 10 10 5 6 9 7 11 12 10 12
2013 7 9 10 10 4 7 8 9 4 8 6 9
2014 6 10 5 6 9 4 6 6 4 5 7 7
2015 5 6 10 5 4 5 6 4 5 4 3 10
2016 2 3 3 7 8 10 7 7 9 13 10 8
2017 11 12 11 10 10 8 6 12 10 9 8 10
2018 13 12 13 13 12 12 11 12 14 14 12 14
2019 14 10 13 11 9 10 5 11 13 11 10 11
2020 13 12 13 14 10 11 12 13 8 3 3 1

Over the past 3 years, there has been a significant spike in tracked SERP volatility once every 2.78 days – outside of the more regular ICS updates, and the more comprehensive confirmed algorithm updates.

In comparison to both 2018 and 2019, we can also see a much quieter Q4 in terms of reported volatility for 2020 – potentially a hangover from the much more volatile March to July period.

By knowing, and understanding how the algorithm behaves and updates, we can better forecast outcomes to the recommendations we make to our clients.