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66 Content Marketing Statistics for 2022

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66 Content Marketing Statistics for 2022

According to a recent study, only 10% of marketers don’t use content marketing (spoiler alert). So for the remaining majority of marketers, and perhaps the unconvinced minority, we’ve curated, vetted, and categorized a list of up-to-date statistics that will help you make the best of 2022.

In this article, we’ll highlight the following:

Top content marketing statistics

Here are the most interesting content marketing statistics we think you should know.

  1. 82% of marketers are actively investing in content marketing, 10% report not using content marketing, and 8% are unsure if their company uses content marketing (HubSpot).
  2. 43% of B2B marketers have a documented content marketing strategy, and 60% of the most successful B2B marketers have a documented content marketing strategy (Content Marketing Institute).
  3. 69% of marketers actively invest time in SEO (HubSpot).
  4. 60% of marketers report that content marketing generates demand/leads. In addition, 70% of marketers say that content marketing helps to educate the audience, and 60% say that it helps build loyalty with existing clients/customers (CMI).
  5. Video is the primary form of marketing media being created in 2021, followed by blogs (used by more than half of marketing teams), then infographics (HubSpot).
  6. 73% of people admit to skimming blog posts, while 27% consume them thoroughly (HubSpot).
  7. 71% of marketers say that targeting strategic keywords is their top SEO tactic  (HubSpot).
  8. 51% of the businesses that invest in content marketing publish content every day (The Manifest).
  9. Although 32% of respondents say they are overwhelmed by the amount of content available, a majority (44%) say they typically consume three to five pieces of content before engaging with a vendor (DemandGen).
  10. 69% of respondents prefer to learn about a product or service from a short video. 18% prefer to read a text-based article, website, or post. 4% prefer to view an infographic. 3% prefer to download an ebook or manual. 3% prefer to attend a webinar or pitch. 2% prefer to receive a sales call or demo (Wyzowl).
  11. 81% of marketers view content as a core business strategy (CMI).

Content marketing strategy statistics

As you can see in the previous section, most successful content marketers have a documented content strategy. Here are some statistics to inspire your strategy.

  1. The top three primary goals of creating content are boosting sales, building relationships with customers, and increasing brand awareness (eMarketer).
  2. Content marketing generates over 3x as many leads as outbound marketing and costs 62% less (Demand Metric).
  3. 72% of the most successful marketers in North America measure the ROI of their content marketing (eMarketer).

B2B content marketing statistics

Here’s what B2B marketers say about their content marketing efforts.

  1. Only 4% of B2B marketers don’t plan to develop a content marketing strategy (CMI).
  2. 87% of B2B marketers prioritize the audience’s informational needs over the organization’s sales/promotional messages (CMI).
  3. Content creation is the most outsourced content marketing activity among B2B marketers (CMI).
  4. The top three organic content distribution channels used by B2B marketers are social media, email, and their organization’s blog/website (CMI).
  5. LinkedIn is used by 96% of B2B content marketers (CMI).
  6. Only 28% of B2B marketers don’t use paid content distribution channels (CMI).
  7. 78% of B2B marketers employ keyword research for SEO while creating content (CMI).

B2C content marketing statistics

Let’s see how answers given by B2C content marketers compare to their B2B counterparts.

  1. Only 6% of B2C marketers don’t plan to develop a content marketing strategy (CMI).
  2. Content creation is the most outsourced content marketing activity among B2C marketers (CMI).
  3. The top three organic content distribution channels used by B2C marketers are social media, email, and their organization’s blog/website (CMI).
  4. The top three most often used content formats by B2C marketers are blog posts/short articles, email newsletters, and videos (CMI).
  5. B2C marketers who use at least two organic social media platforms report that Facebook (59%) and Instagram (21%) produce the best overall content marketing results (CMI).
  6. Only 28% of B2C marketers don’t use paid content distribution channels (CMI).
  7. 73% of B2C marketers employ keyword research for SEO while creating content (CMI).

Organic search statistics

As our collected statistics have already proven, most content marketers do keyword research and invest in SEO. In other words, they invest in organic search. Let’s see what else we can find about this marketing channel.

  1. 90.63% of pages get no organic search traffic from Google (Ahrefs).
    90.63% of pages get no organic search traffic from Google
  2. 68% of online experiences begin with a search engine (Brightedge).
  3. 71% of B2B researchers start their research with generic search instead of branded search (Google).
  4. 53% of shoppers say they always do research before a purchase to ensure they are making the best possible choice (Google).
  5. Only 5.7% of pages will rank in the top 10 search results within a year of publication (Ahrefs).
  6. Almost two-thirds of global online search comes from mobile devices (Perficient).
  7. 69% of marketers actively invest in SEO (HubSpot).
  8. The top three most often used metrics for SEO are keyword ranking, organic traffic, and time spent on page (HubSpot).
  9. Generally speaking, the more backlinks a page has, the more organic traffic it gets from Google (Ahrefs).
  10. The average #1 ranking page will also rank in the top 10 for nearly 1,000 other relevant keywords (Ahrefs).
    Most top-ranking pages also rank for hundreds of other keywords
  11. There’s no correlation between Flesch Reading Ease scores and ranking positions (Ahrefs).
    There's no correlation between flesch reading ease scores and ranking positions

Handpicked statistics for this cornerstone of many content strategies.

  1. People rarely read online. They’re far more likely to scan than read word for word. They simply want to pick out the information that is most pertinent to their current needs (Nielsen).
  2. 70% of people rather get information from blogs than traditional advertisements (Demand Metric).
  3. To date, there are more than 600 million blogs out of 1.9 billion websites in the world. Their authors account for over 6 million blog posts daily, or over 2.5 billion annually (hosting tribunal).
  4. How-to articles are the most popular content formats (77%), followed by news and trends (49%), and guides and ebooks (47%) (Statista).
  5. Only one-third of bloggers regularly check their blogs’ traffic analytics (Statista).
  6. Engagement starts to drop for posts with a reading time longer than seven minutes (Medium).
    Peoples' attention drops when a post is too long

As reported by Hubspot, video is the primary form of marketing media being created in 2021. It seems that video marketing is no longer an option.

  1. 70% of viewers bought from a brand after seeing it on YouTube (Google).
  2. 79% of people say they’ve been convinced to buy or download a piece of software or app by watching a video (Wyzowl).
  3. 50.9% of B2B decision-makers use YouTube to research purchases (Hootsuite).
  4. YouTube is the second-most visited website in the U.S. by organic traffic (Ahrefs).
  5. YouTube reaches more 18- to 49-year-olds in an average week than all cable TV networks combined (Google).
  6. Video viewers say relating to their passions is 3X more important than content featuring famous actors (Google).
  7. Video viewers say relating to their passions is 1.6X more important than content with high production quality (Google).
  8. 86% of businesses use video as a marketing tool (Wyzowl).
  9. 91% of marketers feel the pandemic has made video more important for brands (Wyzowl).
  10. 96% of people have watched an explainer video to learn more about a product or service (Wyzowl).
  11. 85% of people want to see more videos from brands in 2021 (Wyzowl).

Brands like Slack, Shopify, and Basecamp have already invested in creating a branded podcast. These statistics show that marketers should at least consider advertising on podcasts.

  1. Podcast ad spending in the U.S. is expected to reach $1.74B in 2022, a 23% increase from 2021 (Statista).
  2. 57% of Americans listen to podcasts (Edison Research).
  3. 80% of podcast listeners listen to all or most of each episode (Podcast Insights).
  4. Weekly podcast listeners tune in to an average of eight podcasts per week (Edison Research).
  5. Each week in America, there are more podcast listeners than Netflix account holders (Edison Research).

Other interesting statistics

Is visual content important? Is there something like the best day to publish? How do people decide what link to click? Find out below.

  1. People following directions with text and illustrations do 323% better than people following directions without illustrations (W.H Levie, H.Lentz).
  2. The majority of marketers surveyed say that visual content is a key component of their marketing strategy. 64% say that visuals are either essential or very important. Only 9.6% don’t require visuals for their content marketing (Venngage).
  3. There’s no “best day” to publish a new piece of content. Social shares are distributed evenly among posts published on different days of the week (Backlinko).
  4. The average time a reader allocates a newsletter after opening it is only 51 seconds. Participants of the study fully read only 19% of newsletters (Nielsen).
  5. When deciding which links to click on the web, users choose those with the highest information scent, which is a mix of cues they get from the link label, the context in which the link is shown, and their prior experiences (Nielsen).

Final thoughts

I hope you’ve found valuable insight in the above collection of statistics. After all, it’s always a good idea to look for data-driven answers to your marketing hypotheses.

But let’s remember that it’s wise to take any statistic with a grain of salt. Statistics are generalizations of only a portion of the reality around us. Also, they aren’t methodologically sound all of the time: small sample sizes, p‑hacking, and spurious correlations are quite common.

So if you’ve just read a piece of data that contradicts your professional marketing experience, don’t throw away your content strategy just yet. Instead, dig deeper.

Finally, if you want to learn more about content marketing, we’ve got a few guides ready for you:

Got questions? Ping me on Twitter.




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Google Updates Structured Data Guidance To Clarify Supported Formats

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Google Updates Structured Data Guidance To Clarify Supported Formats

Google updated the structured data guidance to better emphasize that all three structured data formats are acceptable to Google and also explain why JSON-LD is is recommended.

The updated Search Central page that was updated is the Supported Formats section of the Introduction to structured data markup in Google Search webpage.

The most important changes were to add a new section title (Supported Formats), and to expand that section with an explanation of supported structured data formats.

Three Structured Data Formats

Google supports three structured data formats.

  1. JSON-LD
  2. Microdata
  3. RDFa

But only one of the above formats, JSON-LD, is recommended.

According to the documentation, the other two formats (Microdata and RDFa) are still fine to use. The update to the documentation explains why JSON-LD is recommended.

Google also made a minor change to a title of a preceding section to reflect that the section addresses structured data vocabulary

The original section title, Structured data format, is now Structured data vocabulary and format.

Google added a section title the section that offers guidance on Google’s preferred structured data format.

This is also the section with the most additional text added to it.

New Supported Formats Section Title

The updated content explains why Google prefers the JSON-LD structured data format, while confirming that the other two formats are acceptable.

Previously this section contained just two sentences:

“Google Search supports structured data in the following formats, unless documented otherwise:

Google recommends using JSON-LD for structured data whenever possible.”

The updated section now has the following content:

“Google Search supports structured data in the following formats, unless documented otherwise.

In general, we recommend using a format that’s easiest for you to implement and maintain (in most cases, that’s JSON-LD); all 3 formats are equally fine for Google, as long as the markup is valid and properly implemented per the feature’s documentation.

In general, Google recommends using JSON-LD for structured data if your site’s setup allows it, as it’s the easiest solution for website owners to implement and maintain at scale (in other words, less prone to user errors).”

Structured Data Formats

JSON-LD is arguably the easiest structured data format to implement, the easiest to scale, and the most straightforward to edit.

Most, if not all, WordPress SEO and structured data plugins output JSON-LD structured data.

Nevertheless, it’s a useful update to Google’s structured data guidance in order to make it clear that all three formats are still supported.

Google’s documentation on the change can be read here.

Featured image by Shutterstock/Olena Zaskochenko



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Ranking Factors & The Myths We Found

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Ranking Factors & The Myths We Found

Yandex is the search engine with the majority of market share in Russia and the fourth-largest search engine in the world.

On January 27, 2023, it suffered what is arguably one of the largest data leaks that a modern tech company has endured in many years – but is the second leak in less than a decade.

In 2015, a former Yandex employee attempted to sell Yandex’s search engine code on the black market for around $30,000.

The initial leak in January this year revealed 1,922 ranking factors, of which more than 64% were listed as unused or deprecated (superseded and best avoided).

This leak was just the file labeled kernel, but as the SEO community and I delved deeper, more files were found that combined contain approximately 17,800 ranking factors.

When it comes to practicing SEO for Yandex, the guide I wrote two years ago, for the most part, still applies.

Yandex, like Google, has always been public with its algorithm updates and changes, and in recent years, how it has adopted machine learning.

Notable updates from the past two-three years include:

  • Vega (which doubled the size of the index).
  • Mimicry (penalizing fake websites impersonating brands).
  • Y1 update (introducing YATI).
  • Y2 update (late 2022).
  • Adoption of IndexNow.
  • A fresh rollout and assumed update of the PF filter.

On a personal note, this data leak is like a second Christmas.

Since January 2020, I’ve run an SEO news website as a hobby dedicated to covering Yandex SEO and search news in Russia with 600+ articles, so this is probably the peak event of the hobby site.

I’ve also spoken twice at the Optimization conference – the largest SEO conference in Russia.

This is also a good test to see how closely Yandex’s public statements match the codebase secrets.

In 2019, working with Yandex’s PR team, I was able to interview engineers in their Search team and ask a number of questions sourced from the wider Western SEO community.

You can read the interview with the Yandex Search team here.

Whilst Yandex is primarily known for its presence in Russia, the search engine also has a presence in Turkey, Kazakhstan, and Georgia.

The data leak was believed to be politically motivated and the actions of a rogue employee, and contains a number of code fragments from Yandex’s monolithic repository, Arcadia.

Within the 44GB of leaked data, there’s information relating to a number of Yandex products including Search, Maps, Mail, Metrika, Disc, and Cloud.

What Yandex Has Had To Say

As I write this post (January 31st, 2023), Yandex has publicly stated that:

the contents of the archive (leaked code base) correspond to the outdated version of the repository – it differs from the current version used by our services

And:

It is important to note that the published code fragments also contain test algorithms that were used only within Yandex to verify the correct operation of the services.

So, how much of this code base is actively used is questionable.

Yandex has also revealed that during its investigation and audit, it found a number of errors that violate its own internal principles, so it is likely that portions of this leaked code (that are in current use) may be changing in the near future.

Factor Classification

Yandex classifies its ranking factors into three categories.

This has been outlined in Yandex’s public documentation for some time, but I feel is worth including here, as it better helps us understand the ranking factor leak.

  • Static factors – Factors that are related directly to the website (e.g. inbound backlinks, inbound internal links, headers, and ads ratio).
  • Dynamic factors – Factors that are related to both the website and the search query (e.g. text relevance, keyword inclusions, TF*IDF).
  • User search-related factors – Factors relating to the user query (e.g. where is the user located, query language, and intent modifiers).

The ranking factors in the document are tagged to match the corresponding category, with TG_STATIC and TG_DYNAMIC, and then TG_QUERY_ONLY, TG_QUERY, TG_USER_SEARCH, and TG_USER_SEARCH_ONLY.

Yandex Leak Learnings So Far

From the data thus far, below are some of the affirmations and learnings we’ve been able to make.

There is so much data in this leak, it is very likely that we will be finding new things and making new connections in the next few weeks.

These include:

  • PageRank (a form of).
  • At some point Yandex utilized TF*IDF.
  • Yandex still uses meta keywords, which are also highlighted in its documentation.
  • Yandex has specific factors for medical, legal, and financial topics (YMYL).
  • It also uses a form of page quality scoring, but this is known (ICS score).
  • Links from high-authority websites have an impact on rankings.
  • There’s nothing new to suggest Yandex can crawl JavaScript yet outside of already publicly documented processes.
  • Server errors and excessive 4xx errors can impact ranking.
  • The time of day is taken into consideration as a ranking factor.

Below, I’ve expanded on some other affirmations and learnings from the leak.

Where possible, I’ve also tied these leaked ranking factors to the algorithm updates and announcements that relate to them, or where we were told about them being impactful.

MatrixNet

MatrixNet is mentioned in a few of the ranking factors and was announced in 2009, and then superseded in 2017 by Catboost, which was rolled out across the Yandex product sphere.

This further adds validity to comments directly from Yandex, and one of the factor authors DenPlusPlus (Den Raskovalov), that this is, in fact, an outdated code repository.

MatrixNet was originally introduced as a new, core algorithm that took into consideration thousands of ranking factors and assigned weights based on the user location, the actual search query, and perceived search intent.

It is typically seen as an early version of Google’s RankBrain, when they are indeed two very different systems. MatrixNet was launched six years before RankBrain was announced.

MatrixNet has also been built upon, which isn’t surprising, given it is now 14 years old.

In 2016, Yandex introduced the Palekh algorithm that used deep neural networks to better match documents (webpages) and queries, even if they didn’t contain the right “levels” of common keywords, but satisfied the user intents.

Palekh was capable of processing 150 pages at a time, and in 2017 was updated with the Korolyov update, which took into account more depth of page content, and could work off 200,000 pages at once.

URL & Page-Level Factors

From the leak, we have learned that Yandex takes into consideration URL construction, specifically:

  • The presence of numbers in the URL.
  • The number of trailing slashes in the URL (and if they are excessive).
  • The number of capital letters in the URL is a factor.
Screenshot from author, January 2023

The age of a page (document age) and the last updated date are also important, and this makes sense.

As well as document age and last update, a number of factors in the data relate to freshness – particularly for news-related queries.

Yandex formerly used timestamps, specifically not for ranking purposes but “reordering” purposes, but this is now classified as unused.

Also in the deprecated column are the use of keywords in the URL. Yandex has previously measured that three keywords from the search query in the URL would be an “optimal” result.

Internal Links & Crawl Depth

Whilst Google has gone on the record to say that for its purposes, crawl depth isn’t explicitly a ranking factor, Yandex appears to have an active piece of code that dictates that URLs that are reachable from the homepage have a “higher” level of importance.

Yandex factorsScreenshot from author, January 2023

This mirrors John Mueller’s 2018 statement that Google gives “a little more weight” to pages found more than one click from the homepage.

The ranking factors also highlight a specific token weighting for webpages that are “orphans” within the website linking structure.

Clicks & CTR

In 2011, Yandex released a blog post talking about how the search engine uses clicks as part of its rankings and also addresses the desires of the SEO pros to manipulate the metric for ranking gain.

Specific click factors in the leak look at things like:

  • The ratio of the number of clicks on the URL, relative to all clicks on the search.
  • The same as above, but broken down by region.
  • How often do users click on the URL for the search?

Manipulating Clicks

Manipulating user behavior, specifically “click-jacking”, is a known tactic within Yandex.

Yandex has a filter, known as the PF filter, that actively seeks out and penalizes websites that engage in this activity using scripts that monitor IP similarities and then the “user actions” of those clicks – and the impact can be significant.

The below screenshot shows the impact on organic sessions (сессии) after being penalized for imitating user clicks.

Image Source: Russian Search NewsImage from Russian Search News, January 2023

User Behavior

The user behavior takeaways from the leak are some of the more interesting findings.

User behavior manipulation is a common SEO violation that Yandex has been combating for years. At the 2020 Optimization conference, then Head of Yandex Webmaster Tools Mikhail Slevinsky said the company is making good progress in detecting and penalizing this type of behavior.

Yandex penalizes user behavior manipulation with the same PF filter used to combat CTR manipulation.

Dwell Time

102 of the ranking factors contain the tag TG_USERFEAT_SEARCH_DWELL_TIME, and reference the device, user duration, and average page dwell time.

All but 39 of these factors are deprecated.

Yandex factorsScreenshot from author, January 2023

Bing first used the term Dwell time in a 2011 blog, and in recent years Google has made it clear that it doesn’t use dwell time (or similar user interaction signals) as ranking factors.

YMYL

YMYL (Your Money, Your Life) is a concept well-known within Google and is not a new concept to Yandex.

Within the data leak, there are specific ranking factors for medical, legal, and financial content that exist – but this was notably revealed in 2019 at the Yandex Webmaster conference when it announced the Proxima Search Quality Metric.

Metrika Data Usage

Six of the ranking factors relate to the usage of Metrika data for the purposes of ranking. However, one of them is tagged as deprecated:

  • The number of similar visitors from the YandexBar (YaBar/Ябар).
  • The average time spent on URLs from those same similar visitors.
  • The “core audience” of pages on which there is a Metrika counter [deprecated].
  • The average time a user spends on a host when accessed externally (from another non-search site) from a specific URL.
  • Average ‘depth’ (number of hits within the host) of a user’s stay on the host when accessed externally (from another non-search site) from a particular URL.
  • Whether or not the domain has Metrika installed.

In Metrika, user data is handled differently.

Unlike Google Analytics, there are a number of reports focused on user “loyalty” combining site engagement metrics with return frequency, duration between visits, and source of the visit.

For example, I can see a report in one click to see a breakdown of individual site visitors:

MetrikaScreenshot from Metrika, January 2023

Metrika also comes “out of the box” with heatmap tools and user session recording, and in recent years the Metrika team has made good progress in being able to identify and filter bot traffic.

With Google Analytics, there is an argument that Google doesn’t use UA/GA4 data for ranking purposes because of how easy it is to modify or break the tracking code – but with Metrika counters, they are a lot more linear, and a lot of the reports are unchangeable in terms of how the data is collected.

Impact Of Traffic On Rankings

Following on from looking at Metrika data as a ranking factor; These factors effectively confirm that direct traffic and paid traffic (buying ads via Yandex Direct) can impact organic search performance:

  • Share of direct visits among all incoming traffic.
  • Green traffic share (aka direct visits) – Desktop.
  • Green traffic share (aka direct visits) – Mobile.
  • Search traffic – transitions from search engines to the site.
  • Share of visits to the site not by links (set by hand or from bookmarks).
  • The number of unique visitors.
  • Share of traffic from search engines.

News Factors

There are a number of factors relating to “News”, including two that mention Yandex.News directly.

Yandex.News was an equivalent of Google News, but was sold to the Russian social network VKontakte in August 2022, along with another Yandex product “Zen”.

So, it’s not clear if these factors related to a product no longer owned or operated by Yandex, or to how news websites are ranked in “regular” search.

Backlink Importance

Yandex has similar algorithms to combat link manipulation as Google – and has since the Nepot filter in 2005.

From reviewing the backlink ranking factors and some of the specifics in the descriptions, we can assume that the best practices for building links for Yandex SEO would be to:

  • Build links with a more natural frequency and varying amounts.
  • Build links with branded anchor texts as well as use commercial keywords.
  • If buying links, avoid buying links from websites that have mixed topics.

Below is a list of link-related factors that can be considered affirmations of best practices:

  • The age of the backlink is a factor.
  • Link relevance based on topics.
  • Backlinks built from homepages carry more weight than internal pages.
  • Links from the top 100 websites by PageRank (PR) can impact rankings.
  • Link relevance based on the quality of each link.
  • Link relevance, taking into account the quality of each link, and the topic of each link.
  • Link relevance, taking into account the non-commercial nature of each link.
  • Percentage of inbound links with query words.
  • Percentage of query words in links (up to a synonym).
  • The links contain all the words of the query (up to a synonym).
  • Dispersion of the number of query words in links.

However, there are some link-related factors that are additional considerations when planning, monitoring, and analyzing backlinks:

  • The ratio of “good” versus “bad” backlinks to a website.
  • The frequency of links to the site.
  • The number of incoming SEO trash links between hosts.

The data leak also revealed that the link spam calculator has around 80 active factors that are taken into consideration, with a number of deprecated factors.

This creates the question as to how well Yandex is able to recognize negative SEO attacks, given it looks at the ratio of good versus bad links, and how it determines what a bad link is.

A negative SEO attack is also likely to be a short burst (high frequency) link event in which a site will unwittingly gain a high number of poor quality, non-topical, and potentially over-optimized links.

Yandex uses machine learning models to identify Private Blog Networks (PBNs) and paid links, and it makes the same assumption between link velocity and the time period they are acquired.

Typically, paid-for links are generated over a longer period of time, and these patterns (including link origin site analysis) are what the Minusinsk update (2015) was introduced to combat.

Yandex Penalties

There are two ranking factors, both deprecated, named SpamKarma and Pessimization.

Pessimization refers to reducing PageRank to zero and aligns with the expectations of severe Yandex penalties.

SpamKarma also aligns with assumptions made around Yandex penalizing hosts and individuals, as well as individual domains.

Onpage Advertising

There are a number of factors relating to advertising on the page, some of them deprecated (like the screenshot example below).

Yandex factorsScreenshot from author, January 2023

It’s not known from the description exactly what the thought process with this factor was, but it could be assumed that a high ratio of adverts to visible screen was a negative factor – much like how Google takes umbrage if adverts obfuscate the page’s main content, or are obtrusive.

Tying this back to known Yandex mechanisms, the Proxima update also took into consideration the ratio of useful and advertising content on a page.

Can We Apply Any Yandex Learnings To Google?

Yandex and Google are disparate search engines, with a number of differences, despite the tens of engineers who have worked for both companies.

Because of this fight for talent, we can infer that some of these master builders and engineers will have built things in a similar fashion (though not direct copies), and applied learnings from previous iterations of their builds with their new employers.

What Russian SEO Pros Are Saying About The Leak

Much like the Western world, SEO professionals in Russia have been having their say on the leak across the various Runet forums.

The reaction in these forums has been different to SEO Twitter and Mastodon, with a focus more on Yandex’s filters, and other Yandex products that are optimized as part of wider Yandex optimization campaigns.

It is also worth noting that a number of conclusions and findings from the data match what the Western SEO world is also finding.

Common themes in the Russian search forums:

  • Webmasters asking for insights into recent filters, such as Mimicry and the updated PF filter.
  • The age and relevance of some of the factors, due to author names no longer being at Yandex, and mentions of long-retired Yandex products.
  • The main interesting learnings are around the use of Metrika data, and information relating to the Crawler & Indexer.
  • A number of factors outline the usage of DSSM, which in theory was superseded by the release of Palekh in 2016. This was a search algorithm utilizing machine learning, announced by Yandex in 2016.
  • A debate around ICS scoring in Yandex, and whether or not Yandex may provide more traffic to a site and influence its own factors by doing so.

The leaked factors, particularly around how Yandex evaluates site quality, have also come under scrutiny.

There is a long-standing sentiment in the Russian SEO community that Yandex oftentimes favors its own products and services in search results ahead of other websites, and webmasters are asking questions like:

Why does it bother going to all this trouble, when it just nails its services to the top of the page anyway?

In loosely translated documents, these are referred to as the Sorcerers or Yandex Sorcerers. In Google, we’d call these search engine results pages (SERPs) features – like Google Hotels, etc.

In October 2022, Kassir (a Russian ticket portal) claimed ₽328m compensation from Yandex due to lost revenue, caused by the “discriminatory conditions” in which Yandex Sorcerers took the customer base away from the private company.

This is off the back of a 2020 class action in which multiple companies raised a case with the Federal Antimonopoly Service (FAS) for anticompetitive promotion of its own services.

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Google Updates Search Console Video Indexing Report

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Google Updates Search Console Video Indexing Report

Google’s updated Search Console Video indexing report now includes daily video impressions and a sitemap filter feature.

  • Google has updated the Search Console Video indexing report to provide more comprehensive insights into video performance in search results.
  • The updated report includes daily video impressions, which are grouped by page, and a new sitemap filter feature to focus on the most important video pages.
  • These updates are part of Google’s ongoing efforts to help website owners and content creators understand and improve the visibility of their videos in search results.



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