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Most “Types” of Content Marketing Are Nonsense. Here Are 5 That Actually Make Sense

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Most "Types" of Content Marketing Are Nonsense. Here Are 5 That Actually Make Sense

Here are some popular “types” of content marketing:

  • Blogs
  • Videos
  • Podcasts
  • Emails
  • Infographics
  • Webinars
  • Case studies
  • Presentations
  • eBooks
  • Whitepapers
  • Testimonials
  • Slide decks
  • Quizzes
  • Tools

You’ll find these on pretty much any list of content marketing types that pops up on Google. But the truth is that none of these are types of content marketing. They’re types of content… which isn’t the same thing!

In this guide, I’ll explain why this is and share some real types of content marketing that you can use.

But let’s start by getting our definitions straight.

What is content marketing?

Content marketing is the process of creating and distributing content to attract and retain customers.

You can use any type of content for this. But it’s not content marketing unless the content actually promotes your business and is paired with a distribution strategy. 

Types of content marketing

Let’s go through a few types of content marketing to see how they work in real life.

Blogging for SEO

Blog posts are a popular content type. Many businesses publish them regularly. However, most posts don’t get any traffic. Even if promoted via social media, they usually get a spike in the beginning but fade to nothing later on.

Line graph showing article traffic sharply spikes then flattens to nothing as time passes

The reason for this is that most businesses lack a distribution strategy. They just publish and pray. To avoid this same mistake, you need a way to attract consistent traffic to your blog posts. And given that 57.8% of all web traffic comes from Google, we argue that there’s no better way than writing for SEO.

How do you do it?

The simplest way is to look for topics people are searching for and where there are opportunities to write blog posts that promote your business. For example, if we enter a few topics related to our business into Ahrefs’ Keywords Explorer and check the Matching terms report, we see thousands of keyword ideas.

List of keyword ideas and metrics like KD, etc

If we look at the results for one of these keywords, you’ll see our list of free keyword research tools ranking in position #3. Because it ranks well for a few thousand keywords, it gets an estimated 6,700 organic visits per month.

SERP overview of "keyword research tools"

If you look at the blog post itself, you’ll see one of the recommended tools is our free keyword generator. In other words, our blog post promotes our business to every single one of its thousands of monthly readers.

Excerpt of blog post promoting Ahrefs' keyword generator tool

Video marketing for YouTube

With over 2 billion monthly logged-in users, YouTube has an existing audience you can tap into when you create videos. However, there is a lot of competition, and your videos still have to be promoted and discovered.

How do you do this?

Like blogging for SEO, the simplest way is to find topics people are searching for on YouTube that also allow you to promote your product naturally. To find these topics, enter a few keywords relevant to your niche into Ahrefs’ Keywords Explorer, set it to “YouTube,” and check the Matching terms report.

You’ll see hundreds of potential keyword ideas.

Keyword ideas for "keyword research" and "link building"

That’s how we built up our YouTube channel. Our main focus initially was to rank well for keywords relevant to our business. For example, if we look at the topic “keyword research,” you can see that we’re ranking #1 for it, which drives thousands of views per month.

Youtube serp

Our video also plugs Keywords Explorer as the tool of choice for doing keyword research, promoting our business to thousands of YouTube viewers each month.

Podcasting for brand awareness

Like blogging, podcasting is a great content type to build up an audience and promote your brand. But most podcasts also don’t get traction and end up getting abandoned a few months later.

And unlike YouTube, the current podcast discovery landscape isn’t ideal and can make building momentum difficult.

But there is an alternative route, which is one we did ourselves: appear on podcasts as a guest. It makes things simple. You don’t have to sink resources into creating one that may not work out, and you can also tap into an existing audience.

With over 2 million podcasts today, there are plenty of opportunities for you to get interviewed.

But how do you find these opportunities?

An easy way is to simply Google for the top podcasts in your niche.

Google results showing top business podcasts

However, most of them will be popular and difficult for you to get on. A better way is to identify someone of similar caliber in your industry who’s been a guest on multiple podcasts. For example, Laura Roeder, the founder of MeetEdgar, is someone in our industry who has appeared on many podcasts.

To discover the podcasts she’s been on, let’s paste her site into Ahrefs’ Site Explorer, go to the Backlinks report, set the search dropdown to “Referring page title,” and search for her name. Now, the links in the “Referring page” column are pretty much all podcasts.

Backlinks report results for Laura Roeder

Once you’ve identified the podcasts you want to be on, pitch yourself as a guest.

Email marketing to nurture leads

Brian Dean gets over 500,000 monthly organic visits:

Overview 2.0 stats of Brian's website

This audience is exposed to his blog, where he actively captures contact information:

Blog post showing "unlock now" option for visitors who want to read more

Then, whenever Brian publishes a new post, he promotes it to his list:

Brian's email promoting his blog post

This works like a flywheel: email distributes blog content to an existing email list, while blogging builds up the list through exposure to new visitors and capturing their contact information.

This tiki-taka between email and blogging also helps nurture his audience. It helps inch his audience in the initial stages of the buyer’s journey closer to the “purchase” stage, and his brand is always top of mind too.

Once in a while, when Brian launches his online courses, purchasing becomes a no-brainer because his audience is already familiar with his work and name:

Brian's email promoting his Grow Your Blog Fast course

Social media marketing for brand building

Don’t expect to succeed on social media with just any content. To effectively promote your brand on social media, you have to understand what works on a particular platform and publish content natively.

Brands like Innocent and Wendy’s do well on Twitter because they understand social media’s affinity for playful banter, strong opinions, and satire.

GoPro knows that Instagram is all about beautiful pictures and influencer marketing. It successfully combines the two (and subtly promotes itself) by curating gorgeous pictures and videos taken by customers who used GoPro products.

GoPro's Instagram posts

It’s not about spamming your feed with links to your own products. Rather, it’s about actively engaging with people on social media.

How to choose the right type of content marketing for you

You’ve seen how the different types of content marketing actually work. But which ones should you choose? Which ones will work for you?

Your choices will depend on your answers to these four questions.

Four circles intersect, overlapping in the middle. Each circle features 1 of the 4 questions.

What’s right for your goal?

Content marketing is ultimately just a tool you use to reach your goals. So the type of content marketing you choose in the end should align with what you want to achieve.

For example, if your goal is to get more traffic to your website, then perhaps email marketing is not the right type of content for that goal. You may want to consider blogging so you can rank high on Google and get passive traffic over time.

What’s right for your niche?

Certain types of content make more sense for certain niches.

For example, if you’re teaching breakdancing, then a podcast doesn’t make sense. No one’s going to sit down and figure out how to bust moves from audio. But videos are perfect because your audience can watch and learn how to execute those moves.

What’s right for your audience?

How does your audience like to consume content? Are they predominantly reading, or are they watching videos on YouTube? Which platforms are they spending most of their time on? Is it YouTube, TikTok, Instagram, Twitter, or something else?

If you’re unsure, find out by looking at what your audience is sharing on social media or communities like Reddit. For example, there are hardly any b‑boys or b‑girls on Twitter. But they’re all over Instagram.

B-boy's post on Instagram

What’s right for you?

Content marketing can be resource-intensive. It makes a lot of sense to work on something that you enjoy and something that is your strength.

If you are an excellent writer but aren’t very confident on camera, then perhaps it may not be a good idea to produce videos. Blogging may be a better choice.

Final thoughts

Don’t be mistaken. Creating content by itself won’t work. There are plenty of stories of abandoned blogs, YouTube channels, podcasts, and social media accounts.

For your chosen type of content marketing to work, you need to get more reach for your content and make sure the content helps your business to grow.

Any questions or comments? Let me know on Twitter.




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Google CEO Confirms AI Features Coming To Search “Soon”

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Google CEO Confirms AI Features Coming To Search "Soon"

Google announced today that it will soon be rolling out AI-powered features in its search results, providing users with a new, more intuitive way to navigate and understand the web.

These new AI features will help users quickly understand the big picture and learn more about a topic by distilling complex information into easy-to-digest formats.

Google has a long history of using AI to improve its search results for billions of people.

The company’s latest AI technologies, such as LaMDA, PaLM, Imagen, and MusicLM, provide users with entirely new ways to engage with information.

Google is working to bring these latest advancements into its products, starting with search.

Statement From Google CEO Sundar Pichai

Sundar Pichai, CEO of Google and Alphabet, released a statement on Twitter about a conversational AI service that will be available in the coming weeks.

Bard, powered by LaMDA, is Google’s new language model for dialogue applications.

According to Pichai, Bard, which leverages Google’s vast intelligence and knowledge base, can deliver accurate and high-quality answers:

“In 2021, we shared next-gen language + conversation capabilities powered by our Language Model for Dialogue Applications (LaMDA). Coming soon: Bard, a new experimental conversational #GoogleAI service powered by LaMDA.

Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence, and creativity of our large language models. It draws on information from the web to provide fresh, high-quality responses. Today we’re opening Bard up to trusted external testers.

We’ll combine their feedback with our own internal testing to make sure Bard’s responses meet our high bar for quality, safety, and groundedness and we will make it more widely available in coming weeks. It’s early, we will launch, iterate and make it better.”

In Summary

Increasingly, people are turning to Google for deeper insights and understanding.

With the help of AI, Google can consolidate insights for questions where there is no one correct answer, making it easier for people to get to the core of what they are searching for.

In addition to the AI features being rolled out in search, Google is also introducing a new experimental conversational AI service called Bard. Powered by LaMDA, Bard will use Google’s vast intelligence and knowledge base to deliver accurate and high-quality answers to users.

Google continues demonstrating its commitment to making search more intuitive and effective for users. As Pichai said in his statement, the company will continue to launch, iterate, and improve these new offerings in the coming weeks and months.

Source: Google



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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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