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How to Choose an Enterprise SEO Tool



How to Choose an Enterprise SEO Tool

Enterprise SEO tools are made to meet the needs of larger and more complex companies. They typically need to support more users and have controls and permissions for what different user groups can access. Companies need the user interface to cover many languages so teams all over the world can use the tools.

Enterprises usually have the highest demands for data. They also need high limits and easy access through APIs so they can get quick insights and report on progress.

Let’s look at your options.

Popular enterprise SEO platforms

There are a lot of tools for specific tasks, but SEO platforms have many tools to help businesses with a variety of needs. Some popular choices include:

  1. Ahrefs Enterprise
  2. Conductor
  3. seoClarity
  4. Searchmetrics
  5. Botify
  6. BrightEdge

As I mentioned, these tools have to do a lot of different things. Some of the common tasks they need to cover are:

  • Keyword research
  • Content creation
  • Competitor research
  • Link building
  • Rank tracking
  • Reporting
  • Technical SEO
  • International SEO
  • Forecasting

But with so many different tools and so many different needs, how do you know what’s right for you? Let’s look at how many companies shop for tools.

The typical shopping process

The process for onboarding a new tool is similar for most enterprise companies. 

Figure out your needs

What are your goals and your success criteria? 

This is likely to be different based on your SEO maturity. You may want visibility into rankings. Or you may have specific goals like increasing the number of keywords ranking in the top three positions or increasing traffic by x% YoY. 

Your goals may even be related to the organization, such as bringing SEO in-house or increasing SEO awareness in your organization. Thinking about where your company is in its SEO maturity can help you figure out your needs.

An image describing the different levels of SEO maturity that most companies go through

Who will use the tool? What will they use it for? What data is needed? This brainstorming session usually turns into a wish list of features. You’ll probably end up adding more to this list as you see some things in particular tools that you want to add. 

Demos and trials

This is the fun part. Companies will usually run you through how to use their tools, show you cool use cases, and give you access so you can play around with the tools to make sure they fit your needs.

Decision time

I’ve seen many companies evaluate tools based on how well they fit their needs. They usually score features and tally up the totals to narrow down their choices.

I recommend using the scores as a guide, but don’t base your decisions solely on the scoring. Talk to your team about what they like in different tools, check if the tools have the data they need, and make sure the tool is easy to use and they actually want to use it. A tool is only valuable if it is used effectively by your team. 

Make a business case

You will have to make a business case and get buy-in from leadership in order to secure funding for a tool.

This step may also come earlier in the process, but I personally think this is where it belongs. You’ve done the legwork needed to answer all the right questions about why a tool is needed. And now, you know what tool(s) you want and the cost of it (them). With this information, you can make a much stronger business case and present your options.

Vendor onboarding

There’s no easy way to say this. Vendor onboarding is typically a painful process for everyone involved.

This can easily be the longest part of the process, as it usually involves a lot of people for sign-offs, budgeting, procurement, legal, and security. It’s the redlining process where things are marked out of terms, conditions can be intense, and the information that companies ask for can be extremely specific.

Don’t panic! It’s never fun, but lots of companies have been through this before. Just take deep breaths, and you’ll survive this part of the process.

I’ve been around for a while, and I’ve been through this process. I’ve made mistakes, and I’ve talked to many others about their experiences as well. Here are some things I’d suggest looking into more.

Jack of all trades, master of none

Some tools seem to build features to check all the boxes on the shopping lists we talked about earlier. They may look great on paper. But when you go to use them, you realize that many of those tools may be mediocre and contain questionable data. 

For example, as a technical SEO, I’ve found some tools to have things in their audit that they flag as issues—even though Google has said many times said things are not issues.

The platforms often become reporting platforms rather than being used to help people do their jobs better. This can be because of long update frequencies. Or in some cases, you have to submit a request to get data, which slows down the process.

You may encounter features that sound great on paper, such as automated insights. But more often than not, those insights have questionable value. They cause people to waste a lot of time doing things that don’t have an impact.

Ahrefs is generally the preferred tool for people who work in SEO. SEOs trust our data and find our tool full-featured and easy to use. We also have so much educational material that SEOs can rely on to be accurate and help guide them through almost any subject.

We also have real SEO experts on staff who use the platform daily and help shape its future. 

Obscured pricing

A lot of companies in this space require you to contact them for pricing. They will build a “custom” package for you after asking about your budget—because they want to know what you’re willing or able to pay. 

One company may be paying 20X more than another company for the exact same package. This part of the process can be far from transparent.

At Ahrefs, all of our prices are listed along with the limits and the cost of all add-ons.

Check the cancellation process

I personally think this can tell you a lot about any company. Some companies may make this difficult. You may have to contact them to cancel or have a meeting before you’re able to cancel. 

Read your contract very carefully. Some companies require a written notice several months in advance if you want to cancel. Of course, they’re not going to remind you of this when your contract is about to be due.

At Ahrefs, we send you a reminder before your renewal date.

Aggressive sales teams

I have nightmares about being contacted by a person from a certain company after being overwhelmed by him a few years ago. I still remember his name to this day, and I’m pretty bad with names in general.

That company isn’t the only one known for aggressive salespeople, though. Some will call you, email you, email your personal email, call your personal cell phone, and message you on every social platform you’re on. 

When that doesn’t work, they start doing the same to coworkers, your boss, your boss’s boss. They may even email your boss and tell them how bad of a job you’re doing because you don’t use their platform. Yes, it’s really a thing, and it’s ridiculous.

They also tend to overpromise. They’ll tell you things like the tool is all you need and can replace an SEO team. This is never true.

I’d recommend searching a few platforms like Twitter, Facebook, and Reddit for the names of the companies so you can see some of the stories and what people think about some of the enterprise SEO tools. You should hear the stories and experiences of others before signing a contract. You’ll find that many of the enterprise tools do not have the best reputation with SEOs.

At Ahrefs, we have a small accounts team to help teams assess whether Ahrefs is a good fit for their organization. After an initial discovery call, our team will develop a custom demo, guide the evaluation process by bringing all the stakeholders on the same page, and help navigate any red tape. Transparency is core to the process. To avoid surprises, Ahrefs lets customers test-drive the product before purchasing.

Once a customer decides to work with us, the Ahrefs team will provide custom training sessions specific to the use cases they need, help them learn how to use Ahrefs quickly, and ultimately reduce the time it takes to reach their goals.


This can be a positive or a negative, to be honest. It’s kind of a fine line that tool companies have to walk when also providing services. I’ve seen some vendors step over this line and try to steal work from agency partners—even those who had recommended their tool to the company.

If you need services, check what they offer and see if you can find some people who work with them so you can ask some questions. Some of the consultants at companies do good work, and others will provide work at about the same level as a junior SEO. I’ve seen services pitched as SEO consulting when all they really did was use the hours to help set up the tool.

Your reps will likely vary in their skill sets a lot, and some of the companies have high employee turnover. In many cases, you may be better off with an agency partner.

Ahrefs does not offer client service work. 

Lack of innovation

Just like at some enterprise companies, some of these tool companies can be slow-moving. You will hear typical excuses like it’s on the roadmap or it’s coming soon. But in many cases, the features just never show up. In some cases, they may acquire other tools to try to make up for the lack of innovation, but they may kill whatever made the previous tool worth acquiring.

We do roundups of all of our product updates every month or two. You can see the kind of progress we make and the innovative features we launch. 

Biased comparisons and studies

Lots of these tools will show you cherry-picked comparisons that make them look awesome and studies they have run where they declare themselves the winner. Take all of this with a grain of salt.

Don’t just take any tool’s word that it’s the best. Ask around. See who comes up as the platform of choice. See who real users favor.

Why Ahrefs is the right choice

As I mentioned earlier, we’re the favorite tool for SEO teams. We epitomize big data

We crawl faster than any other SEO tool, according to Cloudflare Radar. 

Image showing AhrefsBot as the 4th fastest crawler on the web according to Cloudflare Radar
Image showing AhrefsBot as the 4th fastest crawler on the web according to Cloudflare Radar

We’re the best backlink checker, according to Matthew Woodward’s test of 1 million domains. We’re the only SEO tool that will pick up links added with JavaScript for our backlink index because we’re the only one that renders pages while crawling the web.

We have the largest keyword database for U.S. keywords and the most accurate traffic estimates, according to Authority Hacker. For keywords where we have enough data, we use individually modeled click-through rate (CTR) curves rather than a single generic model for the curves.

Getting data into your own systems with Ahrefs is easy using our API. We give you the request needed based on the report you’re in and the filters you have set.

By clicking the API button you get the full curl response needed for an API requestBy clicking the API button you get the full curl response needed for an API request

You can also get data out of the platform with our Looker Studio connector (formerly Google Data Studio).

For Site Audit, we only charge for internal HTML pages that return a 200 HTTP status code.

There are also our industry-leading articles, videos, and courses. These resources will educate you on SEO and show you how to make the best use of the platform.

Final thoughts

Onboarding an enterprise SEO tool can be a long and difficult process. Getting rid of a bad tool once it’s integrated into your systems can be even harder. Make sure you do your homework and select the right solution for you. 

As we’ve built out our enterprise offering, we’ve added a lot of features that enterprise companies need to be successful and all the pieces they need to meet compliance guidelines. If you have a feature you want to see us add, message me on Twitter.

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Google Updates Structured Data Guidance To Clarify Supported Formats



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



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


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 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 (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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Featured Image: FGC/Shutterstock

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Google Updates Search Console Video Indexing Report



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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