SEO
How Accurate Are the Search Traffic Estimations in Ahrefs? (New Research)
One of the most common use cases of Ahrefs is to check how much organic search traffic a given website (or webpage) gets.
It is widely known that the search traffic numbers in Ahrefs are merely estimations. But how far do they deviate from the actual traffic numbers?
We decided to run a small study and try to quantify that.
What to expect from Ahrefs’ traffic estimations
Before I share the results of our study with you, I think it is quite important that you understand how Ahrefs traffic estimates are calculated in the first place.
Here’s what it looks like in a nutshell:
- Take all keywords that a website ranks for
- Pull the monthly search volume of each keyword
- Look up that website’s ranking position for each keyword
- Predict the CTR for each of their search snippets
- Sum up clicks from each keyword and compute the total estimated monthly search traffic
Can you tell at which step the discrepancies kick in? (Hint: each one)
- Keywords – It is practically impossible to know all the keywords that a given website is ranking for.
- Monthly search volumes – These aren’t particularly precise either (see our recent study).
- Ranking positions – The SERPs are very volatile. Today, your page ranks #3; a day later, it ranks #4.
- CTR – It is incredibly hard to predict the CTR of a page on a SERP. There are just too many factors to consider: search intent, ads, SERP features, brand affinity, etc.
As you can tell, the data that we (or any other SEO tool) use for computing the search traffic estimations has many issues that are nearly impossible to fix. And these issues inevitably affect the quality of the resulting traffic estimations.
And yet, even though our traffic estimations may deviate from the actual data quite a bit, they’re still good enough to be of immense value for digital marketing professionals of all kinds.
Especially so when one understands how these values are being computed (which you do now) and can, therefore, factor in some level of discrepancy while using that data (more on that later).
So that aside, let’s finally see the results of our research.
How accurate are Ahrefs’ search traffic estimations?
This very study is inspired by the good folks at AuthorityHacker. They recently performed research comparing the quality of search traffic estimations from six different SEO tools.
In their study, Ahrefs turned out to be the winner (with an average discrepancy of 22.5% and 0.99 correlation with GSC data).
But that study was done on a small sample of just 50 websites. So we decided to replicate it on a larger scale and see how the numbers change.
In our study, we took 1,635 random websites and compared their monthly GSC traffic in the U.S. to their monthly U.S. organic traffic estimates in Ahrefs.
We studied the same two factors:
- How much Ahrefs’ traffic estimations deviate from GSC
- How consistent our traffic estimations are when compared to GSC
Deviation
In our studied sample of 1,635 websites, the median deviation turned out to be 49.52%.
In other words, most of the time you can expect Ahrefs to misreport a website’s U.S. traffic by up to half of its value.
This may seem like a lot. But in reality, the margin of our error largely depends on the type of website and the industry the site is in. For some websites, we are off by less than 5%. For some others, we can be off by more than 1,000%.
But more often than not, our estimates are rather good:
To put our median deviation of 49.52% in perspective, we also calculated it for SEMrush, and it turned out to be 68.36%.
Consistency
Here’s what the “consistency” of traffic estimations refers to:
If one website gets more traffic than the other website (according to GSC data), that should hold true when looking at Ahrefs’ data (regardless of the accuracy of the estimates).
As you may have already guessed, this can be studied by calculating the good old correlation between the two sets of values.
In our sample size of 1,635 websites, the monthly GSC traffic in the U.S. correlated with that of Ahrefs’ at 0.76 (Pearson’s). This means that the above statement will hold true in the vast majority of cases.
Sidenote.
In case you’re unfamiliar with correlations, “1” means that the two sets of values are in perfect sync. And 0.76 is pretty close to 1.
As for SEMRush, theirs was 0.74 for the same set of websites.
A workaround for manually fixing the discrepancy
As you just learned, Ahrefs can be off by a rather considerable amount when estimating a website’s traffic. But at the same time, it is highly consistent in its traffic estimations. Even more so if the websites you’re comparing belong to the same industry.
What this means is that you can better estimate the actual search traffic of your competitors by using this simple formula:
The relationship between GSC traffic and Ahrefs traffic for your own website should hold roughly true for your competitors’ (given that they’re in the same niche as you). So more often than not, using this formula should produce a pretty accurate result.
Final words
Hopefully, this helped you folks understand what you can expect from Ahrefs’ search traffic estimations and how to work around the discrepancy to get much more accurate data.
But most important of all, the results of this research will now serve as a reference point for our product team on our quest to further improve the accuracy of our estimations. Even though it is technically impossible to make them perfect, there’s still quite a bit of room for improvement—which we can try to address.
As always, should you have any comments or questions, you can find me on Twitter.
P.S. Big thanks to Alex from our Data Science team for helping me with this.
SEO
Google Analytics 4 Features To Prepare For Third-Party Cookie Depreciation

Google will roll out new features and integrations for Google Analytics 4 (GA4) for first-party data, enhanced conversions, and durable ad performance metrics.
Beginning in Q1 2024, Chrome will gradually phase out third-party cookies for a percentage of users, allowing for testing and transition.
Third-party cookies, which have been central to cross-site tracking, are being restricted or phased out by major browsers, including Chrome, as part of its Privacy Sandbox project.
The following features should help advertisers “unlock durable performance” while preserving user privacy.
Support For Protected Audience API In GA4
A key feature of recent updates to Google Analytics 4 is the integration of Protected Audience API, a Privacy Sandbox technology that is set to become widely available in early 2024.
This API allows advertisers to continue reaching their audiences after the third-party cookie phase-out.
What Is The Protected Audience API?
The Protected Audience API offers a novel approach to remarketing, which involves reminding users about sites and products they have shown interest in without relying on third-party cookies.

This method involves advertisers informing the browser directly about their interest in showing ads to users in the future.
The browser then uses an algorithm to determine which ads to display based on the user’s web activity and advertiser inputs.
It enables on-device auctions by the browser, allowing it to choose relevant ads from sites previously visited by the user without tracking their browsing behavior across different sites.
Key Features And Development
Key features of the Protected Audience API include interest groups stored by the browser, on-device bidding and ad selection, and ad rendering in a temporarily relaxed version of Fenced Frames.
The API also supports a key/value service for real-time information retrieval, which can be used by both buyers and sellers for various purposes, such as budget calculation or policy compliance.
The Protected Audience API, initially known as the FLEDGE API, has evolved from an experimental stage to a more mature phase, reflecting its readiness for wider implementation.
This transition is part of Google’s broader efforts to develop privacy-preserving APIs and technologies in collaboration with industry stakeholders and regulatory bodies like the UK’s Competition and Markets Authority.
The Protected Audience API offers a new way to connect with users while respecting their privacy, necessitating a reevaluation of current advertising strategies and a focus on adapting to these emerging technologies.
Support For Enhanced Conversions
Rolling out in the next few weeks, enhanced conversions is a feature enhancing conversion measurement accuracy.


Enhanced conversions for the web cater to advertisers tracking online sales and events. It captures and hashes customer data like email addresses during a conversion on the web, then matches this with Google accounts linked to ad interactions.
This method recovers unmeasured conversions, optimizes bidding, and maintains data privacy.
For leads, enhanced conversions track sales from website leads occurring offline. It uses hashed data from website forms, like email addresses, to measure offline conversions.
Setup options for enhanced conversions include Google Tag Manager, a Google tag, or the Google Ads API, with third-party partner support available.
Advertisers can import offline conversion data for Google Ads from Salesforce, Zapier, and HubSpot with Google Click Identifier (GCLID).
Proper Consent Setup
To effectively use Google’s enhanced privacy features, it’s essential to have proper user consent mechanisms in place, particularly for traffic from the European Economic Area (EEA).
Google’s EU user consent policy mandates consent collection for personal data usage in measurement, ad personalization, and remarketing features. This policy extends to website tags, app SDKs, and data uploads like offline conversion imports.
Google has updated the consent mode API to include parameters for user data consent and personalized advertising.
Advertisers using Google-certified consent management platforms (CMPs) will see automatic updates to the latest consent mode, while those with self-managed banners should upgrade to consent mode v2.
Implementing consent mode allows you to adjust Google tag behavior based on user consent, ensuring compliance and enabling conversion modeling for comprehensive reporting and optimization.
Consent Mode integration with CMPs simplifies managing consent banners and the consent management process, adjusting data collection based on user choices and supporting behavioral modeling for a complete view of consumer performance.
Durable Ad Performance With AI Essentials
To effectively utilize AI, marketers need robust measurement and audience tools for confident decision-making.
Google provided a general checklist of AI essentials for Google advertisers. In it, advertisers are encouraged to adopt AI-powered search and Performance Max campaigns, engage in Smart Bidding, and explore video campaigns on platforms like YouTube.
Google also offers a more in-depth checklist for Google Ads, Display & Video 360, and Campaign Manager 360.


More Ways To Prepare For The Third-Party Cookie Phase Out
As third-party cookies are phased out, it’s essential to audit and modify web code, especially focusing on instances of SameSite=None using tools like Chrome DevTools.
Adapting to this change involves understanding and managing both third-party and first-party cookies, ensuring they are set correctly for cross-site contexts and compliance.
Chrome provides solutions like Partitioned cookies with CHIPS and Related Website Sets.
At the same time, the Privacy Sandbox introduces APIs for privacy-centric alternatives, with additional support for enterprise-managed Chrome and ongoing development of tools and trials to assist in the transition.
As Google continues to update resources and documentation to reflect these changes, stakeholders are encouraged to engage and provide feedback, ensuring that the evolution of these technologies aligns with industry needs and user privacy standards.
Featured image: Primakov/Shutterstock
SEO
4 Ways To Try The New Model From Mistral AI

In a significant leap in large language model (LLM) development, Mistral AI announced the release of its newest model, Mixtral-8x7B.
magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%https://t.co/uV4WVdtpwZ%3A6969%2Fannounce&tr=http%3A%2F%https://t.co/g0m9cEUz0T%3A80%2Fannounce
RELEASE a6bbd9affe0c2725c1b7410d66833e24
— Mistral AI (@MistralAI) December 8, 2023
What Is Mixtral-8x7B?
Mixtral-8x7B from Mistral AI is a Mixture of Experts (MoE) model designed to enhance how machines understand and generate text.
Imagine it as a team of specialized experts, each skilled in a different area, working together to handle various types of information and tasks.
A report published in June reportedly shed light on the intricacies of OpenAI’s GPT-4, highlighting that it employs a similar approach to MoE, utilizing 16 experts, each with around 111 billion parameters, and routes two experts per forward pass to optimize costs.
This approach allows the model to manage diverse and complex data efficiently, making it helpful in creating content, engaging in conversations, or translating languages.
Mixtral-8x7B Performance Metrics
Mistral AI’s new model, Mixtral-8x7B, represents a significant step forward from its previous model, Mistral-7B-v0.1.
It’s designed to understand better and create text, a key feature for anyone looking to use AI for writing or communication tasks.
New open weights LLM from @MistralAI
params.json:
– hidden_dim / dim = 14336/4096 => 3.5X MLP expand
– n_heads / n_kv_heads = 32/8 => 4X multiquery
– “moe” => mixture of experts 8X top 2 👀Likely related code: https://t.co/yrqRtYhxKR
Oddly absent: an over-rehearsed… https://t.co/8PvqdHz1bR pic.twitter.com/xMDRj3WAVh
— Andrej Karpathy (@karpathy) December 8, 2023
This latest addition to the Mistral family promises to revolutionize the AI landscape with its enhanced performance metrics, as shared by OpenCompass.
What makes Mixtral-8x7B stand out is not just its improvement over Mistral AI’s previous version, but the way it measures up to models like Llama2-70B and Qwen-72B.
It’s like having an assistant who can understand complex ideas and express them clearly.
One of the key strengths of the Mixtral-8x7B is its ability to handle specialized tasks.
For example, it performed exceptionally well in specific tests designed to evaluate AI models, indicating that it’s good at general text understanding and generation and excels in more niche areas.
This makes it a valuable tool for marketing professionals and SEO experts who need AI that can adapt to different content and technical requirements.
The Mixtral-8x7B’s ability to deal with complex math and coding problems also suggests it can be a helpful ally for those working in more technical aspects of SEO, where understanding and solving algorithmic challenges are crucial.
This new model could become a versatile and intelligent partner for a wide range of digital content and strategy needs.
How To Try Mixtral-8x7B: 4 Demos
You can experiment with Mistral AI’s new model, Mixtral-8x7B, to see how it responds to queries and how it performs compared to other open-source models and OpenAI’s GPT-4.
Please note that, like all generative AI content, platforms running this new model may produce inaccurate information or otherwise unintended results.
User feedback for new models like this one will help companies like Mistral AI improve future versions and models.
1. Perplexity Labs Playground
In Perplexity Labs, you can try Mixtral-8x7B along with Meta AI’s Llama 2, Mistral-7b, and Perplexity’s new online LLMs.
In this example, I ask about the model itself and notice that new instructions are added after the initial response to extend the generated content about my query.


While the answer looks correct, it begins to repeat itself.


The model did provide an over 600-word answer to the question, “What is SEO?”
Again, additional instructions appear as “headers” to seemingly ensure a comprehensive answer.


2. Poe
Poe hosts bots for popular LLMs, including OpenAI’s GPT-4 and DALL·E 3, Meta AI’s Llama 2 and Code Llama, Google’s PaLM 2, Anthropic’s Claude-instant and Claude 2, and StableDiffusionXL.
These bots cover a wide spectrum of capabilities, including text, image, and code generation.
The Mixtral-8x7B-Chat bot is operated by Fireworks AI.


It’s worth noting that the Fireworks page specifies it is an “unofficial implementation” that was fine-tuned for chat.
When asked what the best backlinks for SEO are, it provided a valid answer.


Compare this to the response offered by Google Bard.


3. Vercel
Vercel offers a demo of Mixtral-8x7B that allows users to compare responses from popular Anthropic, Cohere, Meta AI, and OpenAI models.


It offers an interesting perspective on how each model interprets and responds to user questions.


Like many LLMs, it does occasionally hallucinate.


4. Replicate
The mixtral-8x7b-32 demo on Replicate is based on this source code. It is also noted in the README that “Inference is quite inefficient.”


In the example above, Mixtral-8x7B describes itself as a game.
Conclusion
Mistral AI’s latest release sets a new benchmark in the AI field, offering enhanced performance and versatility. But like many LLMs, it can provide inaccurate and unexpected answers.
As AI continues to evolve, models like the Mixtral-8x7B could become integral in shaping advanced AI tools for marketing and business.
Featured image: T. Schneider/Shutterstock
SEO
OpenAI Investigates ‘Lazy’ GPT-4 Complaints On Google Reviews, X

OpenAI, the company that launched ChatGPT a little over a year ago, has recently taken to social media to address concerns regarding the “lazy” performance of GPT-4 on social media and Google Reviews.

This move comes after growing user feedback online, which even includes a one-star review on the company’s Google Reviews.
OpenAI Gives Insight Into Training Chat Models, Performance Evaluations, And A/B Testing
OpenAI, through its @ChatGPTapp Twitter account, detailed the complexities involved in training chat models.


The organization highlighted that the process is not a “clean industrial process” and that variations in training runs can lead to noticeable differences in the AI’s personality, creative style, and political bias.
Thorough AI model testing includes offline evaluation metrics and online A/B tests. The final decision to release a new model is based on a data-driven approach to improve the “real” user experience.
OpenAI’s Google Review Score Affected By GPT-4 Performance, Billing Issues
This explanation comes after weeks of user feedback about GPT-4 becoming worse on social media networks like X.
Idk if anyone else has noticed this, but GPT-4 Turbo performance is significantly worse than GPT-4 standard.
I know it’s in preview right now but it’s significantly worse.
— Max Weinbach (@MaxWinebach) November 8, 2023
There has been discussion if GPT-4 has become “lazy” recently. My anecdotal testing suggests it may be true.
I repeated a sequence of old analyses I did with Code Interpreter. GPT-4 still knows what to do, but keeps telling me to do the work. One step is now many & some are odd. pic.twitter.com/OhGAMtd3Zq
— Ethan Mollick (@emollick) November 28, 2023
Complaints also appeared in OpenAI’s community forums.


The experience led one user to leave a one-star rating for OpenAI via Google Reviews. Other complaints regarded accounts, billing, and the artificial nature of AI.


A recent user on Product Hunt gave OpenAI a rating that also appears to be related to GPT-4 worsening.


GPT-4 isn’t the only issue that local reviewers complain about. On Yelp, OpenAI has a one-star rating for ChatGPT 3.5 performance.
OpenAI is now only 3.8 stars on Google Maps and a dismal 1 star on Yelp!
GPT-4’s degradation has really hurt their rating. Hope the business survives.https://t.co/RF8uJH1WQ5 pic.twitter.com/OghAZLCiVu
— Nate Chan (@nathanwchan) December 9, 2023
The complaint:


In related OpenAI news, the review with the most likes aligns with recent rumors about a volatile workplace, alleging that OpenAI is a “Cutthroat environment. Not friendly. Toxic workers.”


The reviews voted the most helpful on Glassdoor about OpenAI suggested that employee frustration and product development issues stem from the company’s shift in focus on profits.


This incident provides a unique outlook on how customer and employee experiences can impact any business through local reviews and business ratings platforms.


Google SGE Highlights Positive Google Reviews
In addition to occasional complaints, Google reviewers acknowledged the revolutionary impact of OpenAI’s technology on various fields.
The most positive review mentions about the company appear in Google SGE (Search Generative Experience).


Conclusion
OpenAI’s recent insights into training chat models and response to public feedback about GPT-4 performance illustrate AI technology’s dynamic and evolving nature and its impact on those who depend on the AI platform.
Especially the people who just received an invitation to join ChatGPT Plus after being waitlisted while OpenAI paused new subscriptions and upgrades. Or those developing GPTs for the upcoming GPT Store launch.
As AI advances, professionals in these fields must remain agile, informed, and responsive to technological developments and the public’s reception of these advancements.
Featured image: Tada Images/Shutterstock
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