How Accurate Are the Search Traffic Estimations in Ahrefs, Moz, and Semrush? (New Research)

In this article, you’ll learn how traffic estimations actually work inside the major SEO tools, how far those numbers deviate from reality, which tool comes closest, and how to work around the inevitable discrepancies so you can make smarter decisions about your content and competitors. You’ll also learn why traditional traffic estimations miss an increasingly important channel — AI search — and what you can do about it.

How SEO Tools Estimate Search Traffic

Before judging accuracy, you need to understand what these tools are actually doing behind the scenes. Every major SEO platform — Ahrefs, Semrush, and Moz — follows roughly the same five-step process to compute organic traffic estimates:

1. Crawl and index as many keywords as possible.

2. Pull the estimated monthly search volume for each keyword.

3. Check where a given website ranks for each keyword.

4. Predict the click-through rate (CTR) for each ranking position.

5. Multiply volume by CTR, then sum everything up into a monthly traffic estimate.

Each step introduces error. No tool knows every keyword a site ranks for. Search volume data itself is an approximation. Rankings fluctuate daily. And CTR varies wildly depending on search intent, SERP features, brand recognition, and whether an AI Overview is eating clicks at the top of the page.

The result is always an estimate — never a fact. That applies equally to Ahrefs, Semrush, Moz, and every other tool in the market.

Understanding this process is important because it sets realistic expectations. When someone asks “is Ahrefs accurate?”, the honest answer is: it depends on how you define accuracy, and which website you’re looking at.

What the Research Says: Accuracy by Tool

Multiple independent studies have compared third-party traffic estimates against actual Google Search Console (GSC) data. Here’s what they found.

Ahrefs

Ahrefs ran its own study on 1,635 websites comparing its U.S. organic traffic estimates against GSC data. The median deviation was 49.52%, meaning Ahrefs typically underreports a website’s traffic by about half.

That sounds bad in isolation. But context matters. For some sites, Ahrefs was off by less than 5%. For others, the gap exceeded 1,000%. The median tells you what to expect most of the time, not every time.

Ahrefs also measured consistency — whether sites with more GSC traffic also showed more traffic in Ahrefs, regardless of the absolute numbers. The Pearson correlation was 0.76, which is strong. It means Ahrefs reliably ranks websites in the right order, even when the raw numbers are off.

The independent study by Authority Hacker confirmed this. Across 47 sites, Ahrefs had the lowest average discrepancy (22.5%) and the highest correlation with GSC data (0.99) of six tools tested. Ahrefs also consistently underreported traffic rather than overreporting it — which makes it a more predictable tool to work with.

Semrush

In the same Ahrefs study, Semrush’s median deviation was 68.36% — roughly 19 percentage points worse than Ahrefs. Its correlation with GSC data was 0.74, just slightly below Ahrefs’ 0.76.

A separate Collaborator.pro study across 184 websites found Semrush’s average error rate at 61.58%, with a tendency to overestimate traffic. In some cases, Semrush reported 130,000 visits when the actual figure was 50,000.

Semrush’s overestimation tendency is worth noting. If you’re using Semrush to size up a competitor, you might conclude they’re getting more traffic than they actually are. That can lead to overvaluing their content strategy or undervaluing your own.

Moz

Moz has the smallest keyword database of the three tools, which limits the completeness of its traffic estimates. In Authority Hacker’s study, Moz Pro performed noticeably worse than both Ahrefs and Semrush on traffic estimation accuracy.

Moz’s strength lies more in domain authority scoring and link analysis than in raw traffic estimation. If traffic data is your primary need, Moz is the weakest of the three options.

Similarweb

Similarweb takes a different approach. Instead of relying primarily on keyword rankings and search volume, it uses clickstream data from browser extensions and ISP partnerships to model total website traffic (not just organic search).

In the Collaborator.pro study, Similarweb’s error rate was 56.95% — better than Semrush, worse than Ahrefs. However, Similarweb occasionally produces extreme outliers. The same study noted cases where Similarweb estimated traffic at 3.6 million visits for a site that Ahrefs estimated at 86,000.

Similarweb is best treated as a directional indicator for total traffic, not as a precise organic search metric.

Summary: How the Tools Compare

Sources: Ahrefs internal study (1,635 sites), Collaborator.pro study (184 sites), Authority Hacker study (47 sites).

Why the Numbers Are Always Off

The deviation isn’t a bug — it’s a structural limitation. Here’s why no third-party tool can get traffic estimates right 100% of the time.

Incomplete keyword databases

Ahrefs tracks roughly 22 billion keywords. Semrush tracks around 26 billion. Those are enormous numbers, but they still don’t cover every query that someone types into Google. Long-tail queries, misspellings, conversational searches, and brand-specific terms often fall outside even the largest databases.

If a tool doesn’t know about a keyword your site ranks for, it can’t count the traffic from it. This alone accounts for a large chunk of the underreporting you see in Ahrefs.

Inaccurate search volumes

All third-party tools rely on Google Keyword Planner data (or their own estimates based on it) for monthly search volumes. But Google Keyword Planner groups related keywords together and rounds heavily, which means the volume you see for any single keyword might be significantly off.

A keyword showing 1,000 monthly searches might actually drive 400 or 2,200 — and the tool has no way of knowing which.

Volatile rankings

Rankings change constantly. A page might sit at position 3 when Ahrefs checks, but position 7 the next day. Since traffic estimates are based on a snapshot of rankings, any movement between crawls introduces error.

This is especially pronounced for competitive keywords where SERP volatility is high.

Unpredictable CTR

Click-through rates depend on far more than ranking position. Featured snippets, People Also Ask boxes, knowledge panels, video carousels, shopping results, and — increasingly — AI Overviews all reduce clicks to organic listings.

A page ranking #1 for an informational query might get a 30% CTR in one SERP and 8% in another, depending entirely on what else Google shows. No CTR model can account for all of this.

AI Overviews and zero-click searches

This is the newest and fastest-growing source of error. Google’s AI Overviews now appear on roughly 21% of all search queries, and they reduce organic CTR by an average of 34.5%.

If a tool’s CTR model was built before AI Overviews became widespread, it’s going to overestimate traffic for any query where an AI Overview appears. And since AI Overviews predominantly trigger on informational queries — the type most content marketing strategies target — this is a meaningful blind spot.

How to Work Around the Inaccuracy

Knowing the numbers are off doesn’t make them useless. It just means you need to use them differently.

Use ratios, not absolutes

The single most practical workaround is this: stop looking at absolute traffic numbers and start looking at ratios.

Here’s the logic. If Ahrefs underreports your site’s traffic by 40%, it probably underreports your competitor’s traffic by a similar percentage (assuming you’re in the same niche). The ratio between your traffic and theirs stays roughly constant.

This means you can use a simple formula to estimate a competitor’s actual traffic:

Competitor’s actual traffic ≈ (Competitor’s Ahrefs traffic / Your Ahrefs traffic) × Your GSC traffic

This works because Ahrefs’ traffic estimates are highly consistent (0.76 correlation). The tool might be wrong about the exact number, but it reliably preserves the relative order and magnitude.

Compare within the same tool

Never compare an Ahrefs estimate for one site against a Semrush estimate for another. Each tool uses different keyword databases, different CTR models, and different crawl schedules. Cross-tool comparisons introduce compounding errors.

Pick one tool and use it consistently across all competitors in a given analysis.

Cross-reference with GSC

For your own sites, always verify third-party estimates against GSC data. GSC gives you actual clicks, actual impressions, and actual average positions for every query Google tracks. It’s the closest thing to ground truth.

Use the third-party tool for competitor analysis (where you don’t have GSC access) and GSC for your own performance.

You can use Analyze AI’s free Website Traffic Checker to get a quick directional estimate for any domain, then verify your own data in GSC.

Track trends, not snapshots

A single traffic estimate on a single day is the least reliable data point you can get. But a trend over months is much more useful. If Ahrefs shows a competitor’s traffic steadily climbing from 50K to 80K over six months, you can be confident they’re growing — even if the actual numbers are different.

The direction of the line matters more than the height.

The Blind Spot: Traffic You Can’t See in Any SEO Tool

Here’s a problem that no traditional SEO tool addresses: AI search traffic.

When someone asks ChatGPT, Perplexity, Claude, or Gemini a question and those platforms cite your website, you get a visitor. That visitor shows up in your analytics as a referral from chatgpt.com, perplexity.ai, or similar domains. But Ahrefs, Semrush, and Moz don’t track this traffic at all. Their models are built exclusively around Google’s keyword-ranking-CTR pipeline.

This matters because AI referral traffic is growing fast. For many B2B and SaaS companies, AI-referred visitors already account for a meaningful share of organic discovery. If you’re only measuring traditional search traffic, you’re looking at an incomplete picture.

Why AI search traffic behaves differently

Traditional organic traffic is a function of keywords, rankings, and clicks. AI search traffic is a function of citations — whether an AI model mentions or links to your content when answering a user’s question.

The rules are different. You can rank #1 on Google for a query and still never get cited by ChatGPT. Conversely, a page buried on page 3 of Google might be a top source that AI models consistently reference.

This means you need a separate measurement layer for AI search — one that tracks which prompts trigger mentions of your brand, which AI platforms cite your content, and how much actual traffic those citations drive.

How to measure AI search traffic with Analyze AI

Analyze AI was built specifically for this. Its AI Traffic Analytics dashboard shows you every visitor arriving from AI platforms — broken down by source (ChatGPT, Perplexity, Claude, Gemini, Copilot), with engagement metrics like bounce rate, session time, and conversions.

This is the data that Ahrefs, Semrush, and Moz simply don’t have. And unlike their organic traffic estimates, this isn’t an estimation — it’s actual visitor data pulled from your site’s analytics.

The Landing Pages report goes a step further. It shows you exactly which pages on your site receive AI-referred traffic, which AI platforms cite each page, and how those visitors behave compared to your Google traffic.

This is where you find patterns. If certain types of content consistently attract AI citations and traffic, you can create more of it. If a page gets cited by ChatGPT but not Perplexity, you can investigate why and optimize accordingly.

How to Track Your Brand’s Visibility in AI Search

Beyond measuring traffic, you also need to understand how AI models perceive and present your brand. This is where traditional SEO tools offer zero visibility.

Monitor which prompts mention your brand

In Analyze AI’s Prompts dashboard, you can track specific prompts across ChatGPT, Perplexity, Claude, and Gemini to see whether your brand appears in the response, what position you’re mentioned in, and how positive the sentiment is.

Think of this as rank tracking for AI search. Instead of monitoring keyword positions on Google, you’re monitoring prompt positions across AI models. Both matter for organic discovery — they just operate in different channels.

You can also use the Suggested Prompts feature to discover new prompts your brand could target but currently doesn’t show up for. This is the AI search equivalent of keyword research — finding opportunities where demand exists but your brand isn’t visible yet.

Understand what sources AI models cite

The Sources dashboard shows every URL and domain that AI platforms cite when answering questions in your industry. You can see which competitor domains get cited most, which content types AI models prefer (blogs, product pages, review sites), and which specific pages carry the most weight.

This data is actionable. If AI models consistently cite G2 reviews and your product pages, but rarely cite your blog, that tells you where to focus. If a competitor’s documentation gets cited 3x more than yours, you know where the gap is.

Benchmark against competitors

Analyze AI’s Competitors view shows you exactly which brands appear alongside yours in AI responses. For each tracked prompt, you can see every competitor that gets mentioned, their position, and their sentiment score.

This is competitive intelligence that doesn’t exist in any traditional SEO tool. You can identify competitors who dominate AI search even if they rank poorly on Google, spot emerging brands that AI models are starting to favor, and track your share of voice over time.

Track brand perception

Beyond visibility, Analyze AI’s Perception Map shows you how AI models describe your brand — the exact language, themes, and attributes they associate with you. This matters because the way AI models talk about your brand shapes how millions of users perceive it.

If AI models consistently describe a competitor as “enterprise-grade” while calling your product “affordable,” that perception gap will influence buying decisions whether you like it or not. The Perception Map makes this visible so you can address it.

How to Cross-Reference Traditional and AI Search Data

The most complete picture of your organic visibility comes from combining three data sources:

Google Search Console gives you actual clicks and impressions from Google Search. This is ground truth for traditional organic performance.

A third-party SEO tool (Ahrefs, Semrush, or Moz) gives you estimated competitor traffic and keyword data. Use this for competitive benchmarking, not absolute numbers.

Analyze AI gives you AI search visibility, AI-referred traffic, citation data, and brand perception across ChatGPT, Perplexity, Claude, and Gemini. Use this for the organic channel that traditional tools can’t measure.

Together, these three sources cover the full organic discovery landscape. Separately, each one shows you only part of the picture.

Analyze AI also sends weekly email digests that summarize your AI search performance — visibility changes, new competitor appearances, citation trends — so you can stay on top of this channel without logging in every day.

Practical Steps to Improve Your Traffic Estimation Workflow

Here’s a step-by-step workflow that accounts for the inaccuracies we’ve discussed and incorporates AI search data.

Step 1: Establish your GSC baseline

Log into Google Search Console and export your actual monthly clicks by page for the last 3 months. This is your ground truth for Google traffic.

Step 2: Compare against your SEO tool

Pull the same site’s traffic estimate from your preferred SEO tool (Ahrefs or Semrush). Calculate the deviation: (GSC traffic - Tool estimate) / GSC traffic.

This gives you your personal “correction factor.” If Ahrefs reports 60% of your actual traffic, you can apply that ratio when estimating competitor traffic.

Step 3: Apply the correction to competitors

For each competitor, pull their Ahrefs traffic estimate and multiply by your correction factor. This gives you a more realistic estimate of their actual Google traffic.

For example: if your correction factor is 1.67 (Ahrefs reports 60% of reality), and a competitor shows 30,000 in Ahrefs, their likely actual traffic is around 50,000.

Step 4: Add your AI search traffic layer

Connect your site to Analyze AI to measure AI-referred traffic. Look at the AI Traffic Analytics dashboard to see:

• Total visitors from AI platforms

• Which AI sources drive the most traffic (ChatGPT vs. Perplexity vs. Claude)

• Which landing pages receive AI traffic

• How AI visitors engage compared to Google visitors

This gives you the full picture of organic discovery — not just the Google portion.

Step 5: Identify AI search opportunities

Use Analyze AI’s Prompts and Competitors dashboards to find prompts where competitors appear but you don’t. These are your AI search keyword gaps — the equivalent of finding untapped keywords in traditional SEO, but for AI platforms.

Use Analyze AI’s free Keyword Generator and Keyword Difficulty Checker to evaluate whether those same topics also represent opportunities on Google. The best content strategy targets both channels simultaneously.

Step 6: Monitor weekly

Set up your Analyze AI weekly email digests to track changes in AI visibility. Review your GSC data monthly. Check your SEO tool’s traffic trends for competitors quarterly.

This cadence keeps you informed without drowning in dashboards.

Why This Matters More Now Than Ever

The organic search landscape is splitting in two. Traditional Google rankings still drive the majority of website traffic. But AI search — through ChatGPT, Perplexity, Claude, Gemini, and Google’s own AI Overviews — is growing as an independent discovery channel.

Neither channel is replacing the other. SEO is not dead. But the teams that treat AI search as an additional organic channel — and measure it with the same rigor they apply to Google — will have a structural advantage over those who don’t.

Third-party SEO tools like Ahrefs and Semrush remain essential for keyword research, competitor analysis, and backlink tracking. They’re imperfect at traffic estimation, but they’re the best available option for benchmarking competitors on Google.

For AI search, you need a different tool — one built specifically to track visibility, citations, and traffic across AI platforms. That’s what Analyze AI does.

The most accurate picture of your organic performance isn’t in any single tool. It’s in the combination of GSC (for ground truth), a traditional SEO platform (for competitive estimates), and an AI search analytics tool (for the channel that’s growing fastest). Use all three, understand the limitations of each, and make decisions based on trends rather than snapshots.