If you evaluate websites for a living like I do (or even casually research domains you’re thinking about buying) you’ve almost certainly familiar with SEMrush. It’s often the first tool people open when assessing a site’s perceived size, momentum, and visibility. Enter a domain, wait a moment, and you’re presented with traffic charts, growth curves, and monthly visit estimates that appear clean, quantitative, and authoritative.

The problem is not that SEMrush exists or that it is widely used. The problem is how its data is commonly interpreted. Repeatedly, I've had to explain to Buyers that the data does not reflect the real-world performance for a website they are interested in buying. Hence, the need for this article as a deep-dive as to why --

SEMrush traffic figures are estimates, not real analytics, and they can diverge significantly from actual site performance. Understanding why this happens—and how SEMrush data should and should not be used—is essential for anyone involved in acquisitions, monetization, or valuation.

At its core, SEMrush is a competitive intelligence platform. It was designed to help users understand search visibility, keyword rankings, and relative performance across websites. It is exceptionally strong at answering comparative questions: which site is ranking better, which keyword set is expanding, or whether SEO visibility is trending up or down over time. What SEMrush is not, however, is a measurement system for actual website traffic.

The most important limitation to understand is that SEMrush has no direct access to a site’s internal data. It does not see Google Analytics, server logs, CDN logs, or any other first-party tracking system. It cannot observe logged-in user behavior, payment flows, app usage, or private referral sources. SEMrush operates entirely outside the websites it analyzes, which means all of its traffic numbers are derived through modeling rather than measurement.

To produce traffic estimates at scale, SEMrush relies on a mix of third-party clickstream data, browser extensions, anonymized user panels, and statistical extrapolation. While these sources can be useful in aggregate, they are inherently incomplete. No third-party dataset captures the full spectrum of internet behavior, and none can replicate the precision of first-party analytics.

A large portion of SEMrush’s traffic modeling is keyword-based. In simple terms, the platform looks at which keywords a site ranks for, estimates the click-through rate associated with each ranking position, multiplies that by estimated search volume, and aggregates the results. This methodology works reasonably well for certain types of websites—particularly non-branded, SEO-heavy sites in mainstream niches—but it breaks down quickly in real-world scenarios.

Keyword-based modeling systematically undercounts several major traffic sources. Brand searches are often undervalued or excluded entirely, even though they frequently represent the highest-intent and highest-converting traffic. Long-tail keywords, which may drive substantial cumulative traffic, are often missing from public keyword databases. Zero-click search results, where Google answers the query directly without sending a visitor to a website, further distort click assumptions. Additionally, traffic from non-Google search engines, bookmarks, saved links, and returning users is largely invisible to keyword models.

Another structural issue is sampling bias. SEMrush’s datasets are not evenly distributed across geographies, devices, or user behavior profiles. Certain countries are overrepresented, while others are underrepresented. Desktop users may be sampled differently than mobile users. VPN usage, privacy tools, and logged-in environments all skew visibility. These distortions matter because traffic behavior varies significantly by region, device type, and audience sophistication.

This bias becomes especially pronounced in industries that fall outside mainstream consumer browsing patterns. Sites with privacy-conscious users, international audiences, or niche communities often show traffic figures in SEMrush that are materially lower than reality—not because the sites are underperforming, but because their users are poorly represented in third-party datasets.

Perhaps the largest blind spot in SEMrush’s traffic estimates is its limited visibility into private and non-search channels. Email marketing, direct navigation, social traffic, messaging apps, referrals, mobile apps, and logged-in ecosystems are all largely invisible to SEMrush. If a site’s growth is driven by community, repeat visitors, or off-search distribution, SEMrush may show flat or declining traffic even while real analytics show sustained or accelerating growth.

There is also the issue of algorithmic smoothing and data lag. To make charts readable and reduce noise, SEMrush averages and normalizes its estimates over time. While this produces visually appealing graphs, it masks volatility. Short-term spikes, campaign-driven surges, seasonal effects, and abrupt drops may not appear clearly—or may appear weeks later. For operators who live inside daily analytics dashboards, this smoothing can give a false sense of stability or misrepresent the timing of changes.

None of this means SEMrush is unreliable or unhelpful. On the contrary, it is one of the most powerful tools available for understanding relative performance and SEO dynamics. Where SEMrush excels is in directional insight rather than absolute truth. It is extremely effective for comparing sites against one another, identifying visibility trends, analyzing keyword footprints, and spotting competitive opportunities. Used properly, it provides valuable context that first-party analytics alone cannot offer.

Problems arise only when SEMrush traffic numbers are treated as precise or authoritative (because they aren't). In acquisitions especially, this mistake can be costly. Buyers may undervalue assets with strong brand or direct traffic. Sellers may be forced to defend legitimate performance that appears understated in third-party tools. Deals can fail simply because modeled estimates are mistaken for real measurement.

This is why serious due diligence always relies on first-party analytics. Only tools like Google Analytics ("GA"), server logs, or internal dashboards can accurately answer fundamental questions about actual user behavior. These systems measure real visits, real sessions, real sources, and real engagement. They capture what SEMrush cannot see and correct for the blind spots inherent in external modeling. That said, even GA does not count all traffic and is not an exact measure - but at least it has direct access to the website and the capacity to log most website traffic.

The correct way to use SEMrush in evaluation and acquisitions is as a supporting third-party instrument, not a source of truth. It should be used to understand visibility, validate SEO narratives, and compare relative performance across competitors. When SEMrush data aligns with first-party analytics, it reinforces confidence. When it diverges, the analytics should always take precedence.

In short, SEMrush is best thought of as a telescope rather than a measuring device. It provides an external view of how a site appears from the outside—how visible it is, how it compares, and how trends may be shifting. But it cannot tell you what is happening inside the business.

Understanding this distinction is a foundational skill for anyone buying, selling, or operating websites. Those who grasp it avoid mispricing assets, misreading performance, and making decisions based on assumptions rather than evidence.