Key Takeaways
- Higher education SEO performance should be benchmarked across Google, AI platforms, and program-level search visibility, not rankings and traffic alone.
- Non-branded search visibility helps universities reach students who have not yet chosen a school, while CTR, engagement, and conversion rates show how effectively that visibility turns into interest.
- Core Web Vitals and valid structured data are important for search and AI visibility because they help search engines and AI systems access, understand, and present university content correctly.
- AI visibility should be measured by program because each academic program can have different visibility levels and compete with a different group of schools in AI search.
- The Manaferra AI Index helps higher-ed teams compare their AI visibility, state position, program performance, and competitor set against other institutions.
Sixty percent of prospective students switch between platforms while researching colleges. This means ranking and click-through-rate data from a few years ago is no longer enough to explain how students find institutions. College discovery now happens across Google, AI platforms, rankings sites, listicles, and forums, often well before a student reaches a university website.
The higher education SEO benchmarks in this report provide a current reference point for organic visibility, click-through rate, engagement, lead generation, technical performance, and AI search presence. More importantly, they show how to compare those numbers with your own institution’s performance by program, market, and enrollment goal. Used well, these benchmarks reveal where your school is losing visibility to competitors and where focused improvements could create more qualified student demand.
Why SEO Benchmarks Matter for Higher Education in 2026
SEO benchmarks turn a general concern such as “our traffic feels off” into a clear measure of how far an institution is behind its peers. That evidence helps marketing teams identify priorities, defend investment, and focus on the SEO metrics to track based on the outcomes their institution is trying to improve.
Generic benchmarks can mislead universities because choosing a college is a long and complex decision. Manaferra’s State of College Search study found that 40% of prospective students start their search without a specific institution in mind, often beginning with broad questions about programs, careers, cost, or location. Their search may continue for months or years and include broad informational searches, visits to program pages, research across countries and languages, and renewed activity as application deadlines approach.
How Higher Education Search Behavior Is Changing
Students tend to begin with non-branded questions about programs, careers, cost, and fit, then use both traditional search engines and AI tools to compare their options. AI is already an established research channel for some audiences, with 48% of adult learners reporting that they used an AI search engine during their college search. This means institutions must be visible before students know which schools they want to consider, and their information must be clear enough for both search engines and AI platforms to understand and present accurately.
Zero-click search provides stronger evidence of this change. A SparkToro analysis using Similarweb data found that 68% of Google searches in the United States ended without a click. A strong ranking can still create visibility, but no longer guarantees that a prospective student will visit the page.
AI visibility is now part of higher education SEO. Institutions still need pages that rank, but they also need accurate, well-structured information that AI tools can find, summarize, and cite. For schools recruiting internationally, the same pattern appears across major global markets: zero-click rates range from 62.1% in Germany to 69.5% in the United Kingdom, showing that students are less likely to reach a university website directly from Google.
This broader view of discovery is also the basis of Manaferra’s IDO framework, which looks at how institutions appear across the information sources students use to research and compare colleges.
Higher Education SEO Benchmarks
The figures below are useful comparison points. Results will vary based on institution size, program mix, location, audience, and level of competition, so each benchmark should be read alongside your own historical performance and that of a relevant group of peer institutions. Where higher-ed-specific research is unavailable, the benchmark is clearly labeled as general or cross-industry data and should be used only as a starting point.
Share of voice
Share of voice is the percentage of available organic clicks across a tracked keyword set that goes to your domain, based on ranking position and search demand. It provides a cleaner competitive comparison than raw traffic because paid spend and the total size of each website do not enter the calculation.
A 20% share of voice is the parity point when an institution is compared with four competitors. The parity figure is simple arithmetic (not a research finding): when five institutions are competing for the same tracked searches, an equal share is 20%. A result above 20% means your institution is capturing more than its even share of the available clicks, while a lower result means competitors are taking more of that visibility.
A stronger reading comes from excess share of voice (ESOV), which compares visibility with current market share. In higher education, that means comparing your organic share of voice with your share of enrollment across the same group of institutions. LinkedIn’s B2B Institute found that 10% excess share of voice was associated with about 0.7 percentage points of annual market-share growth. Because this research comes from advertising and B2B markets, universities should use ESOV as a directional comparison, not as a prediction of enrollment growth.
For a university, this means that if your share of voice is below your enrollment share, competing schools are earning more search visibility than their current enrollment size would suggest. Run this analysis separately for branded and non-branded keywords. A keyword set dominated by the institution’s name can make visibility look stronger than it is, while non-branded searches provide a clearer view of whether new students can find your programs before they know your institution.
How to benchmark your institution: Build a program-focused keyword set in Ahrefs Rank Tracker, add three to five competitors, and review Share of Voice in the Competitors Overview. Calculate parity by dividing 100 by the total number of institutions, then use IPEDS enrollment data to calculate enrollment share across the same group and subtract it from your non-branded share of voice. A positive result means search visibility exceeds enrollment share; a negative result shows where competitors have the visibility advantage.
Branded vs. non-branded keywords
According to Manaferra’s research, 2 in 5 students begin their college search with no schools in mind. That creates a large discovery window for non-branded searches around programs, careers, cost, location, and other questions students ask before choosing an institution. Branded searches are tied to an institution or brand students already know, while non-branded searches help show how people discover a school without searching for its name directly.
Strong branded visibility helps universities reach students who are already familiar with the institution. Higher ed content marketing can expand non-branded visibility by answering the broader questions students search as they compare programs and schools. For universities, tracking both groups shows how much organic visibility comes from existing awareness and how much comes from new discovery.
How to benchmark your institution: Use Google Search Console’s branded queries filter to compare branded and non-branded impressions, clicks, and CTR. Track the split over time and by priority program to see whether your institution is expanding its visibility among students who are not already searching for your name.
Click-through rate (CTR)
Click-through rate can vary substantially even when ranking position stays the same. Data shows that the first organic result averages about 43.66% CTR on a search results page containing only organic listings. When AI Overviews and video results are present, position-one CTR falls to about 12.01%. These are general web figures, but they show why universities should consider the layout of the search results page when comparing CTR.
AI Overviews have made this comparison even more important. An Ahrefs study of 300,000 keywords found that the presence of an AI Overview was associated with a 58% lower CTR for the top-ranking page. A program page can therefore hold a strong organic position while receiving fewer clicks when Google answers more of the query directly in the search results.
How to benchmark your institution: Export queries, clicks, impressions, average CTR, and average position from Google Search Console, then compare queries with similar rankings and search-result layouts. Pay particular attention to top-ranking program pages with low CTR, and check whether AI Overviews, videos, or other search features appear for those queries before deciding that the title, description, or page itself is the problem.
Organic engagement rate
In Google Analytics 4 (GA4), an engaged session lasts longer than 10 seconds, includes a key event, or contains at least two page or screen views. No representative public higher-ed engagement-rate benchmark is currently available, so universities should evaluate this metric against their own historical performance and relevant peers. Google Analytics also provides peer benchmarking for engagement-related metrics, using comparison ranges such as the median, 25th percentile, and 75th percentile.
For higher-ed teams, engagement rate helps show whether visitors arriving from organic search are interacting with the content they find. A higher rate can be encouraging, but it should still be reviewed alongside inquiry and application activity, especially on program pages where student intent is stronger.
How to benchmark your institution: Filter Organic Search in GA4 and compare engagement by page type, such as program pages, articles, and application pages. Use Google’s peer benchmarks where available, then compare each page group with its own historical performance to identify meaningful changes.
Organic lead generation
Higher education landing pages have a 6.3% median conversion rate, based on Unbounce’s analysis of landing-page performance across traffic sources. This is not an organic-search-specific benchmark, so universities should use it as broad context. No reliable university-specific benchmark for organic visitor-to-lead conversion is currently published.
Lead generation is also only the beginning of the enrollment process. An inquiry has more value when the student continues to an application and ultimately enrolls, so universities should connect organic leads with later funnel outcomes wherever their data allows. For application-to-admission and admission-to-enrollment comparisons, IPEDS admissions data provides institution-reported acceptance rates and admission yields that can be compared with relevant peer schools.
How to benchmark your institution: Mark inquiry submissions and application starts as key events in GA4, then measure conversion rates for Organic Search by program or page type. Compare performance with your own historical results, and use IPEDS peer data to benchmark acceptance rates and enrollment yield later in the funnel.
Backlink quality
Domain Rating is measured on a scale from 0 to 100, but there is no single score that defines a strong higher education website. No representative higher-ed benchmark exists because university sites vary widely in age, size, reputation, and the number of colleges, programs, and resources housed under one domain. DR should therefore be treated as a relative measure of backlink strength.
Backlink quality is better assessed through Domain Rating (DR) and referring domains than through the total number of links alone. Referring domains count the unique websites linking to an institution, so several links from one website still count as one referring domain. This helps marketing teams better understand how widely the university has earned authority across credible third-party sources.
Ahrefs recommends judging DR against sites in the same competitive space. A DR of 50 may be strong when similar institutions are near 40, but weak when the main competitors are closer to 85. For higher education teams, a good result is one that compares favorably with the institutions competing for the same programs and students (not one that reaches an arbitrary number).
How to benchmark your institution: Compare your Domain Rating and number of referring domains with three to five relevant competitors in Ahrefs. Review the quality and relevance of the sites linking to each institution, instead of looking only at the total backlink count.
Core Web Vitals pass rate
About 48% of mobile pages and 56% of desktop pages pass all three Core Web Vitals. These are general web figures because no recent higher-ed-specific pass-rate study is available. The metrics cover loading speed, responsiveness, and visual stability based on real user experiences.
Largest Contentful Paint (LCP) had the lowest mobile pass rate at 62%, compared with 81% for visual stability and 77% for responsiveness. LCP measures how quickly the main content on a page becomes visible, making it a common performance issue to check first. For universities, slow program-page templates can create a poor experience for students trying to review requirements, costs, or deadlines.
How to benchmark your institution: Check mobile performance using Chrome User Experience Report (CrUX) data in PageSpeed Insights or the Core Web Vitals report in Google Search Console, then compare your pass rate with the 48% mobile reference point. Review program-page templates separately because they support high-intent visits and enrollment actions.
Structured data and FAQ/schema adoption
Structured data appears on about 51% of web pages, based on Web Data Commons analysis of nearly 2.4 billion pages. With no comparable higher-ed-specific adoption rate currently available, universities should use this figure for context rather than as an industry target.
For universities, adding schema is only useful if it is implemented correctly. Google recommends checking structured data with its Rich Results Test, since errors can affect eligibility for enhanced search results. The priority should be valid markup on key pages such as programs, articles, events, and FAQs.
Structured data may also help AI systems understand page content, although the evidence is still developing. In a small controlled Search Engine Land experiment, the page with well-implemented schema was the only one to appear in an AI Overview. The researchers were careful not to treat this as proof of causation, so schema is best viewed as a supporting signal for AI visibility, not a ranking factor on its own.
How to benchmark your institution: Start by measuring what percentage of your priority pages use structured data, with the 51% general-web figure as a reference point. Then run key program, article, event, and FAQ pages through Google’s Rich Results Test and fix errors so the markup is present and valid.
AI Search Benchmarks
AI visibility now has a direct consequence for college consideration. In 2026, 59% of undergraduates said they considered a school less when it did not appear in their AI search, up from 27% a year earlier. That means being absent from AI results can remove an institution from consideration before a student ever reaches its website.
For the first time, this performance can also be compared at scale. Manaferra’s AI Index analyzed 200,000 student prompts across more than 100 undergraduate and graduate programs and all 50 states, giving higher-ed teams a benchmark for how visible their institution is overall, by AI platform, and by individual program.
AI visibility
According to Manaferra’s 2026 State of College Search research, AI search influenced shortlist decisions for 82% of undergraduates who used it, compared with 63% for traditional search and college ranking sites. For universities, that means AI visibility plays a growing role in whether a school makes it onto a student’s list of serious options.
The AI Index Score measures how often a school is mentioned, how prominently it appears, and where it is placed across a fixed set of student prompts, with scores ranging from 0 to 100. Nationally, MIT ranks highest with a score of 100, followed by Stanford at 98, and Michigan, Harvard, and Carnegie Mellon at 96.
AI recommends schools from a student’s home state five times more often than schools outside that state, which makes state-level visibility a more useful comparison for many institutions. A university may sit well below the national leaders and still perform strongly against the schools students are most likely to see alongside it.
External presence also separates stronger performers from the rest. A typical school gets six of every 10 AI citations from its own website, while top-performing schools get only three of 10 from their own domain. This shows why visibility on credible third-party sites is an important part of improving how often AI includes and recommends an institution.
How to benchmark your institution: Check your AI index score using the Manaferra AI Index and compare both your national and state rankings, giving more weight to the schools competing with you in-state. Then review your citation mix. If more than six in 10 citations come from your own domain, your next priority should be building stronger visibility across trusted external sources.
AI visibility per channel
ChatGPT still has the largest audience, but Gemini is growing much faster. Bing Chat and Copilot remained at 11%, Claude reached 9%, and Perplexity rose from 4% to 5%.
These differences mean AI visibility should be weighted by where students actually search. Strong visibility on a platform used by 5% of students carries less weight than weak visibility on one used by 79%. And because 87% of undergraduates use more than one platform during college search, institutions need to look at performance across channels instead of treating AI visibility as a single score.
Google AI Overviews also need to be tracked because they can answer a student’s question directly at the top of the search results, before the traditional organic listings. In Manaferra’s higher-ed AI Overviews study, one tracked higher-ed keyword saw clicks fall 51% in the week after an AI Overview appeared, while impressions declined 34% and CTR fell 25%. This means a university can still rank well in Google but receive fewer visits if the AI Overview answers the query first, making inclusion in the AI Overview an important part of search visibility.
How to benchmark your institution: Test the same priority student questions in ChatGPT and Gemini first, since they account for the largest shares of student AI use, and record whether your institution appears and where. Use the Manaferra AI Index as your primary benchmark for overall AI visibility, competitor position, and how your institution performs across the major AI platforms.
AI share of voice per program
An institution-wide AI visibility score can hide major differences between individual programs. A university may appear frequently when students ask about one field but rarely when they search for another, and the schools AI recommends alongside it can change by program. For higher-ed marketers, that means each academic program needs to be evaluated within its own competitive market.
The Manaferra AI Index helps make those differences visible by measuring more than 100 undergraduate and graduate programs separately. Higher-ed teams can see how their institution performs for specific programs and identify the schools appearing alongside them in AI results. This provides a more useful benchmark for program-level decisions than relying only on the institution’s overall AI visibility.
A Manaferra analysis of Western Washington University shows how program-level visibility can vary. Its Business program appeared in 40% of tested AI queries, while ChatGPT frequently recommended the University of Washington’s Foster School of Business and Washington State University’s Carson College of Business instead. Western Washington’s Computer Science program appeared in only 30% of queries, showing that visibility and competition can change significantly from one program to another.
How to benchmark your institution: Check each program separately in the Manaferra AI Index and compare your visibility with the schools AI recommends alongside you. If AI regularly names five schools for that program, parity is 20%, meaning your institution should appear in roughly one of every five answers to match an even share of visibility.
AI traffic
No reliable higher-ed-specific benchmark for AI referral traffic is currently available, so institutions should establish their own baseline and track how the channel changes over time. AI referrals still account for a relatively small share of website traffic compared with organic search, but the channel has grown rapidly as the use of AI platforms has expanded.
In June 2025, AI platforms generated more than 1.1 billion referral visits to the top 1,000 websites, up 357% year over year. ChatGPT accounted for more than 80% of those referrals, giving it a much larger role in AI-driven website traffic than other platforms. For higher-ed teams, this makes ChatGPT an important channel to track even while total AI referral volume remains relatively low.
How to benchmark your institution: Build a Google Analytics 4 segment for referrals from ChatGPT, Gemini, Perplexity, Copilot, and other AI platforms. Because no higher-ed traffic benchmark exists yet, compare your AI referral share with the cross-industry range while giving more weight to your own month-over-month growth.
Measure Higher Ed Visibility Across Search and AI
Higher education SEO can no longer be judged by rankings alone. Student discovery now happens across Google, AI platforms, third-party sites, and individual program searches, which means an institution can rank well and still lose visibility at important points in the decision process. The strongest benchmark is therefore not a single traffic or ranking number, but how your institution performs against relevant competitors across the channels students actually use.
Use the Manaferra AI Index to check your institution’s score, state position, program-level visibility, and competitor set, then refer to that baseline to see where competitors are more visible and where your institution should focus next. AI search influenced college shortlists for 82% of undergraduates who used it, making AI visibility an important part of how institutions are compared today.
Frequently Asked Questions (FAQs)
Where can I check the AI visibility of my school?
Use the Manaferra AI Index to see your institution’s AI visibility score, national and state position, program-level performance, and competitors.
How do I know which programs AI is recommending my school for?
Check your institution in the Manaferra AI Index and review visibility program by program. Each program is measured separately because AI may recommend your school more often for some subjects than others.
How do I find out which schools I compete with in AI search?
Compare the institutions AI recommends alongside yours for each program in the Manaferra AI Index. Your AI competitors may differ from the schools you consider traditional competitors.
What is a good organic CTR for a university website in 2026?
There is no single higher-ed CTR benchmark because performance depends heavily on ranking position and search features. As a general reference, position one averages about 43.66% CTR when only organic results are present, but CTR can drop when AI Overviews, videos, and other search features appear.
What organic traffic should a university program page expect?
There is no universal traffic benchmark for program pages because demand varies by program, location, season, and search volume. Compare each page with its own historical performance, relevant competitor pages, and the search demand for its target topics.
