SEO metrics in the age of AI: KPIs in SERP and LLMs

If SEO metrics used to be "just" about ranking and gaining organic traffic, now they have also become a discussion about presence in ready-made answers, fewer clicks and more influence on the path to enrollment.

For performance teams at universities and educational institutions, this can feel like chasing a moving target: you optimize, rank higher, but part of the search ends up in an AI-generated summary.

The good news is that you can measure, compare and make methodical decisions. What changes is the set of signals.

In addition to the classic SERP, this includes SEO for AI, SEO for LLM (Large Language Models), visibility in AI Overviews, citations and impact on the funnel.

What you'll see in the post

In order not to become a theoretical compilation, this guide is designed so that you leave with an applicable measurement model. You'll see:

• Which classic metrics remain indispensable in organic search
• What changes when the answer is ready in AI Overviews and answer engines
• How to define citation and visibility metrics in AI answers, without inventing "magic"
• How to instrument collection with GSC, GA4, tracking and CRM
• A practical unified dashboard model for SEO + AI + funnel
• How to connect metrics to the sales funnel in educational marketing
• An implementation checklist for on page, tracking and routine

SEO metrics, in the age of AIs, need to measure performance on the SERP and also visibility and influence on responses generated by LLMs, connecting everything to the funnel.

In practice, this means maintaining classic KPIs such as position, click-through rate and organic traffic, while adding indicators of presence in AI Overviews and answer engines (citations, mentions, share of voice in prompts and assisted visits).

The decisive step is to unify the sources: Google Search Console for search performance, GA4 (Google Analytics 4) for behavior and conversions, tracking to identify origin and a CRM (Customer Relationship Management) for attribution down to opportunity and enrollment.

Image: Illustration representing the analysis of SEO KPIs, SERP visibility and presence in AI-generated responses.

Classic SEO metrics that still apply to organic search

Before talking about LLMs, it's worth clearing up a common doubt: AI Overviews and AI resources don't "retire" search. What they do is redistribute attention.

The basis of an SEO strategy continues to be relevance, experience and the search engine's ability to understand your content, which is aligned with Google Search Essentials, which brings together technical requirements, policies and good practices for content to be eligible and perform in Search.

If you need a quick starting point with traditional KPIs, the list of SEO indicators that your university can't afford not to monitor provides a great overview. Here, I'll organize them by decision: what to look at, why it matters and how to interpret it.

Visibility: average position, impressions and participation by topic

• Average position: useful for trends, bad for "absolute truth". In educational clusters, the average can hide the fact that different pages are vying for the same intent.
• Impressions: your demand and eligibility thermometer. If impressions drop, it could be seasonality, a change in intent or a loss of coverage.
• Participation by cluster: instead of just isolated keywords, group them by theme. For example: "entrance exam + course X", "tuition + course X", "curriculum + course X".

This is where SEO Ops comes in: mapping clusters, page owners and revision cadence. If you don't already have this in place, the SEO Ops approach helps turn SEO into an operable routine, not an endless project.

Attractiveness: click rate, CTR and changes per feature

Click-through rate is the most underestimated signal in Educational Institutions because it speaks directly to intent and value proposition. When the SERP becomes more "crowded" (PAA, maps, videos, AI Overviews), you compete for attention, not just position.

• Click-through rate (CTR): use the acronym CTR once to standardize the language with the media and BI team, but keep click-through rate as the main term.
• CTR by device: courses and postgraduate courses tend to behave differently on mobile.
• CTR by type of query: informational (e.g. "what is Enem"), comparative (e.g. "best college for...") and transactional (e.g. "register for course X").

When the click-through rate drops and the position is maintained, the most common hypothesis is a change of SERP feature.

The way forward is to look at the query cut-off and compare periods with notes on changes (title change, snippet change, growth of AI Overview in the query, etc.).

Organic traffic: sessions, landing pages and user quality

Organic traffic alone is vanity if you don't understand what that user does afterwards. For educational marketing, a good dashboard includes:

• Organic sessions per landing: which pages really attract demand.
• Engagement (in GA4): time engaged and key events (scroll, click on CTA, start registration, simulate tuition).
• Navigation by path: can the user who enters "course X" move on to "curriculum matrix", "faculty" and "enrollment"?

This is where content marketing and UX meet. If your content architecture isn't conducive, SEO becomes traffic that evaporates. Thinking about how to do content marketing helps to structure pages and interconnections with funnel intent.

Technical expertise: Core Web Vitals and indexing health

In performance, there is an "invisible cost" when the site is slow, unstable or difficult to crawl. This doesn't always bring everything down at once, sometimes it just stops you from scaling.

• Core Web Vitals: use as an indicator of risk and backlog priority, not as a trophy. The most objective reference for aligning expectation with ranking and evaluation is the page experience guide , which explains how Google treats page experience and how to evaluate its main signals.
• Coverage and indexation (GSC): important pages being left out of the index = silent damage.
• Logs and crawling: for large sites (many course pages, centers, units), log analysis is the shortcut to understanding bottlenecks.

Authority: backlinks, mentions and thematic reputation

Backlinks still count, but for educational institutions, the "authority that matters" is thematic and contextual, where a relevant mention on an educational portal can be worth more than dozens of generic links.

• Backlinks by context: evaluate pages and domains that cite courses, research, projects and teachers.
• Mentions without a link: useful for reputation, even without directly gaining PageRank.
• Entities and consistency: course name, campus, recognition and consistent institutional data increase reliability.

SEO metrics for AI and SEO for LLMs: AI Overviews and answer engines

The change that disrupts the routine is simple: part of the searches now have an overview layer, and the answer may appear before the click.

If your team uses AI to speed up drafts or searches, it makes a difference to know Google's guidance on using content with generative AI, because the rule is still quality, usefulness and trust, not the production method.

Google describes, in AI Resources and your site, that experiences such as AI Overviews and AI Mode use advanced models to generate answers with supporting links, and that the guidance for appearing in them follows the good SEO practices already known, changing the way of measuring, because your result can be influence, not visits.

If you want to understand the context and how this behavior affects content decisions, it's worth looking at what changes in ChatGPT vs Google and in the overview of the future of SEO with AI and LLM.

Metric 1: presence in AI Overviews (visibility and coverage)

What it is: frequency with which your pages appear as a supporting link, cited source or related reference within an overview created by AI.

How to measure (approximate):

1. Define a fixed set of priority queries (by course and by intent).
2. Run standard weekly manual checks (same geography, clean browser, evidence log).
3. When available, use SERP feature monitoring tools to mark "AI Overview present" and "domain cited".

Limitations: AI Overviews vary by user, location, language and moment. So treat it as a sample, not a census.

To delve deeper into the concept and how it appears in the SERP, the practical reference is AI-created overviews.

Metric 2: citations and mentions in answer engines

What it is: how many times your Educational Institution is cited as a source in answers from LLM-based tools, for example, wizards and conversational search modes.

How to measure (good practice):

• Create a "bank of prompts" with real questions (e.g. "what is the difference between a technologist and a bachelor?", "how does FIES work for course X?").
• Rotate every 15 days and record: if you quoted, which page, which passage and in what context.
• Separate by intention: informational, comparative and decision-making.

Possible tools: spreadsheet + manual collection works at first; later, you can automate with scripts and monitoring, as long as you respect the limits of use and variability.

Limitations: answers can change; a lack of citation doesn't mean "worse", it could just be a variation in the source.

To make this evaluation less subjective, a practical approach is to run a "readiness for answer engines" diagnosis.

Tools such as the HubSpot AEO Grader help review signals that increase the chance of a page being understood and used as a reference by LLMs (answer clarity, structure, scannability and trust elements) .

Use the result as a checklist for improvement, not as a final grade: it guides priorities, but does not replace SERP monitoring, prompt auditing and funnel reading.

This point connects directly with SEO for LLM: clear, well-structured content with FAQs, tables and definitions tends to be more "citable".

The full logic lies in SEO for LLM and how to structure it to appear as a source on how to do SEO and be cited by IAs.

Metric 3: share of voice in prompts

What it is: percentage of times your institution appears in responses, within a fixed set of defined prompts and competitors.

How to measure (approximate):

1. Select 30 to 60 critical prompts per area (undergraduate, postgraduate, distance learning, scholarships).
2. Define "reference competitors" (3 to 5 educational institutions) and a counting criterion (direct mention of the institution, link, recommendation).
3. Assign a score: 2 for citation/link, 1 for mention without link, 0 for absence.
4. Add up by period and compare trends.

Limitations: not an official platform indicator. It serves as a relative presence thermometer, not as an auditable metric.

Metric 4: AI-assisted traffic and conversion

Not everything will turn into a direct click, but part of the user arrives "hotter" after consuming an answer. Here, the question changes: "which journeys were influenced by organic content, even if it wasn't the last click?"

• Assisted organic sessions: path-based attribution (GA4) and, where possible, CRM.
• Assisted conversions: events that occur after a previous organic visit.
• Lead quality: MQL rate, SQL and enrollment by first visit channel.

This ties in with the discussion of the new sales funnel for SEO and LLMs, because AI may capture attention at the top, but the proof and decision still come through your website.

How to measure: data sources and instrumentation (GSC, GA4, tracking, CRM)

If you try to measure AI and SEO with a loose spreadsheet, it will work for two weeks, then it becomes noise. The sustainable path is minimum viable instrumentation, with standards.

Google Search Console: what it delivers and where it stops

GSC is the "organic search thermometer": impressions, clicks, position and queries.

Google itself details how these figures are calculated and displayed in the Search Console performance report, which helps to avoid erroneous comparisons between periods and filters.

It helps to separate demand problems (impressions) from attractiveness problems (click-through rate) and ranking problems (position). It does not measure AI Overviews as a dedicated report.

Therefore, AI reading needs to be combined with SERP feature monitoring and sample auditing.

GA4: behavior, events and conversions

In GA4, the main thing is to standardize events and conversions. In order not to reinvent names and make reporting easier, it's worth using GA4's recommended events as a basis when it makes sense for your journey.

For Educational Institutions, the basis is usually:

• Viewing the course page.
• Click on Enrollment CTA.
• Start form.
• Submit form.
• Simulate tuition/scholarship (if available).
• Contact via WhatsApp or telephone.

The common mistake is to measure "conversion" only as form submissions and ignore micro-conversions that explain bottlenecks.

Tracking: how not to get lost in UTMs and attribution

For paid campaigns, UTMs are obvious. For organic, tracking comes in another form: standardization of URLs, parameters when necessary, consistent events and identification of key pages.

A useful practice is to create a content taxonomy: cluster, intent, funnel stage and "primary CTA", as this allows you to cross SEO with performance and with inbound marketing without arguing semantics every week.

CRM: the moment SEO becomes revenue

When the team asks "Is SEO generating enrollment?", the answer can only be trusted in the CRM. And in order not to measure each stage with a different concept, it's worth standardizing terms and models with the HubSpot attribution settings.

What you need as a minimum:

• First touch source.
• Lead source.
• Conversion page.
• Course/unit/pole of interest.
• Stage in the funnel (MQL, SQL, opportunity, enrollment).

An SEO consultancy that works with educational performance usually comes in here: align taxonomy, attribution rules and audit routines to avoid "generic SEO = organic" without an owner.

Unified dashboard: practical model of dashboards and routines

A good dashboard isn't the one with the most graphs, it's the one that answers questions without you having to open five tabs. For performance teams, I like to separate them into four dashboards: Health, Demand, Visibility and Funnel.

Before looking at the table, an important note: organizing by questions avoids internal conflicts. Each dashboard should have 3 to 6 KPIs, with targets or ranges and an action routine.

Table 01: Unified dashboard: practical model of dashboards and routines

What this table makes clear is this: you don't "measure AI" with GA4 alone, and you don't "measure revenue" with GSC. The value of the dashboard lies in putting pieces together with consistency.

Which SEO and AI routines should you follow to measure and improve results?

• Weekly (30-60 min): variation in impressions and position by cluster, CTR drops, pages losing organic traffic.
• Fortnightly (60-90 min): AI Overview audit and prompt bank, citation check and SERP feature changes.
• Monthly (90-120 min): cross-referencing with CRM: MQL, SQL and organic opportunities, with analysis of assisted pages.

If you want to reduce improvisation and gain predictability, predictive SEO's process logic helps you treat SEO as a pipeline, not an "art".

How to link SEO metrics to the sales funnel (inbound marketing) in educational marketing

In higher education, the journey is rarely "search, click, enroll". It involves comparison, social proof, financial doubts, real anxiety and often deadline pressure.

This is where classic metrics and AI metrics need to talk to the sales funnel.

Matrix of KPIs by sales funnel for universities

For performance teams in educational marketing, the matrix needs to be didactic enough to become routine and rigorous enough not to become a "pretty dashboard".

The idea is to separate what measures SERP and organic search, what measures visibility in AI, and what measures impact on the funnel.

Below you'll find the full version, with interpretation and cautions. Note that some new metrics, such as share of voice in prompts, are useful, but are not auditable as GSC. They come in as a presence thermometer, not as "final proof".

Awareness

At this stage, the aim is to cover demand and gain presence when the student is still forming criteria. The ideal is to look at clusters and intentions, not single words.

Table 02: Visibility and discovery KPIs (Top of Funnel)

What to do with it: if impressions rise and position falls, it could be expansion of coverage to broader terms. If impressions fall, investigate intent and SERP changes. If AI Overviews increase in a cluster, expect a change in click-through rate and adjust focus to quality and citability.

Consideration

Here, the student compares, validates and tries to reduce risk. Behavioral metrics (GA4) need to be read together with attractiveness (GSC) so you don't blame the page when the problem is "it didn't promise right on the SERP".

Table 03: Engagement and intent KPIs (Mid-Funnel)

What to do about it: if CTR drops and position doesn't change, investigate feature change and adjust title, snippet and tagging.

If engagement drops, review scannability, proof and next steps, but if citations fluctuate, treat it as a trend and correct the content to make it more accurate and "citable".

Conversion

Ultimately, the conversation is about CRM, not impressions. SEO becomes part of the pipeline: it generates leads, qualifies them, takes them to the next stage and, at some point, converts them into enrolments.

Table 04: Conversion and revenue KPIs (Bottom of Funnel)

What to do about it: if leads go up and enrollments don't, the bottleneck could be in qualification, offer or journey.

And if organic appears a lot as assisted and little as last click, great: it's educating and reducing friction, but you need to make sure the CRM records the first touch and the page path.

With this matrix in hand, it's easier to answer where to act: expand coverage (top), reduce friction and objections (middle), or improve qualification and attribution (bottom).

At the top: visibility that generates recall

At the top, your goal is to appear when the student is still forming criteria. This is where you come in:

• Impressions and coverage by theme.
• Presence in AI Overviews (if any).
• Share of voice in informational prompts.

Be careful not to confuse top with "any traffic". You want real educational intent, not empty curiosity.

In the middle: content that solves objections

In the middle, the student is comparing. Useful metrics:

• Pages per session and most common paths.
• Clicks on secondary CTAs (curriculum matrix, coordinator, differentials).
• Conversion rate per course landing.

Here, on-page SEO stops being "title and H1" and becomes experience: clarity, scannability and proof.

At the end of the day: assignment to opportunity

The bottom line is:

• Lead to MQL.
• MQL to SQL.
• SQL to enrollment.

If SEO isn't appearing at these stages, it's usually not because of a lack of traffic, but because of a lack of connection between content, offer and CRM.

For teams that are recalibrating their organic positioning, the guide on how to increase visibility on Google helps to align content, authority and prioritization.

How to implement and measure SEO in practice? On-page checklist, tracking and routine

You don't have to do everything at once, but do the basics well so as not to measure "noise".

Before the checklist, a reminder: order matters. Instrumentation comes before volume.

• Update headings and H1 by intent (course, price, registration, schedule).
• Structure short, direct answers at the beginning of important sections.
• Use real FAQs (without making up questions) for recurring doubts.

• Ensure indexing of critical pages and correct cannibalization.
• Prioritize CWV on the pages that generate the most leads.
• Maintain a coherent sitemap and internal architecture.

• Standardize events in GA4 for micro and macro conversions.
• Create clusters and label content (theme, intent, funnel stage).
• Set up a bank of prompts and a fortnightly audit routine.

• Define ownership by panel (Health, Demand, Visibility, Funnel).
• Hold short, decisive meetings, with actions recorded.
• Review what goes into the backlog based on expected impact, not opinion.

If you notice that the team is stalling on prioritizing and integrating data, it's a sign that it's worth structuring a service or diagnostic page. For example, SEO consultancy is a direct way to talk to the team.

What are the main doubts about SEO metrics in the age of LLMs?

What are SEO metrics in the age of LLMs?

They are indicators that measure performance in organic search and also visibility and influence in AI-generated responses, connecting this to the funnel.

Do AI Overviews "kill" organic traffic?

Not necessarily. They can reduce clicks on some queries and redistribute traffic to others. The impact varies by intent and by SERP.

How to measure presence in AI Overviews if there is no official report?

With sampling: fixed list of queries, standardized periodic checks and, where possible, monitoring of SERP features to mark occurrences.

What is share of voice in prompts?

It's your institution's participation in AI responses within a fixed set of prompts. It measures relative presence, not an "official" metric.

Is SEO for AI different from traditional SEO?

The basis is the same: useful, crawlable content. The difference is that you optimize the structure and clarity more to be understood and cited by models.

Which data sources are mandatory for a unified dashboard?

At the very least: Google Search Console (search), GA4 (behavior and conversion), tracking/events and CRM (opportunity and enrollment).

Can you accurately attribute enrollment to SEO?

You can greatly increase accuracy with CRM and attribution models. Even so, there will always be limitations due to multi-touch and journey variation.

What is the safest first step for performance teams in educational institutions?

Standardize events and conversions in GA4, organize clusters in GSC and create a simple weekly and fortnightly analysis routine.

How do you know if your SEO metrics are working in the age of LLMs?

The LLM era doesn't ask you to abandon classic SEO metrics. It asks you to stop measuring SEO as if the SERP were just "10 blue links".

For universities and educational institutions, the focus is on: maintaining a technical base and strong content to rank, learning to measure presence and citations in AI responses and, above all, connecting everything to the sales funnel with instrumentation and CRM.

When your dashboard answers "where do we gain visibility", "where do we lose attention" and "where does this turn into leads and enrollment", SEO stops being a feeling discussion and becomes a predictable operation.

If you want to turn what you've learned in this post into real on-page improvement, the guide "How to apply search intent optimization in SEO?" is the next step.

It provides a step-by-step guide to identifying the intent behind queries in Search Console, organizing pages by search type (informational, comparative and decision) and adjusting what changes on the page (title, answer snippets, modules and CTA) to consistently increase relevance and conversion.