Classic SEO KPIs stop at rankings and clicks.

AI search adds citations, answer quality, and zero-click impact.

Here is the direct answer up front: group your AI SEO metrics into exposure, quality, trust, and impact; run weekly prompt panels; log citations and sentiment; and tie changes to leads and revenue.

This guide gives you definitions, dashboards, sampling methods, and reporting cadences.

Keep our AI Search Ranking Factors guide open as the backbone while you implement.

Introduction: the gap in measurement

AI Overviews, Perplexity, ChatGPT Search, and Bing Copilot influence buyers without sending clicks.

You need metrics that show if you’re present, what is being said, and whether it drives business.

This playbook defines a practical taxonomy, shows how to collect data ethically, and explains how to blend it with traditional SEO and CRM metrics.

It matters because without it you can’t prioritize AISO work or prove ROI.

AI SEO metric taxonomy

Exposure

  • Inclusion rate: % of prompts where your domain appears.

  • Citation share: Share of citations vs competitors for a prompt set.

  • Engine diversity: Number of engines where you appear (AIO, Perplexity, Copilot, ChatGPT Search).

  • Prompt coverage: % of your prompt library tested weekly.

Quality

  • Accuracy score: % of prompts with correct facts about your brand.

  • Sentiment: Positive/neutral/negative in AI answers.

  • Recommendation rank: Position or prominence when assistants list options.

  • Citation depth: Number of unique URLs cited per prompt.

Trust

  • Entity consistency: SameAs completeness and naming alignment across pages/locales.

  • Schema health: Validation pass rate and error counts by template.

  • E-E-A-T signals: Bios, sources, update cadence on cited pages.

  • External authority: Mentions/reviews volume and freshness.

Impact

  • Branded lift: Change in branded and entity queries after visibility shifts.

  • Assistant referrals: Sessions from AI browsers/Edge/direct spikes after citations.

  • Conversions on cited pages: Leads/revenue tied to URLs cited in AI answers.

  • Assisted conversions: Deals influenced by periods of higher AI visibility.

Building your prompt library

  • By persona: Buyer, practitioner, exec.
  • By funnel stage: Awareness (“what is”), consideration (“best”, “vs”), decision (pricing, security), post-sale (troubleshooting).
  • By market/language: EN/PT/FR prompts with local phrasing and currency.
  • By product/feature: Integrations, pricing, compliance.
  • By risk: Topics prone to inaccuracies (legal, medical, pricing, security).

Version the library and refresh monthly based on sales/support questions and campaigns.

Data collection methods

  • Manual or scripted prompt panels across engines; capture screenshots, cited URLs, and wording.
  • Allowed APIs or exporters where available; respect terms and privacy.
  • Server logs to spot AI user-agents (PerplexityBot, BingBot, GPTBot for some surfaces) and monitor crawl health.
  • Analytics segments for assistant referrals, Edge sessions, and direct spikes after citations.
  • Schema and performance monitors to connect technical health with visibility shifts.
  • Brand mention trackers for authority signals that often precede citation changes.

Handling non-determinism

  • Run prompts at least twice per panel; report medians.
  • Track volatility: % of prompts with changing citations week over week.
  • Use confidence bands: ignore swings under a set threshold (e.g., ±5 points) unless accuracy is wrong.
  • Tag engine/model versions when known; correlate shifts with releases.
  • Keep raw logs/screenshots for audit and trend validation.

Dashboards that matter

  • Executive: Inclusion rate, citation share on revenue topics, accuracy issues, branded lift, conversions on cited pages.
  • SEO/Content: Citation share by cluster and locale, top missed prompts, most cited URLs, schema errors on cited pages.
  • Ops/Engineering: Crawl errors, performance metrics, schema validation status alongside citation trends.

Use filters for engine, locale, cluster, funnel stage, and date. Start with sheets; scale to BI when stable.

KPI definitions and targets (starter)

  • Inclusion rate: start baseline; aim +10–20 points on priority clusters in 90 days.
  • Citation share: target parity with top competitor within a quarter on revenue prompts.
  • Accuracy: >95% correct for brand prompts; 100% for pricing and compliance.
  • Sentiment: >70% neutral/positive; zero negative on risk topics.
  • Assistant referrals: track trend; seek correlation with citation gains.
  • Conversions on cited pages: measure uplift after rewrites/schema fixes; target +10–15% conversion rate where applicable.
  • Time to correction: <14 days from detection to verified fix in AI answers.

Reporting cadence

  • Weekly: prompt panel outcomes, new inaccuracies, critical schema errors, quick wins.
  • Monthly: trend lines for inclusion/share, sentiment, accuracy, top cited pages, and conversions on cited URLs.
  • Quarterly: ROI narrative linking releases to visibility and revenue; roadmap adjustments and budget asks.

Example dashboard layout

  • Top row: Inclusion rate by engine, citation share for revenue clusters, accuracy issues open/closed.
  • Middle: Trends by engine/locale; stacked citations per cluster; sentiment bars.
  • Bottom: Top cited URLs with engagement and conversions; schema error counts by template; assistant referral trends.
  • Sidebar: Change log highlights and upcoming experiments.

Attribute AI SEO to revenue

  • Tag cited URLs; compare conversion rates before/after AI visibility changes.
  • Use assisted conversion models to credit journeys that began with AI exposure and ended in direct/brand searches.
  • Align releases (schema, content, speed) with timeline of citation shifts; annotate dashboards.
  • Pair AI visibility with campaign calendars to isolate effects.
  • Track demo/lead quality; AI citations often bring higher intent—monitor close rates and deal size.

Integrating with classic SEO metrics

  • Keep rankings, impressions, and CTR to see if AI visibility correlates with SERP changes.
  • Use GSC/Bing data for crawl/index health and hreflang correctness.
  • Map internal links and topic clusters to citation performance; adjust linking for underperforming clusters.
  • Monitor Core Web Vitals; poor performance can reduce inclusion eligibility.

Entity and schema metrics

  • Entity consistency score: % of pages with correct sameAs and consistent naming.
  • Schema coverage: % of templates with required/recommended fields and zero errors.
  • About/mentions usage: count per article for key entities to ensure clarity.
  • Logo and author validation: rate of 200 responses for referenced assets and bios.
  • Freshness: share of priority pages updated in last 45 days with aligned dateModified.

Experiment design

  • Hypothesis: moving comparison tables above the fold raises AI citations for “vs” prompts.
  • Plan: select 10 URLs, update layouts, validate schema, log change.
  • Measure: citation share delta after 2 and 4 weeks; engagement metrics; accuracy checks.
  • Decide: keep, roll back, or adjust. Document in changelog.

Alerts to set

  • Inclusion drop >10 points on revenue prompts in any engine.
  • New inaccuracies for pricing/compliance/security.
  • Spike in 4xx/5xx on cited pages or schema error counts.
  • Wrong-language citations (hreflang/locale issue).
  • Competitor overtakes share on a key “best” or “vs” prompt.

Tooling suggestions (agnostic)

  • Prompt logging: spreadsheets or lightweight databases with screenshots.
  • BI: Looker/Power BI/Data Studio for blending AI and web data.
  • Validators: Rich Results Test, Schema Markup Validator, custom linting in CI.
  • Performance: Lighthouse CI, WebPageTest, RUM.
  • Log analysis: BigQuery/ClickHouse + dashboards to monitor AI user-agents.
  • Brand monitoring: alerts for mentions to correlate with trust signals.

Stay within platform terms; avoid scraping that violates policies.

Multilingual AI SEO metrics

  • Separate prompt panels per locale (EN/PT/FR); do not mix data.
  • Track inclusion, share, and accuracy per language; watch for wrong-URL citations.
  • Align hreflang, canonicals, and localized schema; monitor local branded queries.
  • Report by market; local teams need their own dashboards and prompts.
  • Collect local reviews and mentions; tie sentiment shifts to citation changes.

Roles and ownership

  • AI SEO lead: Defines metrics, prompt sets, and prioritization; owns reporting.
  • Content lead: Ensures pages are answer-first, updated, and sourced; addresses inaccuracies.
  • Developer: Maintains schema, performance, and crawl health; fixes errors fast.
  • Analytics: Builds dashboards, alerts, and attribution models; keeps changelog.
  • PR/Comms: Drives mentions and reviews; responds to negative sentiment.
  • Legal/Compliance: Approves policies and sensitive topics; sets risk thresholds.

Governance and documentation

  • Changelog linking each release (schema, content, performance) to date and owner.
  • Prompt library with version history and tags (intent, persona, locale).
  • SOPs for panel runs, accuracy review, and incident response.
  • Quarterly review of taxonomy and KPIs; adjust as engines evolve.
  • Data retention and access policies aligned with GDPR/AI Act.

Case-style examples (anonymized)

  • B2B SaaS: Adding answer-first intros, FAQ schema, and entity cleanup on 15 pages raised citation share in Perplexity from 9% to 24% and increased demo requests on cited URLs by 12%.
  • Ecommerce: Daily price/availability updates plus Product schema cut pricing inaccuracies in ChatGPT to near zero and restored AI Overview inclusion for three categories.
  • Local services: LocalBusiness schema, fresh reviews, and local FAQs shifted Copilot citations from directories to the brand site within two weeks; call bookings from cited pages rose 15%.

Example metrics by funnel stage

  • Awareness: Inclusion rate, sentiment, and brand mentions in AI answers.
  • Consideration: Citation share on “best” and “vs” prompts; recommendation rank.
  • Decision: Accuracy of pricing/policies; conversions on cited product/demo pages.
  • Post-sale: Inclusion on support prompts; reduction in support tickets tied to clearer answers.

Reporting narrative for leadership

  • Lead with business outcomes (leads/revenue from cited pages, branded lift), then show the metric drivers (citation share, accuracy fixes, schema coverage).
  • Highlight risks: inaccuracies on pricing/compliance, wrong-language citations, or loss of inclusion.
  • Present next actions with expected impact and effort; tie to backlog and budget.
  • Show efficiency gains: hours saved using prompt libraries, faster time-to-fix for schema issues.

Combining AI SEO metrics with experiments

  • Link every experiment to a metric goal (e.g., +10 points citation share, -50% inaccuracies).
  • Set success thresholds and stop conditions before launching.
  • Run small batches; avoid global changes without evidence.
  • Share learnings in a central playbook so wins replicate across clusters and locales.

Risks and compliance

  • Respect platform terms and privacy when collecting data; avoid storing sensitive info from prompts.
  • For YMYL topics, keep expert review and disclaimers; measure accuracy more frequently.
  • Avoid dark patterns; keep answers honest and sourced to reduce brand risk in AI citations.

Benchmarking and targets by maturity

  • Early stage: Track 20–30 prompts; aim for inclusion on at least 30% of revenue topics. Fix obvious inaccuracies and schema errors; report monthly.
  • Mid stage: 50–100 prompts across locales; target 50–60% inclusion and growing citation share vs top competitors. Add sentiment and assistant referrals; weekly reporting.
  • Advanced: 150+ prompts segmented by persona and funnel; target 70%+ inclusion on core clusters, >95% accuracy, and measurable conversion lift on cited pages. Automate data pulls; tie KPIs to OKRs and budgets.

Metric glossary (copy/paste)

  • AI Overview inclusion rate: % of prompts where your domain is cited in Google AI Overviews.
  • Recommendation rank: Your position when assistants list options (e.g., 1st, 2nd in a bulleted list).
  • Citation density: Average number of your URLs cited per prompt.
  • Answer freshness: % of cited pages updated within 45 days.
  • Entity resolution score: % of prompts where assistants correctly identify your brand/products vs competitors.
  • AI sentiment score: Weighted sentiment of assistant outputs mentioning your brand.
  • Assistant referral sessions: Sessions attributed to assistant browsers or distinct referral patterns after citations.
  • Time to correction: Days between finding an inaccuracy and seeing a corrected answer.
  • Schema integrity rate: % of pages passing validation with no critical errors.
  • Long-tail coverage: % of niche prompts where you appear (tracks depth beyond head terms).

Combining qualitative and quantitative signals

  • Pair citation share with excerpts to see how you’re framed.
  • Add reviewer comments for YMYL topics to capture nuance (e.g., “answer omits disclaimer”).
  • Record competitive mentions to understand context (are you cited with the right peers?).
  • Capture follow-up suggestions from assistants; they hint at missing content or FAQs to add.

Governance for data quality

  • Define owners for prompt library, data pulls, QA, and reporting.
  • Use templates for screenshot naming and storage; keep a consistent folder structure by date/engine.
  • Run monthly data audits: random sample checks to confirm logs match screenshots and URLs.
  • Document any manual steps; automate where possible to reduce error.
  • Keep access scoped; AI logs may contain brand-sensitive phrasing or competitive insights.

Example leadership slide outline

  1. Goal: Increase AI citations on revenue topics; reduce inaccuracies.
  2. What we did: Answer-first rewrites, schema fixes, prompt panel expansion.
  3. Results: +18 points inclusion in Perplexity, +12 points citation share in Copilot; +9% conversions on cited pages.
  4. Risks addressed: Pricing inaccuracies resolved; wrong-language citations fixed.
  5. Next steps: Expand prompts to PT/FR, A/B test table placement, secure new mentions.
  6. Ask: Resource for schema automation and prompt logging.

Automating parts of the workflow (safely)

  • Use scripts to run prompts and capture HTML/text outputs where allowed; keep human review for accuracy.
  • Automate schema validation in CI; fail builds on critical errors.
  • Trigger alerts from dashboards when thresholds are crossed.
  • Use AI to draft report summaries; verify numbers manually before sharing.

Example changelog entries

  • “2025-03-10: Added FAQ schema to 12 feature pages; updated leads and tables; expected citation gain for ‘best’ prompts.”
  • “2025-03-17: Fixed hreflang on PT pages; wrong-language citations resolved in Copilot.”
  • “2025-03-24: Updated pricing and dateModified on product pages; reduced ChatGPT pricing inaccuracies to zero in retests.”

Connecting AI SEO metrics to backlog

  • Rank backlog items by expected metric lift (citation share, accuracy, sentiment) and effort.
  • Include a metric hypothesis in every ticket: “Add comparison table to target +5 points citation share on ‘vs’ prompts.”
  • After release, log actual impact; adjust scoring models to improve forecasting.

Vertical-specific metrics

  • B2B SaaS: Track integration-related prompts, security/compliance accuracy, and demo conversions on cited URLs.
  • Ecommerce: Monitor price/availability accuracy, product vs category prompt inclusion, and add-to-cart from cited pages.
  • Local services: Track “near me” prompts, citation share vs directories, and call form submissions.
  • Healthcare/Finance: Heavier accuracy and reviewer tracking; watch sentiment and disclaimer presence.

Long-term trend analysis

  • Compare quarter-over-quarter inclusion and citation share; annotate major engine changes.
  • Track seasonality for prompts (e.g., budgeting season, holidays) and adjust panels.
  • Watch decay: how long citations persist after updates; plan refresh cycles accordingly.
  • Monitor competitor moves: new mentions, schema improvements, or content refreshes that change share.

How AISO Hub can help

Measurement is built into every AISO Hub engagement.

  • AISO Audit: Baseline AI visibility, prompt coverage, and data quality; deliver a prioritized plan.

  • AISO Foundation: Set up prompt libraries, dashboards, schema governance, and tracking aligned to your entity graph.

  • AISO Optimize: Run experiments, refine prompts, and close gaps in clusters and locales.

  • AISO Monitor: Ongoing panels, alerts, and executive-ready reports tying AI visibility to revenue.

Conclusion

AI SEO metrics let you prove and improve AI search performance.

Use the exposure–quality–trust–impact taxonomy, run consistent prompt panels, and connect visibility to conversions.

Track accuracy relentlessly, fix issues fast, and keep schemas and entities tight.

Report weekly and monthly so stakeholders see progress and fund the roadmap.

When you align measurement with the AI Search Ranking Factors framework, every content, schema, and performance fix becomes visible in your dashboards and your pipeline.

If you want a partner to build and run this measurement engine, AISO Hub is ready to audit, implement, optimize, and monitor so your brand shows up wherever people ask.