How to Track Brand Visibility in Google AI Overviews (SGE) in 2025
Learn how to measure brand visibility, mentions, citations, and sentiment in Google AI Overviews. Executive guide with 2025 KPIs and practical workflow.
Google’s AI Overviews can make or break your brand’s discoverability for high‑intent questions. In 2025, leaders need more than “Are we number one for a few queries?” You need an executive‑ready system that measures whether AI Overviews mention your brand, cite your content, describe you positively, and cover the entire customer journey.
What you’ll get here: a pragmatic, board‑ready framework with clear KPIs, formulas, and a step‑by‑step workflow your SEO/analytics team can operationalize within a week. We’ll also address volatility, evidence archiving, and reporting cadence—so you can defend the numbers.
Time and difficulty
Time to stand up a first iteration: 5–10 working days (depending on query volume and tooling)
Ongoing effort: 2–4 hours/week for updates, QA, and reporting
Difficulty: Moderate (analytics + SEO operations)
Prerequisites
Defined ICP and journey stages
Access to analytics/BI (e.g., BigQuery/Sheets + Looker Studio/Power BI)
A compliant method to capture AI Overview presence, text, and citations (manual sampling or reputable third‑party services)
Important caveat
AI Overviews are volatile and personalized. Sampling across locations, devices, and time windows—and archiving evidence—is essential. Google documents how AI features surface with linked sources for transparency in 2025; expect shifts as features evolve, per the updated guidance in the Google Search Central AI features documentation (2025) and the Google AI Mode announcement (May 2025).
What “visibility” in AI Overviews really means
Traditional rank tracking doesn’t translate directly to AI Overviews. For leaders, visibility should be defined across four dimensions:
Brand mentions in the generated text: Is your brand named in the AI summary?
Citations to your content: Does Google cite your domain among sources under the AI Overview?
Sentiment of the mention: Is the description positive, neutral, or negative?
Journey coverage: Are you present (mention or citation) across awareness, consideration, decision, and post‑purchase queries?
Why this matters now
Independent studies throughout 2025 show that AI Overviews appear for a meaningful share of informational searches, with prevalence fluctuating by update, market, and query type. For example, the Semrush study (July 2025) found AI Overviews on about 13% of queries in March 2025, while Search Engine Land’s April 2025 analysis observed spikes during core updates.
When AI Overviews appear, users often click less on traditional links. The Pew Research brief (July 2025) reports reduced link clicks in the presence of an AI summary, echoed by Digital Content Next’s May 2025 analysis. That makes mentions and citations in the Overview itself disproportionately important.
Executive KPIs and formulas
Define these over a fixed query universe and timeframe (e.g., weekly).
Brand Mention Rate (BMR)
What it measures: Share of AI Overview responses that mention your brand in the generated text.
Formula: BMR = (# of AI Overviews mentioning Brand) / (# of queries with an AI Overview present) × 100%
Citation Frequency (CF)
What it measures: How often your domain is cited among the AI Overview sources.
Formula options:
CF% = (# of AI Overviews citing your domain) / (# of queries with AI Overviews) × 100%
CF per 100 queries = (Total citations of your domain across all AI Overviews) / (Total queries) × 100
Sentiment of Mentions (SoM)
What it measures: Tone of the text around your brand mention.
Report distribution: % positive / % neutral / % negative
Optional score: Sentiment Score = (% positive – % negative)
Journey Coverage (JC)
What it measures: Presence by stage.
Formula: JC(stage) = (# of queries in stage with a mention or citation) / (# of queries in stage where an AI Overview is present) × 100%
AI Overview Share of Voice (SOV‑AIO) [optional]
What it measures: Your share of mentions or citations vs. a defined competitor set.
Formulas:
SOV‑AIO (mentions) = Brand mentions / (Mentions of all brands in set) × 100%
SOV‑AIO (citations) = Brand citations / (Citations of all brands in set) × 100%
These align with broader generative search KPI thinking discussed by Search Engine Land’s KPI lists (June 2025) and their search visibility framework (August 2025).
The workflow: build, capture, parse, aggregate, report
Follow these steps in order. At each step, we include verification checkpoints and pitfalls.
1) Build a journey‑aligned query universe
Do this
Map awareness → consideration → decision → post‑purchase/support.
Source from ICP pain points, sales FAQs, customer support logs, and competitor claims pages.
Include modifiers that commonly trigger AI Overviews: how, what, why, best, vs, pros/cons, cost, safety, reliability, examples. Studies in 2025 indicate long, question‑style queries are more likely to trigger AI Overviews; see the WebFX 2.3M‑keyword analysis (July 2025) and practical guidance like Single Grain’s 2025 guide.
Sampling guidance
SMB/scale‑up minimum viable set: 200–500 queries per stage.
Enterprise: 1,000+ per stage.
Refresh 10–20% monthly to prevent staleness; maintain a stable control set for trend comparability.
Verify
Each stage has enough queries to produce statistically stable weekly KPIs.
The list reflects your top markets and product lines.
Common pitfalls
Over‑indexing on branded or vanity queries
Ignoring post‑purchase/support topics (a rich area for sentiment and advocacy)
2) Define your sampling plan and evidence archive
Do this
Geography: include your priority countries/regions; local businesses should use granular geo sampling. Tools like Local Falcon illustrate geo‑grid capturing for AI Overview visibility as of 2025; see their AIO feature page (May 2025).
Device: capture both mobile and desktop; mobile often dominates volume.
Cadence: 2–3 runs per week for evergreen topics; daily for volatile/news queries.
Evidence archive: for each run, save screenshot (PNG), HTML or structured extraction, timestamp, geo, device, language, and user‑agent. Compute a SHA‑256 hash for integrity; embed links to artifacts in dashboards. Evidence archiving is essential given volatility and personalization, and it aligns with defensibility guidance echoed in 2025 best‑practice commentary.
Compliance note
Google does not offer a public API for raw AI Overview text. Review Terms of Service and prefer reputable providers or manual sampling. Start from Google’s own guidance on AI features for site owners in the Search Central docs (updated 2025).
Verify
You can reproduce a subset of captures across devices/locations within a reasonable tolerance window.
Your archive foldering and metadata are consistent; artifacts open and render correctly.
Common pitfalls
Sampling only one city/device
No archived evidence—leads to unresolvable leadership questions later
3) Run scheduled AI Overview checks at scale
Do this
For each query and sampling setting, record:
Whether an AI Overview is present
The generated text (if accessible via your method)
The list of cited sources (normalize to domains)
SERP context (e.g., “People also ask” elements) when relevant
Schedule runs and log failures/timeouts; annotate run windows.
References and options
Many teams use third‑party SERP APIs/providers to detect AI Overview blocks and extract citations; a practical example is the Hive Digital Apps Script approach (July 2025), which illustrates presence detection and archiving.
Verify
Presence detection accuracy via manual spot checks on 10–20% of queries per run.
Citation extraction correctly normalizes domains (e.g., with/without www, subdomains).
Common pitfalls
Treating “no AI Overview” as an error rather than a valid outcome
Double‑counting citations due to URL parameter variants
4) Parse outputs: mentions, aliases, and citations
Do this
Brand entity detection: build an alias list (official name, product names, common misspellings). Apply case‑insensitive matching with entity context to avoid collisions with unrelated brands.
Extract citations: tag owned (your domain) vs. third‑party; note frequency and, if available, relative order among sources.
Deduplicate mentions and citations per query per run.
Verify
Manual QA 10–20% of mention detections and citation tags; correct alias list and matching rules.
Ambiguous matches flagged for reviewer decisions.
Common pitfalls
Missing branded product lines in the alias list
False positives from homonymous brands or generic terms
5) Classify sentiment at the mention level
Do this
Assess the sentence/segment surrounding the brand mention as positive, neutral, or negative.
Start with a simple rules‑plus‑model approach; maintain a custom lexicon for your industry and update it from QA feedback.
Escalate ambiguous cases for human review.
Verify
Inter‑rater agreement on a QA sample; calibrate thresholds until agreement >80% is achieved.
Drift checks monthly (language may shift as models and narratives evolve).
Common pitfalls
Assigning sentiment to the full Overview instead of the specific mention context
Over‑trusting generic sentiment models without domain tuning
6) Aggregate and visualize: executive dashboards
Build an executive view with:
KPI tiles: BMR, CF, Sentiment Score + distribution, Journey Coverage by stage, optional SOV‑AIO
Trend lines: last 6–12 weeks
Breakdown charts: stage by stage, and by market/device
Diagnostic tables:
Top queries with “AI Overview present but no citation”
Negative‑sentiment hotspots (query, excerpt, date, evidence link)
Competitor SOV shifts week‑over‑week
Embed evidence
Link each row to the archived screenshot/HTML with timestamp and settings. This is your audit trail.
Why this works
Leaders get the “what” at a glance, and analysts have the “why” and “proof” one click away. This mirrors modern visibility reporting practices in 2025 highlighted by industry frameworks like Search Engine Land’s visibility approach (Aug 2025).
7) Operate the improvement loop
Translate insights into a prioritized backlog:
Content upgrades: concise answers high on the page; structured sections, FAQs, tables; freshness updates; target AIO‑friendly modifiers. See practical guidance in Single Grain’s 2025 overview guide.
EEAT signals: author bios, first‑hand expertise, original data (surveys, benchmarks), transparent sourcing; Google’s 2025 blog on succeeding in AI Search emphasizes quality, experience, and helpfulness—see the Search Central post (May 2025).
Citations strategy: publish unambiguous, citable statements; implement structured data (FAQPage/HowTo where appropriate); pursue authoritative third‑party corroboration.
Reputation: address negative narratives; seed case studies and proof points; engage PR.
Technical hygiene: indexability, speed, canonicalization, and question‑aligned headings.
Verify improvements
Tag updated content; monitor query‑level before/after metrics for BMR/CF/SoM.
Keep a changelog linked to dashboard annotations.
Tooling: manual vs. automated (and a neutral toolbox)
Two viable paths exist; many teams mix both.
Manual/assisted path
Best for pilots and small sets. Use controlled browsers for scheduled checks, capture screenshots and HTML, and log metadata in Sheets/BigQuery. Strongly emphasize evidence archiving and QA sampling.
Automated path
Use reputable providers or internal scripts to detect AI Overview presence, extract citations, and store evidence. The Hive Digital Apps Script walkthrough (July 2025) demonstrates a pragmatic approach for presence/citation extraction and scheduled archiving.
Neutral toolbox (capabilities change fast in 2025—verify current docs)
Geneo — Disclosure: Geneo is our product. Focus areas include cross‑platform AI visibility tracking, sentiment analysis of brand mentions, and actionable content roadmaps aligned to journey stages. Use it when you need scheduled checks, sentiment tagging, and evidence links centralized in one workspace.
Local Falcon — Geo‑focused visibility tracking with AI Overview detection and local grid views; useful for city‑/neighborhood‑level sampling. See their AIO feature description (May 2025).
Custom Apps Script + SERP API stack — As outlined by Hive Digital (July 2025), pairs scheduled runs with Sheets/Drive for archival.
Roundup lists for additional options — For a current landscape of trackers and their capabilities, consult the SitePoint roundup of Google AI Overview trackers (September 2025).
Evaluation criteria (compare tools on these)
Coverage: presence detection, citation extraction, text capture
Automation: scheduling, geo/device sampling, retries
Sentiment: mention‑level sentiment classification and QA workflows
Evidence archiving: screenshots/HTML, hashes, artifact linking back to dashboards
Integrations: exports, BI connectors (Looker Studio/Power BI), data warehouses
Governance: user roles, change logs, auditability
Dashboards, cadence, and governance
Recommended cadence
Weekly: executive KPI tiles and trends; spotlight negative‑sentiment hotspots and “AI Overview present but no citation” opportunities
Monthly: journey coverage review, competitive SOV‑AIO, and a backlog refresh
Quarterly: strategy review, query universe refresh (rotate 10–20%), and governance audit
Roles and responsibilities
SEO lead: owns query universe, backlog, and improvement roadmap
Analytics partner: owns capture pipeline, parsing, QA, and dashboards
Marketing leadership: sets targets, approves priorities, and reviews outcomes
Governance checklist
Change log of query adds/removals with rationale
QA sampling results and threshold adjustments (mentions and sentiment)
Evidence archive integrity checks (spot‑reopen artifacts)
Compliance review of capture methods each quarter
Troubleshooting: what to do when KPIs stall or dip
If Brand Mention Rate is flat
Diagnose: Are you present but not named? Review Overview text excerpts and competitor mentions.
Fix: Update on‑page copy to feature unambiguous brand references aligned to common questions; add comparison pages (“vs,” “best for…”) that AI Overviews frequently summarize.
If Citation Frequency is low
Diagnose: Are your pages structurally citable? Review whether your content provides concise, verifiable statements.
Fix: Publish original data and practical checklists; add structured data; reduce ambiguity and lead with answer statements near the top. Industry commentary in 2025 emphasizes extractable clarity; see SEO.com’s AI Overviews explainer (Sept 2025).
If sentiment trends negative
Diagnose: Identify the exact segments; link to archived evidence to see context.
Fix: Address the issues head‑on (product, policy, or messaging updates); publish transparent responses, customer evidence, and third‑party validations. Re‑measure post‑intervention.
If journey coverage is uneven
Diagnose: Which stages lack presence? Often awareness and post‑purchase are neglected.
Fix: Build content for those stages with the right modifiers and structured answers.
If numbers are erratic week‑to‑week
Diagnose: Sampling bias or volatile topics.
Fix: Increase sample size, stabilize the control set, and widen geo/device sampling. Spikes during algorithm updates are well‑documented, e.g., Search Engine Land’s April 2025 spike report.
Quick reference: implementation checklist
Strategy and scope
Define markets, devices, cadence, and competitor set
Approve KPI formulas and targets
Query universe
200–500 per stage (SMB/scale‑up) or 1,000+ (enterprise)
Include AIO‑friendly modifiers; refresh 10–20% monthly
Capture and archive
Scheduled runs; log presence, text (if accessible), citations
Save PNG + HTML; timestamp, geo, device, language, UA; compute SHA‑256
Parsing and sentiment
Alias list for brand/products; domain normalization for citations
Mention‑level sentiment with QA sampling and lexicon updates
Dashboards
KPI tiles + trend lines; stage/market/device breakdowns
Diagnostic tables and embedded evidence links
Improvement loop and governance
Backlog creation; EEAT/content/PR/technical fixes
Weekly/monthly/quarterly reviews; change logs; compliance checks
What “good” looks like after 6–8 weeks
Stable BMR and CF trend lines with clear uplift tied to specific interventions
Balanced journey coverage (≥70% presence in priority stages where AI Overviews appear)
Sentiment distribution skewing positive, with negative pockets actively addressed
Evidence archive linked throughout dashboards; leadership confidence in the numbers
A prioritized backlog feeding content, PR, and web teams with measurable impact
Next steps
Kick off with a pilot: 1–2 markets, 400–800 queries total, 3 weeks of captures.
Stand up the dashboard and evidence archive in parallel.
Socialize the KPI model with leadership and set quarterly targets.
Optional: If you prefer an out‑of‑the‑box stack that handles scheduled checks, sentiment tagging, and evidence archiving, consider evaluating Geneo alongside the other tools noted above.
Further reading
Background on how AI Overviews surface and link to sources: the Google AI Mode update (May 2025) and the Search Central AI features overview (2025)
Prevalence, volatility, and patterns: the Semrush AI Overviews study (July 2025), WebFX query pattern analysis (July 2025), and Search Engine Land update analysis (April 2025)
Practical capture/automation examples: Hive Digital’s Apps Script guide (July 2025) and the SitePoint trackers roundup (Sept 2025)
