reputation-audit

Reputation & Review Management Audit — Phase 15

Executive Summary

Online reputation is a direct local pack ranking signal and an AI visibility gatekeeper. Businesses with <4.0 stars are systematically excluded from Google AI Overviews, ChatGPT recommendations, and Perplexity citations for most local service categories (2025). Review velocity — not review count — is what separates stagnant profiles from rising ones: Google's algorithm weights reviews from the last 60–90 days at 3–5× more than older reviews (confirmed via multiple local SEO studies, 2024–2025). A single SMS-based review request system set up in one afternoon can generate 4–8 reviews/month consistently — the highest-ROI action in local SEO. This phase audits the full review ecosystem, benchmarks against competitors, identifies fake/malicious content, and builds a systematic reputation management infrastructure.

2025 reputation benchmarks:

• Star rating threshold for AI inclusion: ≥4.3 (Google AIO), ≥4.0 (ChatGPT/Perplexity)
• Review count for local pack top-3: 50–200 depending on niche and market size
• Review velocity minimum to rank top-3: 4–8/month (mid-sized city, home services)
• Response rate target: 100% (Google confirms response rate is a quality signal)
• Recency weighting: reviews in last 60–90 days = 3–5× weight of older reviews (BrightLocal 2025)
• CTR lift from star ratings in SERPs: +17–25% (Search Engine Land, 2024)

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Why Reputation Is an SEO Factor (2025)

1. Local Pack ranking — review count and rating are direct top-3 ranking signals (BrightLocal 2025 Local Search Survey)
2. CTR — star rating in results increases click-through rate by 17–25% (Search Engine Land, 2024)
3. E-E-A-T — reviews are Google's primary trust signal for local Quality Rater Guidelines
4. AI visibility — Google AI Overviews, ChatGPT, and Perplexity explicitly reference review ratings when recommending businesses; <4.0 stars = excluded from AI recommendations in most categories
5. Conversion — 93% of consumers say online reviews impact their purchasing decisions (BrightLocal 2025); average consumer reads 7 reviews before trusting a business

Tools for this phase:

Tool	Purpose	Cost
BrightLocal	Review dashboard, NAP scan, citation audit, review generation	Paid ($29–79/mo)
Podium	SMS-based review requests, review inbox, webchat	Paid ($289+/mo)
Birdeye	Multi-platform review monitoring, AI response generation, sentiment analysis	Paid ($299+/mo)
ReviewTrackers	Competitive benchmarking, sentiment analysis, reporting dashboard	Paid
Grade.us	Review generation funnels, drip campaigns, white-label reporting	Paid ($110+/mo)
Google Business Profile	Direct review management, Q&A, insights dashboard	Free
Google Alerts	Monitor brand mentions in real-time across web	Free
Mention / Brand24	Social listening, competitor review tracking, sentiment scoring	Paid

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Step 1: Read Project Context

Read {AUDIT_DIR}/intake-data.md for business name, URL, location, and services. Read {AUDIT_DIR}/local-findings.md for GBP review baseline. Read {AUDIT_DIR}/competitor-profiles.md for competitor review benchmarks.

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Step 2: Review Inventory — Google

Google Reviews Baseline

Search [Business Name] [City] in Google Maps and note:

• Total review count: [X]
• Average rating: [X.X] / 5.0
• Rating distribution (1★ through 5★)
• Date of most recent review: [date]
• Date of earliest review: [date]
• Review velocity (per month over last 3 months): [X/month]
• % of reviews with photos: [X%]
• % of reviews mentioning specific services: [X%]
• % of reviews with keywords in text: [X%]

2025 Review Benchmarks (Local Pack Competitiveness)

Metric	Strong	Competitive	Weak	Critical
Google rating	≥4.7	4.3–4.6	4.0–4.2	<4.0
Review count	≥100	50–99	25–49	<25
Velocity (per month)	≥8/month	4–7/month	1–3/month	<1/month
Response rate	100%	80–99%	50–79%	<50%
Response time	<4 hrs	4–24 hrs	1–3 days	>3 days
% reviews with photos	≥30%	20–29%	10–19%	<10%
% reviews mentioning services	≥40%	25–39%	10–24%	<10%
Negative review recovery rate	≥80%	60–79%	40–59%	<40%

Review benchmarks by market size and niche:

Niche	Small Market (<100K)	Mid Market (100K–1M)	Major Metro (1M+)
Home services (plumbing, HVAC)	2–4/mo, 25+ total	4–8/mo, 50+ total	8–15/mo, 100+ total
Legal / professional	1–2/mo, 20+ total	2–4/mo, 40+ total	4–8/mo, 75+ total
Healthcare / dental	2–4/mo, 30+ total	5–10/mo, 75+ total	10–20/mo, 200+ total
Restaurant / food	5–10/mo, 50+ total	15–30/mo, 150+ total	30–50/mo, 500+ total
Automotive	2–5/mo, 40+ total	5–10/mo, 100+ total	10–20/mo, 200+ total

Veto: Rating <3.5 → maximum reputation score 40/100; effectively disqualified from local pack. Veto: Rating <4.0 → excluded from Google AIO recommendations for most service categories.

Review Response Analysis

• Owner response rate: [X%] of reviews responded to
• Average response time: [days]
• Response quality: Generic template / Personalized / Service-specific
• Negative reviews responded to: [X%]
• Tone of responses: Professional / Defensive / Empathetic

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Step 3: Multi-Platform Review Inventory

Platform	Review Count	Rating	Response Rate	Profile Complete?	Link
Google
Yelp
Facebook
BBB
Trustpilot
[Industry-specific]
[Industry-specific]

Industry-specific platforms by niche:

• Healthcare: Healthgrades, Zocdoc, WebMD, RateMDs
• Legal: Avvo, Martindale, Lawyers.com
• Home services: Angi, HomeAdvisor, Thumbtack, Houzz
• Restaurants: Tripadvisor, OpenTable, Grubhub
• Automotive: Cars.com, DealerRater, Carfax
• Hospitality: Booking.com, Hotels.com, Expedia
• Beauty/Wellness: Vagaro, StyleSeat, Mindbody

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Step 4: Competitor Review Benchmarking

Metric	Client	Comp 1	Comp 2	Comp 3	Gap
Google review count
Google rating
Review velocity/month
% 5-star
Response rate

Findings:

• Is client above/below competitor average?
• What is the review count gap to close?
• Which competitor has the strongest review velocity?

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Step 5: Sentiment & Content Analysis

Positive Review Themes

What do customers praise most? (Extract from actual reviews)

• [theme 1]: mentioned in X reviews
• [theme 2]: mentioned in X reviews
• [theme 3]: mentioned in X reviews

These are SEO opportunities — build content around what customers love.

Negative Review Themes

What complaints recur?

• [complaint 1]: mentioned in X reviews
• [complaint 2]: mentioned in X reviews

These are operational problems AND reputation risks. Flag for business improvement.

Keyword Presence in Reviews

Do reviews contain service keywords?

• "[primary service]": mentioned in X% of reviews
• "[location]": mentioned in X% of reviews

Service keywords in reviews help local pack rankings.

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Step 6: Review Generation Assessment

Does the business have a systematic review generation process?

Question	Yes/No
Review request sent after every job/purchase?
Review request via SMS?
Review request via email?
QR code at point of sale/service?
Staff trained to verbally ask for reviews?
Review link easily accessible on website?
Review link on GBP?
Follow-up system for non-responders?

Assessment: Active system / Passive (sporadic) / None

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Step 7: Negative Review Analysis

For every 1-star and 2-star review:

• Is there a response? Professional and empathetic?
• Is the complaint legitimate or fake/competitor-placed?
• Is the issue recurring (operational problem)?
• Has the issue been resolved?

Fake Review Detection

Signs of fake reviews:

• Posted in cluster (multiple on same day from accounts with no history)
• Reviewer has reviewed only this business (1-review accounts)
• Generic text ("Great service!" with no specifics)
• Reviewer located in different city

Recommendation if fake reviews found:

• Flag for removal via Google Business Profile reporting
• Respond professionally (do NOT engage aggressively)
• Document pattern for potential legal action if coordinated

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Step 8: Review Marketing Assessment

Reviews as a marketing asset:

• Are top reviews displayed on the website (testimonials section)?
• Are review stars in Google Ads (seller ratings)?
• Are reviews used in social media content?
• Is review count mentioned in ad copy / GMB description?
• Aggregate rating schema on homepage and service pages?

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Step 9: AI Review Impact Assessment (2025)

Reviews directly influence AI recommendation engines — not just traditional search.

Test protocol:

1. Search best [service] in [city] in Google AI Overviews → Does business appear? What rating/review count is displayed?
2. Ask ChatGPT: Who are the top [service] providers in [city]? → Is business mentioned?
3. Ask Perplexity: Best reviewed [service] in [city] → What review thresholds does it cite?
4. Check Google Maps AI summary (2025) — is business featured in AI-generated city/category overviews?

2025 AI Review Thresholds Observed:

• Google AI Overviews: typically features businesses with 4.3+ stars and 50+ reviews
• ChatGPT/Perplexity: cite businesses with established web presence + review mentions on trusted sources (Yelp, BBB, industry directories)
• Siri (Apple Maps): surfaces highest-rated options in category — requires Apple Maps verification

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Step 9b: Brand Mention Scan for AI Visibility

Critical insight: Brand mentions correlate 3× more strongly with AI visibility than backlinks (Ahrefs December 2025 study of 75,000 brands). AI platforms cite businesses they "know" from mentions across the web — not just businesses with strong link profiles.

Platform Mention Correlation with AI Citations

Platform	AI Citation Correlation	Weight	Why It Matters
YouTube	~0.737 (strongest)	25%	AI systems (especially Gemini) heavily index YouTube. Videos, reviews, and tutorials mentioning the brand = high AI visibility.
Reddit	High	25%	Perplexity sources 46.7% of citations from Reddit. ChatGPT also weights Reddit discussions. Authentic brand mentions in subreddit discussions = strong signal.
Wikipedia / Wikidata	High	20%	ChatGPT sources 47.9% from Wikipedia. Wikidata entity = 3× more AI citations. The #1 entity signal for AI.
LinkedIn	Moderate	15%	Copilot (Bing) weights Microsoft ecosystem. Thought leadership posts and company page completeness improve Copilot citations.
Domain Rating / Backlinks	~0.266 (weak!)	15%	Traditional backlinks still matter for organic SEO but are a weak predictor of AI citation. Brand mentions outperform links 3:1.

Key takeaway: A business with 50 genuine brand mentions across YouTube, Reddit, and industry forums will likely have better AI visibility than a business with 500 backlinks but no platform presence.

Brand Mention Audit Protocol

For each platform, search "[Business Name]" and document:

Platform	Search Method	Mentions Found?	Sentiment	Recency
YouTube	Search [Business Name] on youtube.com	Yes/No — [count] videos	Positive/Neutral/Negative	Last 6 months?
Reddit	Search [Business Name] on reddit.com	Yes/No — [count] threads	Positive/Neutral/Negative	Last 6 months?
Wikipedia	Search [Business Name] on en.wikipedia.org	Article / Mentioned / Absent	N/A	N/A
Wikidata	Search [Business Name] on wikidata.org	Entity exists? Q-number?	N/A	N/A
LinkedIn	Search [Business Name] on linkedin.com	Company page? Posts?	Positive/Neutral/Negative	Active?
Quora	Search [Business Name] on quora.com	Yes/No — [count] answers	Positive/Neutral/Negative	Last year?
Industry forums	Search niche-specific communities	Yes/No	Positive/Neutral/Negative	Recent?

Brand Authority Score for AI (0–100)

Component	Points	How to Score
YouTube presence (channel exists + brand mentioned in videos)	25	25 = active channel + external mentions; 15 = channel only; 5 = mentioned by others; 0 = absent
Reddit presence (genuine discussions, not spam)	25	25 = active contributor in relevant subreddits; 15 = mentioned positively; 5 = minimal mentions; 0 = absent
Wikipedia/Wikidata entity	20	20 = Wikipedia article; 15 = Wikidata entity; 10 = mentioned in other articles; 0 = absent
LinkedIn company page (complete + active)	15	15 = complete + regular posts + employee engagement; 10 = complete; 5 = basic; 0 = absent
Cross-platform mention consistency	15	15 = consistent NAP + brand description across all platforms; 10 = mostly consistent; 5 = some conflicts; 0 = major inconsistencies

Brand Mention Action Plan

Action	Impact (1–5)	Feasibility (1–5)	Priority	Effort
Create YouTube channel + publish 3 educational videos	5	3	15	8–16 hrs
Participate authentically in 2–3 relevant subreddits	5	4	20	2 hrs/week ongoing
Create Wikidata entity (if business has external coverage)	4	4	16	2–4 hrs
Complete + activate LinkedIn company page	3	5	15	1–2 hrs
Encourage customers to post YouTube review videos	4	3	12	Ongoing
Answer Quora questions in business category	3	4	12	1 hr/week
Add sameAs schema linking all platform profiles	4	5	20	30 min
Set up brand mention monitoring (Google Alerts + Brand24)	3	5	15	30 min setup

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Step 10: Reputation Recovery (If Needed)

If average rating < 4.0 or significant negative content:

Priority Recovery Roadmap:

Step	Action	Effort	Timeline	Impact (1–5)	Feasibility (1–5)	Priority
1	Resolve operational issues causing negative reviews	2–20 hrs	Immediate	5	3	15
2	Set up SMS review requests via Podium/Birdeye	2 hrs setup	Week 1	5	5	25
3	Respond to every existing negative review	30 min/review	Week 1	4	5	20
4	Request removal of clearly fake reviews (GBP flag)	15 min each	Week 1	3	4	12
5	Create suppression content (FAQs, About page, PR)	4–8 hrs	Month 1	4	4	16
6	Implement Birdeye/Podium for systematic management	4 hrs setup	Month 1	5	4	20

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Step 10: Review Response Templates

Provide 3 customized response templates:

5-Star Response (Personalized): "[Customer name], thank you for taking the time to share your experience with [specific service mentioned]. We're thrilled [specific thing they praised]. [Business name] team loves serving the [city] community. See you next time!"

Negative Review Response (Empathetic): "[Customer name], we sincerely apologize this wasn't the experience you expected. We take feedback very seriously. We'd love to make this right — please contact us at [phone] so we can resolve this personally. — [Owner name], [Business Name]"

Neutral Review Response: "Thank you for your feedback, [Name]. We appreciate you choosing [Business Name]. If there's anything we can do to make your next experience a 5-star one, please let us know."

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Priority Recommendations

Priority Matrix (Impact × Feasibility)

Action	Impact (1–5)	Feasibility (1–5)	Priority Score	Effort
Set up SMS review request system (Podium/Birdeye)	5	5	25	2 hrs setup
Respond to every unanswered review (positive + negative)	5	5	25	30 min/batch
Resolve operational issues driving negative reviews	5	3	15	Varies
Add AggregateRating schema to homepage + service pages	4	5	20	30 min
Flag and report fake/competitor reviews via GBP	3	5	15	15 min/review
Train staff on verbal review request after service	4	4	16	1 hr training
Display top reviews on website (testimonials section)	3	5	15	1–2 hrs
Create review-optimized landing page with schema	4	4	16	2–3 hrs
Set up QR code for review requests (print + digital)	3	5	15	30 min
Build multi-platform review monitoring dashboard	4	4	16	2 hrs setup

Immediate Actions (Week 1)

1. Deploy review request system — Set up Podium or Birdeye SMS flow: trigger = job completed → SMS within 2 hrs → link to GBP review page → automated follow-up if no response in 48 hrs. Expected: 4–8 new reviews/month from month 1.
2. Respond to all unanswered reviews — Prioritize all 1-star and 2-star first (reputation recovery), then 5-star (engagement signal). Use personalized templates (not generic). Expected: response rate 100%.
3. Add AggregateRating schema — Implement JSON-LD on homepage + service pages. Use ratingValue, reviewCount, bestRating:5, worstRating:1. Validate at search.google.com/test/rich-results. Expected: star ratings appear in SERP snippets = +17–25% CTR.
4. Flag fake reviews — For any cluster of reviews from single-review accounts posted on same day → Report via GBP Manager → "Flag as inappropriate" → Document pattern for potential legal action.

Short-Term (Month 1)

5. Fix operational root causes — Identify top 3 recurring negative themes from review content → escalate to operations team → create service delivery improvement SOP.
6. Build suppression content — If damaging content appears in branded SERPs: create positive content (case studies, awards page, testimonials hub, PR mentions) to push negative results below page 1.
7. Expand multi-platform presence — Claim and optimize profiles on 2–3 industry-specific platforms (see niche list in Step 3). Coordinate cross-platform review asks.
8. Create review marketing assets — Export top 5-star reviews → design social media cards → post on Instagram/Facebook weekly. Use as trust signals in Google Ads copy.

Medium-Term (Months 2–3)

9. Run 90-day velocity sprint — Goal: close gap to #1 competitor review count within 90 days. Calculate gap: if competitor has 150 reviews and client has 60 → need 90 reviews → 30/month → intensify SMS campaign + personal outreach from owner.
10. Build review diversity — Aim for reviews that mention: specific services (40%+), location/neighborhood (25%+), staff names (15%+), specific outcomes (20%+). These keyword-rich reviews improve local ranking and AI citation likelihood.

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Scoring

Category	Weight	Score
Google review count vs. competitors	15%	/15
Average rating (target: ≥4.5)	20%	/20
Review velocity (≥4/month for mid-market)	15%	/15
Response rate (100% = perfect)	15%	/15
Multi-platform presence (3+ platforms complete)	15%	/15
Brand mention authority for AI (YouTube/Reddit/Wikipedia/LinkedIn)	20%	/20

Veto: Average rating <3.5 → maximum score 40/100 regardless of other factors. Veto: Average rating <4.0 → flag as AIO exclusion risk; note in report.

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Output

Write to {AUDIT_DIR}/reputation-findings.md with YAML frontmatter:

---
skill: local/reputation-audit
phase: 15
date: [YYYY-MM-DD]
business: [Business Name]
url: [URL]
score: [X/100]
status: [healthy|needs-attention|critical]
google_rating: [X.X]
google_review_count: [X]
review_velocity_monthly: [X]
response_rate_pct: [X%]
aio_eligible: [yes|no|borderline]
veto_triggered: [yes|no]
---

Include:

• Score X/100 with per-category breakdown + veto status
• Review inventory table (all platforms with count, rating, response rate)
• 2025 benchmark comparison table (client vs. Strong/Competitive/Weak/Critical)
• Competitor benchmarking table (client vs. 3 competitors × 5 metrics)
• Market-size-adjusted velocity targets (small/mid/major metro × niche)
• Positive/negative theme extraction from actual review text
• Review generation system assessment (8-point checklist with gap analysis)
• Fake review flagging (if any — document patterns)
• AI visibility review assessment (AIO + ChatGPT + Perplexity citation status)
• Priority recommendations table (Impact × Feasibility scored, 10 actions)
• 30/90-day reputation improvement plan with numbered steps

Output files:

• {AUDIT_DIR}/reputation-findings.md — findings with score and review profile analysis
• {REPORTS_DIR}/phase-15-reputation.pdf — auto-generated PDF after phase completes

Key consumers:

• cross-cutting/local-impact-auditor — Online Reviews dimension (O in LOCAL-IMPACT)
• local/local-seo — shares review baseline data
• local/brand-serp — reputation signals affect Knowledge Panel trust
• output/report-generation — reputation section in master report

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Reputation Quick Reference

Review Platform Priority Table (2025)

Platform	Local Ranking Impact	AIO Citation Impact	Min Reviews Target	Effort to Optimize
Google Business Profile	Critical (primary signal)	High — feeds AIO local pack	50+ (4.5 stars)	30 min/week (respond to all)
Yelp	High (Yelp/Apple Maps/Siri)	Medium	20+ (4.0 stars)	15 min/week
Facebook Reviews	Medium (Meta search, brand SERP)	Low	15+	10 min/week
Industry-specific (Houzz/Angi/Healthgrades/Avvo)	High in niche	Medium	10+ per platform	20 min/week
BBB Rating	Medium (trust signal)	Low	A- or better	1 hr setup
Trustpilot	Low-Medium	Medium (cited by ChatGPT/Perplexity)	10+	15 min/week

AIO Review Thresholds (2025)

• <3.5 stars on GBP → excluded from AI Overview local recommendations entirely
• 3.5–3.9 stars → may appear but flagged as lower-rated; AIO citation probability reduced 60%
• 4.0–4.4 stars → competitive; AIO citable with sufficient review count
• 4.5–4.7 stars → preferred by AIO; cited in "best [service] in [city]" queries
• 4.8–5.0 stars with 50+ reviews → consistently cited in AIO + ChatGPT recommendations

INP + Reputation Connection

Review widgets (embedded Google Reviews, Trustpilot widgets) are common INP killers — they inject late JS that blocks user interaction. Load review badges lazily or use static HTML snippets with AggregateRating schema instead of live widgets for fastest page response.

Specific Thresholds by Business Type

Business Type	Min GBP Reviews	Min Rating	Review Velocity	Star Display
Restaurant/food	100+	4.3+	20+/month	AggregateRating schema
Medical/dental	50+	4.5+	5+/month	AggregateRating schema
Legal/professional	30+	4.4+	3+/month	AggregateRating schema
Home services	50+	4.5+	8+/month	AggregateRating schema
Retail/e-commerce	75+	4.3+	15+/month	Product + Organization schema
General local SMB	25+	4.0+	2+/month	AggregateRating schema