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)

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:

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.

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)

Review benchmarks by market size and niche:

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

Step 3: Multi-Platform Review Inventory

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

Step 4: Competitor Review Benchmarking

Findings:

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

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.

Step 6: Review Generation Assessment

Does the business have a systematic review generation process?

Assessment: Active system / Passive (sporadic) / None

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

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?

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

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

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:

Brand Authority Score for AI (0–100)

Brand Mention Action Plan

Step 10: Reputation Recovery (If Needed)

If average rating < 4.0 or significant negative content:

Priority Recovery Roadmap:

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."

Priority Recommendations

Priority Matrix (Impact × Feasibility)

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.

Scoring

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.

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

Reputation Quick Reference

Review Platform Priority Table (2025)

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