# Reputation Audit

> Reputation Audit

- Skill: `mshahiddigital/reputation-audit` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mshahiddigital/reputation-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mshahiddigital/reputation-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: mshahiddigital (https://skillmd.com/u/mshahiddigital)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mshahiddigital/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:**

| 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 |

---

## 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)
| 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

---

## 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

---

## 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?

---

## 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?

| 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

---

## 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

| 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 |

---

## 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 |

---

## 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)

| 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.

---

## 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.

---

## Output

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

```yaml
---
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)

| 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 |