tonyjbala
9.0 KiB
DOP — Top-5 Cold Audit Outreach Pack
Target verticals (from dop-vertical-prioritization-matrix.md, all score 4.00–4.33):
- HVAC · 2. Plumbers · 3. Law Firms · 4. Dentists · 5. Multi-Location
Grounded in: 68 captured Bizl pages (research/bizl-com/01–68) + 4 case studies (plumbing 55, HVAC 56, law 57, dentist 54). Multi-Location has no Bizl case study — flagged below.
Core motion: Free Cold Audit → Threat Diagnosis → Focused PoC → Retainer. DOP differentiator vs Bizl: human-verified accuracy + the Cold Audit as free, measurable proof (Bizl sells a black-box "done-for-you" with unverified dashboard numbers).
1. HVAC
Hook: "When a homeowner asks ChatGPT 'best HVAC company near me' during a heat wave, they call whoever's in the AI answer. If that's not you, a competitor gets a job worth thousands — instantly."
The Silent Loss
- Summer AC / winter heating spikes: searchers aren't comparison shopping — they call the first credible result.
- Off-season is when visibility gets built; peak season is when it pays. Missing both windows = compounded loss.
- Bizl's own flagship testimonial is HVAC ("287% more AI mentions in 80 days") — the vertical is proven convertible.
Proof anchor (Bizl case study 56, Tier-3 self-reported): Atlanta HVAC — score 14→61, "calls tripled" in 90 days. Owner cut $4k/mo ad spend and still booked more jobs. Flag: hero says 45 days, body says 90 — DOP reports one consistent window.
DOP differentiator: Human-verified emergency availability + equipment-type schema (central air, mini-split, heat pump, gas furnace, boiler). Automated tools drift on live hours; we don't.
Cold Audit offer: "Free 4-minute AI-visibility audit — we'll show you exactly which HVAC searches you're losing to competitors right now."
First-touch CTA (sample): "Hi [Owner] — when someone in [City] asks ChatGPT for an HVAC company during a cold snap, does your shop show up? Most don't, and they lose the call before the phone rings. I'll run a free 4-minute visibility audit and show you the gap — no pitch, just the data."
Measure honestly: Pre/post call-volume baseline (not just a "score"), single defined window, seasonal timing called out.
2. Plumbers
Hook: "A burst pipe at 2 a.m. → the homeowner asks ChatGPT 'emergency plumber open now' → calls the first name. That call is the highest-urgency, fastest-converting lead in the trade. Is it going to you or a competitor?"
The Silent Loss
- Plumbing is pure urgency: "first credible result wins the call."
- Unclaimed Foursquare listing (ChatGPT's ~70% local data source) with wrong hours = invisible to every AI recommendation.
- Most plumbing businesses miss ≥2 of the 3 core signals (citation, schema, GBP completeness).
Proof anchor (Bizl case study 55, Tier-3 self-reported): Tampa emergency plumber, 18 yrs in business — score 21→70, "calls tripled" in 90 days. Owner: "I didn't touch a computer." Flag: hero says 67 days, body says 90 — DOP fixes the inconsistency.
DOP differentiator: Human-verified emergency availability + response-time schema. The blue-collar "I'm a plumber, not a marketer" persona wants proof, not a black box — show the audit.
Cold Audit offer: "Free 4-minute audit — see if ChatGPT is sending your emergency calls to a competitor tonight."
First-touch CTA (sample): "Hey [Owner] — quick question: if someone in [City] has a burst pipe right now and asks ChatGPT for an emergency plumber, does your business appear? I can run a free 4-minute check and show you exactly where you stand. No obligation."
Measure honestly: Track emergency-query visibility (48h measurable) + call baseline; define "tripled" with a real denominator.
3. Law Firms
Hook: "When a client asks ChatGPT or Perplexity for the 'best personal injury attorney near me,' they get a short list. Personal injury is high-consideration, high-value — if you're not in that answer during the research window, you don't get the case."
The Silent Loss
- Legal = highest-value missed leads; one case can "pay for years of service" (Bizl law testimonial).
- AI cross-references bar directories, legal platforms, local listings — inconsistent NAP, post-merger firm names, or missing bar data kills recommendation confidence.
- First-mover advantage: "most attorneys haven't optimized for AI search."
Proof anchor (Bizl case study 57, Tier-3 self-reported): Houston PI firm — score 17→69, "calls tripled" / hero claims "4x high-value inquiries." Flag: two outcome frames not reconciled on page — DOP reports ONE metric, honestly.
DOP differentiator: Human-reviewed bar-admission + practice-area schema; transparent method the attorney can understand (skeptic persona converts on disclosed data, not hype).
Cold Audit offer: "Free AI-visibility audit — see which high-value searches your competitors own and which you're losing."
First-touch CTA (sample): "[Attorney] — when a potential client asks ChatGPT for a PI lawyer in [City], which firms show up? We ran visibility audits on several Houston firms and found respected practices losing cases to weaker AI-recommended competitors. I'll show you your firm's gap in 4 minutes, free."
Measure honestly: Define "high-value inquiry" with a real count; single sourced metric; competitive search-win/loss table.
4. Dentists
Hook: "A practice with 400 five-star reviews — and ChatGPT still sends patients to competitors. Reviews aren't the same as AI recommendability. Are you in the conversation?"
The Silent Loss
- High-star paradox: strong reputation ≠ AI visibility (Bizl dentist case study hook).
- Without structured data, AI can't tell you offer Invisalign, implants, emergency, or pediatric — you miss high-intent searches.
- Directory drift across Healthgrades, Zocdoc, Yelp, local listings reduces trust.
Proof anchor (Bizl case study 54, Tier-3 self-reported): Brooklyn dentist, 400+ 5-star reviews — score 19→74, "calls tripled" in 90 days. By month 2, new patients said "found us through ChatGPT." Flag: n=1, self-reported — DOP supplies verified before/after.
DOP differentiator: Human-verified insurance-accepted + service schema (implant/Invisalign/emergency/pediatric/cosmetic). Healthcare accuracy is where automated tools fail.
Cold Audit offer: "Free 4-minute audit — find out if ChatGPT recommends you for 'dentist near me' and high-intent services like Invisalign."
First-touch CTA (sample): "Dr. [Name] — your 400 reviews are gold, but when a patient asks ChatGPT for a dentist in [City], does it name you? We audit AI visibility for dental practices and show exactly which high-intent searches (Invisalign, implants, emergency) you're missing. 4 minutes, free."
Measure honestly: New-patient attribution (not just "score"); insurance/service schema correctness verified by human.
5. Multi-Location
Hook: "National brand recognition doesn't buy local AI visibility. A well-optimized single-location competitor can outrank each of your stores on ChatGPT. Every inconsistent NAP across your portfolio lowers confidence in ALL of them."
The Silent Loss
- Each location must be individually optimized — brand awareness doesn't transfer.
- Inconsistent location data across the portfolio = compounded, systemic loss.
- Highest aggregate LTV of any vertical; Cold Audit scales cleanly across N sites.
Proof anchor: No Bizl case study exists for multi-location — honest gap. Supporting signal only: Bizl's 72% hero "gap" stat + the per-location Foursquare/NAP pattern. DOP should build the first verified multi-location proof.
DOP differentiator: Per-location human-verified schema at scale + portfolio-wide NAP consistency reporting — exactly the "highest-leverage fix" Bizl names but can't guarantee with automation.
Cold Audit offer: "Free portfolio audit — we'll score every location's AI visibility and show you which stores competitors are outranking."
First-touch CTA (sample): "[Brand] — your locations may be strong in-person but invisible to ChatGPT individually. We audit multi-location AI visibility and surface exactly where competitors outrank your stores. Free portfolio scan, no software to manage."
Measure honestly: Per-location before/after + portfolio NAP-consistency score; aggregate LTV modeled transparently.
Pack Notes (for Leonard)
- All proof anchors are Bizl's self-reported dashboard numbers (Tier-3, n=1). Use them to frame the wedge, never as DOP's own verified results.
- Bizl's recurring framing disjuncts (67 vs 90 days; "tripled" vs "4x") are deliberate honesty flags — DOP's measured reporting must be internally consistent to win the credibility contrast.
- Cold Audit = the free, measurable entry that Bizl's "4-minute audit" mimics. Lead with it; the human-verified accuracy is the closing differentiator.
- Next: turn each one-pager into a deliverable PDF/email template, or run a live Cold Audit on a seeded target per vertical.