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//.case_study_006 MODEL

Illustrative model — not a specific client. Every figure is a projection built from real 2026 benchmarks (Hawaii home-services paid cost-per-lead ~$100, HRS Chapter 444 licensing as a trust signal, and local-3-pack visibility as the primary driver of "near me" service calls). It shows how the Reef Method would play out for a representative operator — not a verified engagement outcome.

Modeled scenario // Hawaii home-services operator · paid-search-dependent lead mix

Recovering ~$43K/yr From Paid-Lead Dependence.

An illustrative model — not a client. How a Hawaii home-services operator could move ~60% of lead volume off paid search and onto the Google local 3-pack with the Reef Method — and what that shift is worth.

Hawaii (Modeled) · Home Services (Modeled Scenario) · 12-Month Model
projected

$72K

Annual paid-lead spend (modeled)

projected

$43K

Modeled lead-cost saved / yr

projected

60%

Volume moved to local pack (modeled)

Renting calls vs owning the 3-pack

For Hawaii home-services operators — plumbing, pest control, cleaning, landscaping — most calls come from one of two places: paid search (Google Ads / Local Services Ads) or the organic Google local 3-pack. Paid leads in home-services categories run to roughly ~$100 per lead, and you pay that price every single time. A local-pack call, once you rank, costs nothing at the margin.

There's also a Hawaii-specific trust lever most operators leave on the table: any work over $1,000 requires a license under HRS Chapter 444 (DCCA Contractors License Board). Surfacing that license — in schema, on the site, and on the Google Business Profile — is exactly the kind of verifiable signal that lifts both human conversion and AI-engine trust.

The modeled operator

  • Lead volume: ~60/month.
  • Paid cost-per-lead: ~$100 (home-services category benchmark, 2026).
  • Licensed under HRS Chapter 444 — currently not surfaced anywhere machine-readable.
  • Local-pack presence: weak — most volume is bought, not earned.

The math, in full

Status quo (paid-dependent)

  • 60 leads/month × ~$100 = ~$6,000/month
  • Annual paid-lead spend: ~$72,000/year

Modeled shift to the local 3-pack

  • Move ~60% of lead volume to owned local-pack visibility (near-zero marginal cost once ranked).
  • Annual saving: 60% × $72,000 = ~$43,000/year.

And the local-pack presence compounds — it keeps producing calls month after month, so the gap between "owned" and "rented" widens over the 12-month horizon rather than holding flat.

Conceptual diagram comparing ~$72K/yr of paid-search lead spend against owned Google local-3-pack lead volume for a Hawaii home-services operator, showing the modeled ~60% shift and ~$43K/yr saved
Modeled shift from paid leads to the owned local 3-pack — conceptual image, not a client's data.

How the Reef Method gets there

01 · Substrate — the local foundation

Google Business Profile optimization, NAP consistency across directories, Service + areaServed schema, the HRS 444 license surfaced in markup and on-page, and a mobile experience fast enough that a tap-to-call converts before the visitor bounces.

02 · Coral — service-area content

Pages built for the queries that actually convert: "emergency plumber Kailua," "pest control Kapolei," and the service-area combinations a single homepage can never rank for.

03 · Citations — win the 3-pack and the AI answer

Local-pack ranking is the primary layer here: it's the difference between renting calls and owning them. The same signals earn AI-engine citations for "best [trade] in [town]" — the new front door for "near me" service discovery.

04 · Ecosystem — reviews are the flywheel

Review velocity is the strongest local-pack ranking lever there is, and it feeds repeat and referral business. A steady review system is what holds 3-pack position once it's won.

What this model is — and isn't

This is an illustrative model, not a client outcome. The cost-per-lead and local-pack dynamics are documented; the 60% volume shift and the savings figure are projections that depend on the trade, the market, and execution. Run your own numbers in the calculator below.

//.methodology

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Primary layer: Citations · GEO, AEO, Local Pack, and AI-engine citation tracking — what makes the reef visible to ChatGPT, Google AI Overviews, and Perplexity.

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Run These Numbers On Your Business.

These are illustrative figures. Book a free audit and we’ll model the math on your actual rates, occupancy, and lead mix — then map the highest-leverage first move.