GWG Research · 2026

Who Decides Your
Lighting Brand?

6 roles. One $312B market. 0% agreement on who picks the brand.
Cross-role analysis of the US building electrical decision chain — with original data tables and market sizing.
Last updated: June 19, 2026 · By Wilson
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The Chain

It Passes Through 6 Hands

Each node has different power — and different leverage points for a brand.
Owner — writes the check
💰
Architect — writes the Spec
📐
General Contractor — controls budget
🏗️
Electrical Contractoractually decides
Distributor — gatekeeper at checkout
📦
Electrician — installs it
🔧

Hidden path: AI procurement platforms now enter at every stage — recommending, blocking, and replacing brands before humans see options.

6 Roles × 5 Dimensions

Who Has What Power

RoleBrand PowerSearch BehaviorChannel
OwnerLowModerateReferral
ArchitectMediumLowSpec DB
GCLowVery LowSubcontractor
ECHighLowDistributor
DistributorHighModerateInternal SO
ElectricianMediumModerateDistributor
Electrical Contractor + Distributor hold the real buying power.
Neither searches Google.
Market Data

$312B — Who Gets What

SegmentMarket SizeGrowthMargin
Residential new$68B+4.2%12-18%
Residential remodel$94B+6.8%15-22%
Commercial new$82B+5.1%8-14%
Commercial remodel$47B+7.3%10-18%
Industrial$21B+8.6%6-12%
Remodel is bigger than new construction in both residential and commercial.
Source: IBISWorld, BLS Construction Spending, NECA 2025
Two Markets

Different Playbook Needed

ProfessionalDIY / Retail
ChannelDistributor-drivenSearch-driven
Buyer age50-65 (EC)25-50
LoyaltyHighLow
Barrier to enterVery highLow
Go-to-marketLunch + rebatesGEO + video
T-Model skewT0-T1T2-T3
GEO effectivenessLowHigh

One brand needs two playbooks. Mixing them produces mediocre results in both.

AI Path

4 Stages Where
AI Enters

1. Design
AI matches real products to BIM models. Brand lock-in pre-Spec.
2. Bidding
AI reads Spec PDFs → matches SKU databases. Invisible if absent.
3. Procurement
AI tracks delivery timelines. Unstable supply = auto-replaced.
4. Pre-install
AI reviews submittals line by line. "Close enough" fails.

The new moat: Being written into the Spec is no longer enough. Being written into AI-readable data streams — SKU databases, BIM libraries, compliance checkers — is the real competitive barrier.

T-Model Applied

Lighting Industry
T Distribution

One industry, three segments — three completely different AI cognition profiles

Electricians T0 70-80%
Field workers, almost no AI use. Search "how to install" / "NEC code". Trust UL listing over brand ads. Reach: traditional SEO + FAQ Schema.
Distributors / Wholesalers T1-T2 ~60%
Use AI to check product specs and compare prices. Won't pay for AI tools. Care about supplier data accuracy. Reach: GEO + product comparison content.
DIY Homeowners T0-T3 Scattered
From complete novice to somewhat knowledgeable. Search "how to replace a ceiling light", YouTube is the primary gateway. Reach: YouTube + scenario content.
Key insight: The same product must speak three different languages. Electricians need UL/labor data (T0 SEO), distributors need spec sheets (T1-T2 GEO), DIY needs YouTube tutorials (T0-T3 scatter). One message cannot reach all three.
Data Sources

Where This Comes From

SourceTypeCoverage
BLS (CEW + OES)Employment/wagesFull US
IBISWorldIndustry reportsOD422, 23611
NECAContractor survey~2,000 firms
US CensusConstruction spendingMonthly
CrunchbaseAI platform funding2020-2026
NAEDDistributor dataAnnual
Full report: role-by-role market sizing, T-Model distribution, brand decision drivers, AI platform details, and a 7-action execution timeline.
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Related: GEO in North America 2026 · T-Model Framework

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