Frequently Asked Questions
What does GWG Research cover?
GWG Research provides independent analysis on how AI is reshaping B2B search, content strategy, and procurement decisions. This FAQ covers four areas: Generative Engine Optimization (GEO) and how it compares to SEO through the 4P marketing framework, the T-Model for AI adoption segmentation, and the US electrical decision chain. Data-driven answers with cited sources — start with the GEO/SEO comparison below if you're new here.
Start with the GEO strategy overview →📊 SEO vs GEO: 4P Marketing Framework
What's the fundamental difference between SEO and GEO as a business strategy?
SEO is a traffic arbitrage business — you create pages optimized for keywords, buy links, and convert search traffic into customers. The product is a rankable page, cost is linear per keyword ($500-$2,000/page), and it operates on a single shelf (Google SERP). GEO is a source asset business — you build a cross-source knowledge identity that AI models cite in their answers. The product is a citable knowledge entity, cost is step-function (breakthrough after reaching citation density), and it operates across multiple shelves (ChatGPT, Gemini, Perplexity, AI Overviews, Claude, and more). One is about ranking; the other is about being cited.
Download the GEO white paper →Should I budget for SEO or GEO in 2026? How should I allocate?
It depends on your stage. At $500/month or less, go 100% SEO (long-tail keywords) — GEO doesn't work at that scale. At $3K-$10K/month, allocate 60% SEO (core keyword maintenance) and 40% GEO (source building + original data). At $15K+/month, flip to 40% SEO and 60% GEO (category AI answer capture). At $50K+/month, go 30% SEO and 70% GEO (multi-platform AI citation dominance). The logic: SEO maintains present-tense traffic; GEO builds future-tense AI visibility. Most companies in 2026 are still at the 100% SEO end, which creates a window for early GEO movers.
Read the GEO strategy overview →Why is AI search conversion so much higher than traditional search?
Data consistently shows AI-referred traffic converts at 5-6x the rate of traditional organic search. Webflow measured ChatGPT referral conversion at 24% vs. Google non-branded search at 4%. The broader benchmark (Exposure Ninja 2026) puts AI search conversion at 14.2% vs. traditional search at 2.8%. Why? AI users are in an evaluative mindset — they asked a question, got a synthesized answer with sources, and click with intent. Google users are often in discovery mode — scanning, comparing, not ready to buy. However, AI search traffic only accounts for ~1.08% of total site traffic (growing ~1%/month), so the absolute volume is still much smaller than organic search.
Read the full GEO report →Does GEO replace SEO? Do I need both?
No — GEO does not replace SEO in 2026. They operate on different shelves: SEO targets Google's 10 blue links; GEO targets AI-generated answers across ChatGPT, Perplexity, Gemini, Claude, and AI Overviews. The traffic volumes are very different — SEO still drives 25-30% of typical site traffic while GEO drives ~1%. But GEO's share is growing ~1% per month and its conversion rate is 5x higher. The right question isn't "which one" but "when to shift." For early-stage products: SEO first (you need volume to validate). For established brands with original data: GEO first (your data is wasted on keywords). For mature companies: both — SEO for today's traffic, GEO for tomorrow's.
Download the GEO white paper →What does it actually cost to do GEO well?
DIY-level GEO (tools only, no service) costs $10-$100/month — but you'll get minimal results without strategy. The industry norm for meaningful GEO engagement is a retainer of $3,000-$10,000/month (per Superlines, DemandLocal, and multiple agency benchmarks). This covers: AI visibility audit, content optimization for citation, entity management across platforms (Wikipedia, Crunchbase, G2, Capterra), and multi-model tracking. Enterprise-grade GEO runs $10,000-$30,000+/month with proprietary tools and content factories. Compare this to SEO: a single content page costs $500-$2,000, and a 10-keyword program runs $5,000-$20,000/month plus tools. The key difference: SEO has predictable per-keyword cost; GEO's cost is about building citation density — once you reach a threshold, marginal cost drops sharply.
Download the GEO white paper →Which types of businesses should prioritize GEO over SEO?
Three types of businesses should prioritize GEO. First: B2B companies with high per-customer value (>$500/sale) — AI recommendation converts at 5x, and the higher deal size makes lower traffic volume acceptable. Second: companies with original, citable data — research reports, benchmarks, industry surveys, proprietary methodologies. Data is the 'gold' of GEO because AI models preferentially cite verifiable data points. Third: brands in categories where AI is already answering questions — search your category on ChatGPT and see if answers exist. If they do and you're not cited, you're invisible to AI users. Conversely, local businesses, low-ticket consumer products, and companies with no differentiation should prioritize SEO — GEO's ROI doesn't materialize without something worth citing.
Read the full GEO report →🧠 T-Model Framework
What is the T-Model for AI adoption?
The T-Model is a five-stage AI cognitive stratification framework that segments US adults by AI adoption depth: T0 Outsider (27%), T1 Triers (20%), T2 Free Users (22%), T3 Power Users (15%), and T4 Builders (4%). It helps product and marketing teams match strategy to AI maturity level.
Explore the T-Model framework →What percentage of US adults use AI daily?
Approximately 19% of US adults use AI daily (Menlo Ventures 2025). Only 3% pay for AI tools. 27% have never used AI at all (Quinnipiac 2026).
Explore the T-Model framework →What is the difference between T2 and T3 in the T-Model?
T2 (Free Users, 22%) are monthly active users on free tiers who haven't found a "worth paying for" scenario. T3 (Power Users, 15%) are daily/weekly active users who have formed an "ask AI first" habit and are the primary paying base. The T2 to T3 conversion is the biggest growth opportunity in AI products.
Explore the T-Model framework →Who are T4 Builders in AI adoption?
T4 Builders (~4% of US adults, ~10M people) are multi-tool users who work with API, agents, and custom solutions. They have the highest ARPU, shape industry discourse, and influence the other 96% of users. Software/IT occupations make up ~36% of Claude usage (Anthropic 2025).
Explore the T-Model framework →How does the T-Model apply to B2B marketing?
The T-Model reveals that T distribution varies wildly by industry. For example, in the lighting industry: electricians are T0 70-80% (reach via SEO + FAQ), distributors are T1-T2 ~60% (reach via GEO + comparison content), and DIY homeowners are T0-T3 scattered (reach via YouTube). One-size-fits-all marketing fails when tiers have different AI cognition levels.
Explore the T-Model framework →⚡ US Electrical & Lighting Market
Who decides which lighting brand gets used in commercial buildings?
Six roles shape the decision: Owner (writes the check), Architect (writes the spec), General Contractor (controls budget), Electrical Contractor (buys and installs), Distributor (gatekeeper at checkout), and Electrician (installs it). Electrical Contractors and Distributors hold the most real buying power, but they rarely search Google for products — making GEO a critical channel for reaching them.
Explore the decision chain →What is the size of the US electrical and lighting market?
The US building electrical market is approximately $312B, with residential remodel being the largest segment at $94B (+6.8% growth). Residential new is $68B, commercial new is $82B, commercial remodel is $47B, and industrial is $21B (+8.6% growth).
Explore the decision chain →How is AI changing lighting procurement?
AI enters the lighting procurement chain at four stages: (1) Design — AI matches real products to BIM models, creating brand lock-in before spec; (2) Bidding — AI reads spec PDFs and matches SKU databases; (3) Procurement — AI tracks delivery timelines and auto-replaces unstable supply; (4) Pre-install — AI reviews submittals line by line. Being written into AI-readable data streams (SKU databases, BIM libraries, compliance checkers) is becoming a competitive barrier.
Explore the decision chain →What is the difference between professional and DIY lighting markets?
The professional market (EC/Distributor-driven) has high loyalty, high barriers to entry, and buyers aged 50-65 who use traditional channels. T-Model distribution is T0-T1. The DIY/retail market is search-driven, low loyalty, buyers aged 25-50 with T2-T3 AI adoption. GEO is highly effective for DIY but low for professional. Mixing the two strategies produces mediocre results in both.
Explore the decision chain →How do electricians and distributors use AI differently?
According to the T-Model analysis: Electricians are T0 70-80% — almost no AI use, they search "how to install" and trust UL listings over brand ads, reachable via traditional SEO and FAQ Schema. Distributors are T1-T2 ~60% — they use AI to check product specs and compare prices, but won't pay for AI tools, reachable via GEO and product comparison content. One message cannot reach both.
Explore the decision chain →