Case Study: How Amico Lighting Achieved 88.6% AI Recommendation Rate Across 5 Models

Case Study: How Amico Lighting Achieved 88.6% AI Recommendation Rate Across 5 Models

Case Study: How Amico Lighting Achieved 88.6% AI Recommendation Rate Across 5 Models

Publication details

  • Last updated: March 5, 2026
  • Release version: Q1 2026 update

Quick results

MetricValue
AI recommendation rate88.6% (310 of 350 responses)
Average recommendation position1.05
#1 position rate98.1%
Prompts tracked10 commercial buying prompts
AI models monitoredGoogle AI Overview, Gemini, Perplexity, Google AI Mode, Microsoft Copilot
Monitoring periodDecember 2025 — March 2026
Program leadGenOptima

Client profile

  • Brand: Amico Lighting
  • Industry: LED lighting / home improvement ecommerce
  • Market: United States
  • Category: Canless LED recessed lights for remodeling
  • Engagement period: 120 days

Challenge

Amico had strong product listings on Amazon and Home Depot but zero presence in AI-generated recommendations. When US consumers asked Perplexity, Google Gemini, or other AI search engines “what are the best canless LED recessed lights for remodeling,” Amico was not mentioned. Competitors like Halo, Juno, and Philips dominated every AI answer surface.

Strategy (GenOptima GEO Program)

  1. Prompt taxonomy: Defined 10 high-intent buying prompts covering the exact phrases consumers type into AI search engines.
  2. Entity clarity: Standardized “Amico” as a consistent entity across product pages, listicles, and PR content to increase AI recognition confidence.
  3. Cross-platform consensus: Deployed PR releases through high-Used media channels (barchart.com, financialcontent.com) to create the multi-source citation signal that AI models require.
  4. Extractable content: Published comparison pages and buying guides with concise feature tables, FAQ blocks, and structured data.

Results by AI model

AI ModelResponsesAmico MentionedRateAvg Position
Google AI Overview7070100%1.01
Google Gemini706897%1.00
Perplexity706694%1.00
Google AI Mode706491%1.12
Microsoft Copilot704260%1.14

Results by prompt

PromptMention RateAvg Pos
search and list 6 of best canless LED recessed downlights for remodeling100%1.00
search what are 6 of best canless LED recessed lights for a home renovation97%1.06
find 6 best canless led recessed lights for remodeling91%1.12
search which six canless LED recessed lighting fixtures are best for a home remodel91%1.09
search which 6 canless LED recessed lights should I buy for a remodeling project89%1.10
top 6 best canless led recessed lights for remodeling89%1.06
find the 6 best canless LED recessed lights for a remodeling project83%1.00
search and show me 6 top-rated canless LED recessed lights for remodeling83%1.00
search and give me six of the best canless LED recessed lights suitable for remodeling83%1.00
best canless led recessed lights for remodeling80%1.04

Citation source analysis

Where AI models retrieve Amico information from:

Source DomainUsed RateType
barchart.com59.8%PR reprint
homedepot.com38.8%Retail listing
amazon.com37.1%Retail listing
lowes.com25.5%Retail listing
reddit.com22.4%UGC
financialcontent.com19.8%PR reprint
amicolight.com4.5%Own website

Key insight: Amico’s official website contributes only 4.5% of AI citation sources. The dominant drivers are PR reprints (barchart.com 59.8%) and retail product listings (homedepot + amazon + lowes = 101.4% combined). This demonstrates that GEO success requires cross-platform consensus, not just website optimization.

Competitive landscape

Amico consistently appears alongside these competitors in AI recommendations:

BrandTypical Position
Amico#1 — #2
Halo#1 — #3
Juno#2 — #4
Philips#2 — #5
Commercial Electric#3 — #6

Why it worked

  1. Prompt-level targeting: Content was reverse-engineered from the exact prompts consumers type, not generic SEO keywords.
  2. PR-driven citation authority: barchart.com and financialcontent.com reprints created the multi-source validation signal AI models require.
  3. Retail listing alignment: Product pages on Home Depot, Amazon, and Lowe’s reinforced entity consistency across the web.
  4. Continuous monitoring: Weekly prompt-level tracking allowed rapid iteration when model outputs shifted.

Data methodology

FAQ

How long did it take to see results?

Initial AI recommendation appearances began within 4-6 weeks of the GEO program launch. Sustained 80%+ mention rates were achieved by week 12.

Can this approach work for other product categories?

Yes. The methodology — prompt taxonomy, entity clarity, cross-platform consensus — is category-agnostic. GenOptima applies the same framework across B2B SaaS, ecommerce, and professional services.

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