CASE STUDY
How a Local Retailer Achieved 51.7% Recommendation-Level AI Visibility
A family-owned business with dual service lines.
118 years in operation. Zero AI optimization before this.
The business
A family-owned jewelry and eyewear retailer in the Kansas City metro area. In business since 1908. Two distinct service lines: traditional jewelry (engagement rings, repairs, custom design) and optical (eyeglasses, frames, repairs).
They had a website. They had a Google Business Profile. They had decades of customer reviews.
What they didn't have: any idea how AI search engines perceived them.
The question
Their customers were increasingly using AI assistants to find local services. "Where can I get my ring resized?" "Best place for engagement rings near [city]?" "Eyeglass repair near me."
Were they showing up in these AI-powered searches? Were they being recommended, or just mentioned in a list? Were they invisible entirely?
They had no way to know.
The audit
We tested 312 queries across multiple AI platforms, covering:
- • 11 geographic zones (from hyper-local to regional)
- • 23 service categories (jewelry repair, engagement rings, eyewear, etc.)
- • 3 query variants (direct, conversational, detailed)
We classified every response using the M/C/R framework: Mentioned, Cited, or Recommended.
The findings
19.1%
Organic Visibility
Nearly 1 in 5 local queries surfaced this business — without asking for them by name.
51.7%
Recommended Rate
Over half of their appearances were explicit endorsements, not just mentions.
30
Queries Endorsed
30 different query types triggered an AI recommendation for this business.
VISIBILITY VALUE DISTRIBUTION
Of the 58 times AI surfaced this business:
For comparison: most local businesses we've measured have RECOMMENDED rates below 20%.
This client was at 51.7%. Something was working.
What we learned
THE DUAL SERVICE LINE ADVANTAGE
The combination of jewelry and eyewear created a competitive moat. Six eyewear-related queries surfaced this business organically — competitors who only sell jewelry couldn't capture these.
Unique positioning translates to unique visibility.
LOCAL QUERIES DOMINATED
Hyper-local queries ("near Belton MO") dramatically outperformed regional queries ("Kansas City metro"). The business was virtually invisible for broad queries, but strong for specific local searches.
Focus beats breadth.
SIMPLE QUERIES OUTPERFORMED
Direct, simple query phrasings generated 2.4x higher visibility than complex "expert" phrasings. AI responds better to how normal people talk.
Don't overcomplicate.
REPAIR SERVICES DROVE RECOMMENDATIONS
Queries about jewelry repair, ring resizing, and watch batteries generated the highest RECOMMENDED rates. The AI specifically cited customer reviews about "handling difficult repairs other jewelers decline."
Reputation echoes.
Platform breakdown
| Platform | Organic Visibility | Recommended Rate |
|---|---|---|
| Perplexity | 19.1% | 51.7% |
| ChatGPT | 0%* | — |
| Gemini | Testing planned | — |
| Claude | Testing planned | — |
*ChatGPT showed 0% organic discovery — only responded to direct brand queries. 100% brand recognition when asked directly, but zero unprompted recommendations.
This highlights why multi-platform testing matters. A business could look invisible on one platform and highly visible on another.
The path forward
Based on the audit findings, we identified specific optimization opportunities:
- • Strengthen visibility for engagement ring queries (high intent, currently underperforming)
- • Expand geographic coverage to capture more Cass County and South KC queries
- • Address ChatGPT's complete lack of organic discovery through structured data improvements
- • Lean into the repair services positioning that AI already recognizes
The client now has a clear map of where they're strong, where they're weak, and what to prioritize.
Get your own audit
This business had no idea AI was recommending them at a 51.7% rate. They also didn't know they were invisible on ChatGPT.
What don't you know about your AI visibility?