Rec Nation case study: ~5x growth since the engagement began

The Challenge

What Rec Nation was up against.

Rec Nation operates across dozens of states, each with its own buyer pool asking local questions. Answering those one page at a time by hand was never going to work at that footprint, and a single national page cannot answer a question about storage in one specific metro.

Multi-state coverage is unforgiving. Every state has a different competitor set and a different set of queries, so the work has to be systematic or it does not scale past the first few markets.

The Approach

How we attacked it.

A programmatic build. Rather than writing state pages one by one, we generated the location and product coverage systematically, using prompt research to decide what each page had to answer before it was built.

Programmatic buildout of location and product pages across the full state footprint
Prompt research driving the page structure, so each page answered the questions buyers actually ask for that market
Coverage of product-related and location-based queries together, rather than treating them as separate problems
Local entity reinforcement and structured data per state page
Per-state tracking, so wins in one market could be pushed into the rest

Results

What changed.

~5x

Growth since 2024

23+

States ranked in first 60 days

2 yr+

Ongoing engagement

#2

Gemini: Texas RV storage

See it yourself

Named in 1 engine

Gemini

These are real AI answers naming Rec Nation. The exact prompt is shown above each one so you can run it yourself, and where the engine allows it you can open the original answer at the source.

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