What We Shipped
We just released GrowthFactor MCP — a direct connection between our site selection platform and the AI tools your team already uses.
If you use Claude, ChatGPT, or any LLM that supports the Model Context Protocol, you can now pull GrowthFactor data directly from a conversation. Demographics, foot traffic, competitive analysis, sales projections, cannibalization risk, analog matching — all of it, from a single prompt.
No new software to learn. No separate login. You ask your AI a question about a location, and it pulls real answers from real data.
First in site selection.
What It Actually Looks Like
Here is a real example. I opened Claude and typed:
Compare these 3 locations for our next store. Score them and check cannibalization.
1. 4325 Glenwood Ave, Raleigh, NC
2. 5085 S State St, Salt Lake City, UT
3. 14045 Abercorn St, Savannah, GA
What happened next took about 30 seconds.
The system geocoded all three addresses, then pulled demographics, foot traffic patterns, competitive density, comparable store matches, cannibalization risk, and nearby businesses for every location simultaneously. Not sequentially. All at once.
Once the data settled, the AI synthesized it into a structured comparison: Savannah scored highest on analog confidence and demographic fit. Raleigh had the fastest population growth. Salt Lake City had the highest overall score but the lowest projected revenue per square foot.
Then it built a branded comparison report — location cards with scores, bar charts across five evaluation lenses, sales projection ranges, a demographics table, and a cannibalization analysis. A document you could put in front of a committee.
One prompt. Thirty seconds. Three complete site evaluations with a presentation-ready deliverable.
Why This Matters More Than It Looks
The real shift is structural.
The evaluation bottleneck disappears
Today, a typical site evaluation takes 3-5 hours of analyst time. You're pulling demographics from one provider, foot traffic from another, competitive analysis from a third — then copying everything into a spreadsheet before you can even start thinking about the decision. That's why Books-A-Million analysts save 25 hours a week using GrowthFactor.
With MCP, that entire workflow collapses into a conversation. The AI orchestrates the data retrieval, runs the analysis, and produces the output. Your team spends time on the decision, not the data gathering.
It scales in a way manual workflows cannot
If it can evaluate three sites in 30 seconds, what does it look like at 50? At 100? At 1,000?
Franchise operators evaluating territory coverage across an entire state. Retailers screening every available pad in a metro area. Portfolio managers running cannibalization checks against their full existing footprint — all from a conversation.
The ceiling on how many sites you can evaluate is no longer limited by analyst hours. It is limited by how many questions you can ask.
The analysis is transparent
Every score, every projection, every recommendation traces back to specific data. The AI shows its work because GrowthFactor shows its work — the opposite of a black box where you get a number and have to trust it.
When your committee asks "why Savannah over Raleigh?" you don't get a shrug. You get the comparable store confidence score, the demographic fit rating, the projected sales range, and the specific stores that informed the model. All visible. All auditable.
Your team keeps their existing tools
MCP does not replace your workflow. It plugs into it. If your expansion team already uses Claude or ChatGPT for market research, competitive intelligence, or internal analysis, GrowthFactor data is now part of that same conversation.
No training. No migration. No IT deployment. The AI tools your team already has now have access to institutional-grade site selection intelligence.
What You Can Do With It
Here are concrete workflows that are now possible in a single conversation:
Quick screen a market. "Show me the top-scoring locations within 5 miles of downtown Austin with median HHI above $80K." Get scored results back in seconds instead of hours.
Compare finalists. Hand the AI your three finalist addresses and get a full side-by-side with projections. Walk into the committee meeting with a report, not a spreadsheet.
Check portfolio cannibalization. "If we open at this address, does it overlap with any of our existing stores?" The system checks against your full portfolio and returns overlap analysis with distance to nearest existing location.
Run analog matching. "Find stores in our portfolio that are demographically similar to this candidate site." Get the closest comparables with match confidence scores, and use them to anchor revenue projections.
Generate client deliverables. Brokers and advisors can produce branded comparison reports for their clients directly from a conversation. What used to take a day of assembly takes a minute.
Batch evaluate expansion targets. Feed a list of addresses and get every one scored, ranked, and summarized. Screen 50 sites before lunch.
How MCP Works (The Short Version)
The Model Context Protocol is an open standard that lets AI models call external tools during a conversation. Think of it like giving your AI a phone it can use to call specific services.
When you connect GrowthFactor MCP to your AI, you are giving it access to our full API — geocoding, scoring, demographics, foot traffic, analog matching, cannibalization, competitive analysis, and report generation. The AI decides which tools to call based on your question, executes them, and brings the results back into the conversation.
You do not need to know which API to call or how to format the request. You just ask a question in plain language and the AI handles the orchestration.
Frequently Asked Questions
What is the difference between GrowthFactor and Cherre for commercial real estate data?
Cherre is a commercial real estate data platform that aggregates property, ownership, and market data for institutional investors and portfolio managers. GrowthFactor serves a different use case, providing AI-powered site scoring, deal pipeline management, and expansion analytics specifically for multi-unit retailers. Where Cherre focuses on data aggregation and property intelligence, GrowthFactor turns location data into defensible site recommendations your committee can act on. Books-A-Million, the #2 book retailer in the US, tripled its new store openings on the same team using GrowthFactor, at 14.1% higher sales per square foot in those stores.
Getting Started
GrowthFactor MCP is available now.
Existing customers: Follow the setup guide for your AI tool, then approve access the first time your agent calls GrowthFactor. Setup takes five minutes, and you'll be pulling site data from Claude or ChatGPT the same day.
New to GrowthFactor? Start for $200/mo, or request a demo and we'll show you MCP in action with your own addresses, not a canned presentation.
We're expanding MCP capabilities based on early user feedback. If there's a workflow you want to see, tell us.