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CTO & Co-founder

Raj Shrimali

CTO and co-founder of GrowthFactor. MIT Sloan MBA. Writes about the data and machine-learning side of retail site selection: trade areas, gravity models, sales forecasting, and the systems that make them work at scale.

Raj runs engineering at GrowthFactor and co-founded the company with Clyde and Sam after MIT Sloan. He owns the data and machine-learning side: how scores are built, what goes into them, and what the score will not tell you.

GrowthFactor's scoring methodology is his work. So is most of what becomes a Labs model: trade areas that fit how a brand actually pulls customers, gravity models that admit overlap honestly, and sales forecasts with a band, not a single number.

He writes about the math. Where models help a real estate team, where they over-promise, and what changes when forecasting moves from a quarterly consulting deliverable to something the analyst can rerun on a Tuesday.

Recent posts by Raj

Why gym operators should forecast members, not just revenue

A new club's revenue is members times what each member spends. The site decides the first number and the operator decides the second, so a gym forecast that starts from members shows which one a miss came from.

Sep 23, 2026

How to find your next market by studying who already won there

Operators already trading in a market made their site decisions in public. Here is how to read those parcels into a screen for your own candidates, and where the method misleads.

Sep 21, 2026

Why your best-performing store is often in a market you almost skipped

Market screening cuts metros on population and income before a single trade area inside them is measured. Here is why the store from a market that nearly failed the screen so often turns out to be the best one in the fleet.

Sep 21, 2026

Does Foot Traffic Data Matter for a Gym or Trampoline Park?

Walk-by counts predict coffee shops. They do not predict a gym, a trampoline park, or a vet clinic. Here is where foot traffic data stops working for destination categories, and the four jobs it still does well.

Aug 21, 2026

Site Selection Software Accuracy: How to Back-Test a Score

A demo scores your worst store an 87. Here is why that happens, what it tells you about the model, and the back-test to run on any vendor before you buy.

Aug 12, 2026

What Is POI Data? Points of Interest, Explained

POI data is a structured record of physical places, each tagged with a name, category, coordinates, and attributes. Here's what it holds, where it comes from, and how it powers site selection.

Jul 17, 2026

Ask GrowthFactor where to open next

Watch it pull the data, run the analysis, and explain the answer in maps and tables. It does the analysis. You make the call.