The decision engine for retail expansion.
For brands — national and international — opening physical stores in India. Every micro-market in your footprint, scored against a model tuned to your brand. Updated as the city moves.
Send us three of your stores. We’ll rank them blind.
One discovery call. Coordinates only. We return a blind ranking — best, worst, and what the score is sensitive to — without ever seeing your performance data.
Three stores. One ranking. Seventy-two hours.
Three coordinates.
Three live store locations. That’s the entire input — no profit-and-loss statements, no tenant mix, no secrets.
Your category’s model.
A blind ranking against the model built for your category — with no knowledge of how the stores actually perform.
Best, worst, why.
Which store should be strongest, which weakest, the reasoning behind each score — and an honest read of where we’re uncertain.
Most stores open in the wrong place. Then they close quietly.
Site selection in Indian retail still runs on three tools, and each one breaks the same way: the decision outruns the information.
The city you know runs out.
Expansion naturally follows the neighbourhoods a team knows best — a sensible instinct, and a small map. The next hundred good sites are mostly in places nobody on the team passes on their commute.
Paid once. Stale on arrival.
A site study takes months and lands as a PDF. By the time the lease is being negotiated, footfall, rents, and competitors have moved — and the study can’t move with them.
All the data. None of the model.
Footfall in one tab, rents in another, competitor screenshots in a folder. Nothing weighs anything against anything else — so the decision still gets made in the meeting, by voice.
Your brand, modeled. The city, scored. The shortlist, delivered.
Your brand, modeled.
Category, target customer, and how your best stores behave. We learn what “a great site for you” means — we don’t guess it.
The city, scored.
Every micro-market in scope, ranked against your model and re-scored as the city changes. The how stays our trade — the why behind every score is always shown.
The shortlist, delivered.
A shortlist worth visiting. Field-ready briefs. Reasoning your real-estate team can defend in a board meeting.
Delhi, scored for a specialty coffee brand.
A preview of the console with illustrative scores — what your team sees once your model is tuned. Pick a category and the whole city re-ranks.
Category
Middle Lane
What changes when the city is scored.
No borrowed case studies, no invented lift numbers — these are the commitments the engagement is built on.
Three ways to work with us.
Scoped per engagement, priced in conversation — not on a rate card.
- One metro, one retail category
- Candidate sites scored & ranked
- Signals refreshed as the city moves
- Field-ready briefs & audit log
- Every Marsh-live city
- Every candidate site in scope
- Continuous re-scoring as cities move
- Model weights tuned to your brand
- Quarterly review with the founders
- Signals extended with your data
- API & warehouse integration
- Dedicated analyst
- Data-room governance & audit
Built by Daedalus.
Daedalus is the parent company — the name on the card in your hand. Marsh is its first instrument: a working answer to where retail brands should open next in India.
We build quantitative infrastructure for decisions that Indian retail still makes on instinct and proximity. We’d rather be tested than trusted — which is why the blind analysis comes first, and the contract comes after.
Urban designer, Columbia University. Reads every blind analysis before it goes out.
Former product manager, Yahoo. Computer science, Purdue. Builds the model behind every score, and the why behind each.