Six cities, fourteen dark stores, ₹8 Crore in monthly GMV. The business was growing. Investors were engaged. The blended unit economics showed a small positive contribution margin. Nobody had ever looked at a single store in isolation. When IUSP Partners built store-level economics for the first time, five of the fourteen were losing money on every order they fulfilled — quietly, invisibly, absorbed into the blended average.
Quick commerce is a hyper-local business. The economics of a dark store in Koramangala — dense, high-AOV, short delivery distances — are structurally different from a store in a traffic-heavy city-centre location where delivery times are long, operational costs are higher, and customer order sizes are driven by impulse rather than weekly shop. Blending them into a single P&L hides both the stars and the disasters.
"In quick commerce, the unit of analysis is not the company — it is the store. A company with 14 stores has 14 businesses. If you cannot read the P&L on each one, you are managing by hope."
IUSP Partners spent one week building the store-level contribution margin model — pulling order data, allocating costs by store, computing per-order economics for each of the 14 locations. The results across the portfolio:
| Store Cluster | Count | AOV | Cost/Order | CM/Order | Status |
|---|---|---|---|---|---|
| CM Positive | 5 stores | ₹520–680 | ₹340–410 | +₹28 to +₹58 | Scale candidates |
| CM Neutral | 4 stores | ₹440–510 | ₹410–470 | −₹5 to +₹5 | Watch list |
| CM Negative | 5 stores | ₹350–420 | ₹400–460 | −₹8 to −₹42 | Immediate action |
One store stood out as structurally unviable. Located in a dense urban market area — high foot traffic, small apartments, ultra-convenience demand — it was processing 190 orders per day at an AOV of ₹380. The true cost per order: ₹390. Loss: ₹10/order.
At 190 orders/day × ₹10 loss × 30 days: ₹57,000 lost every month from this single store — invisible in the blended P&L because it was averaged out against the high-performing stores. Over the 9 months the store had been operational: approximately ₹5.1 Lakh in cumulative losses, none of which had ever been reported as such.
The three root causes for the loss-making stores were consistent across the five:
Ultra-low AOV in dense areas: Customers in compact urban areas ordered 2-3 items — a pack of chips, a cold drink, some bread. AOV of ₹350-380. At this basket size, the fixed costs of dark store operations (picker salary, store rent, shrinkage) cannot be covered at any reasonable delivery fee.
High last-mile cost in high-traffic city centres: Delivery in central business districts took 22-28 minutes versus 12-16 minutes in residential areas. Longer delivery times = lower orders per delivery executive per hour = higher cost per delivery.
No minimum order requirement: The platform had no minimum order value. A customer ordering one item at ₹30 cost the company ₹360 to fulfil. This was not a rare edge case — below-₹200 orders represented 18% of volume at the worst-performing stores.
The minimum order value was the single highest-impact change. In the first two weeks, order volume at loss-making stores dropped 14% — the ultra-low-basket customers either upgraded their order, paid the platform fee, or did not order. The ones who paid the fee or upgraded their order improved unit economics immediately. The ones who left were the orders that were previously costing the company money to fulfil.
AOV moved from ₹380 to ₹510 across the loss-making store cohort within six weeks — partly through the minimum order effect, partly through the bundle strategy.
| Metric | Before (Blended) | After (6 Weeks) | Change |
|---|---|---|---|
| Blended CM/order | −₹10 | +₹18 | +₹28 |
| Average AOV (loss stores) | ₹380 | ₹510 | +34% |
| CM-negative stores | 5 of 14 | 0 of 13 | 5 turned profitable, 1 closed |
| Below-₹200 order % | 18% | 3% | −15pp |
| Avg delivery time (loss stores) | 24 min | 16 min | −8 minutes |
The most important insight from this engagement was not the loss-making stores themselves — it was that the company had been operating 14 distinct businesses for 9-18 months with no visibility into the economics of any individual one. In quick commerce, the store is the unit. Everything else is aggregation. Managing the aggregate without the unit is not managing at all.
If you operate multiple stores, locations, or fulfilment points without store-level P&Ls, you may be funding losses you cannot see. Let's build the visibility.
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