/images/ppid_59c68470-image-176750753143344543.webp)
What is the story about?
Quick commerce has been the most innovative development in India’s consumption/retail environment in recent memory. At its forefront is the visible, omnipresent Gig Worker community.
To understand better how the economics of this eco-system works, I decided to dive into all the publicly available information and a series of calls with sources at Instamart, Blinkit, Big Basket & Zepto. Conversations with a few delivery partners who came home for orders further helped understand the model in a way that would give an approximation of the system.
The purpose of this is to broadly understand the system and not go down to the last decimal. Each of the large players may vary in some parameter or the other, but by no more than a couple of percentage points.
Let's go down the rabbit hole to see what happens when you hit "Place Order" on your favourite QComm app.
What the platform gets
Depending on whether it is an inventory model (Blinkit owns the inventory and makes a gross margin) or market place (Instamart and Zepto charge a take rate to sellers on the platform), there are three major heads of revenue. Two are paid for by the sellers (brands and companies) and one by customers, i.e. you and I.
One is either a gross margin or take rate that varies between 15-18% of the order value. This is paid by customers, and calculated before any discounts are added (Net Order Value).
The second, which generally ranges between 3% and 5% of the order value, is the fees platforms charge brands for advertising/product placement on the app.
The third (and this is paid for by us, the customers) is typically a delivery & handling fee which amounts to roughly 2-3% of the Net Order Value. Increasingly, however, this is being waived by most apps if order values exceed ₹299. This is an exercise in pushing purchasing behaviour.
Q2 data from Blinkit & Instamart suggest Average Gross Order Values stand at ₹693 and ₹697 respectively, with Net Order Values being about 25% lower. For the sake of simplicity I’ve taken the base average order value at ₹600.
At this price, here’s what the QComm player's revenue looks like.
The Anatomy of Q-Commerce: Revenue per order
Where the platform spends
Typically, the platform has 3 major expenditure heads per order:
- Pay-out to Rider
- Dark Store Operations (Picking, Packing, Middle-Mile Logistics)
- Dark Store & Warehouse Rents
Rider Expenses, Dark Store Operations and Rent account for 85-90% of a platform’s fulfilment cost.
The other costs include that of a central customer support system, rider on-boarding charges, wastage/shrinkage expenses, and other fixed costs that are spread over the average order value.
What these platforms get versus what they spend here typically makes up for the contribution profit or loss for each of these companies.
Below the contribution level are 3 major expense heads: Talent, Marketing, and Tech. A few other corporate overheads also come in. For the sake of brevity, I’ll keep that bit out for now.
So here’s what the expense table looks like for a Qcomm player, and its resultant Contribution Profit/Loss. For reference, Blinkit’s Sep 2025 Contribution Profit margin was 3.7% of Gross Order Value (GOV) while Instamart made a Contribution Loss of 2.6%.
Our approximation model, at an AOV of ₹600, gets us to a 2% Contribution Loss.
The Anatomy of Q-Commerce: Expenses
What a gig worker gets
Typically, a gig worker is paid ₹40-50 per quick commerce delivery and ₹50-70 per order for food delivery. However, food deliveries are made over longer distances compared to Qcomm deliveries, so the earnings per hour for food & QComm end up being similar.
So the important metric to track here is Earnings Per Hour and not necessarily the payout per order.
Gig workers & company insiders tell me they typically end up making 1.8-2 food deliveries an hour, and 2.3-2.5 QComm deliveries per hour. This roughly translates to an hourly gross of ₹90-150/hour. Let's assume a fair approximation of fuel/electric and maintenance costs comes to 20% of gross earnings. This implies net earnings per hour of ₹70-120.
As per the companies, a gig worker on average, logs in for about 9-10 hours a day for 25-26 days a month. This works out to a monthly net earnings of ₹16,000-31,000. This doesn’t take into account the tips given to delivery partners.
While companies don’t penalise workers for late deliveries, there are incentives for daily targets. For example, some platforms offer an additional ₹50-100/order if the worker completes 10 orders in a day.
While most platforms offer insurance for the delivery partner, some even offer insurance cover for their immediate families as an added incentive for regular attendance.
The Anatomy of Q-Commerce: Gig worker earnings
When I asked a delivery partner what prompted him to take up gig work, the answer was insightful. “I’m not even a 10th Pass. For the same hours at any company as an office boy, I won’t get more than ₹12,000-13,000 per month. Here, I log in as per my own convenience and make twice the amount,” he said, adding, "All I need is a vehicle, an Aadhar card and a valid license.”
While Quick Commerce players tom-tom their gig-worker-friendly policies and how they’re the best among others, one did say on condition of anonymity that the hustle of gig workers makes them take short-cuts, “We deactivate gig workers mainly for two reasons. One, when they steal a customer's cash. On an average, the industry loses about ₹100 crores annually to gig workers who run away with cash collected at the time of delivery. The second is when they’re working on fake IDs or using someone else's license. A simple check for this is that delivery partners have to upload a selfie each day they log-in for work.”
Through conversations across the chain, I’ve figured out two things. One: The business models of Quick Commerce firms are based on a steady supply of these partners across the country. So these platforms have no option but to keep expanding their coverage and increase their pace of growth, given their valuation multiples and a tough competitive environment.
A higher payout to gig workers, who are the backbone of the business, would probably be prudent given the intense competition. For that, however, the supply of riders has to keep pace with demand. That's not happening. To demonstrate this, I've done a stress test of the model with higher rider charges a little further down this piece.
Two: all efficiency metrics boil down to a gig worker’s earnings per hour. A higher earnings per hour means better order frequency, which implies good dark store locations, a wider product assortment, and service fulfilment along with efficient route planning. More importantly, higher earnings per hour would keep a gig worker engaged with one app and take away the incentive to look for alternatives. That’s a business moat.
In the long run, given the demand for partners, I believe it does not make business sense for companies to run roughshod over these workers. Those who may be doing so for a few basis points of profit are actually borrowing from the future.
When I asked a delivery partner about rash driving, he said, “The more orders I deliver a day, the more money I make. I don’t think I’m risking my life.” One official I spoke to at a quick commerce firm had this to say: “We don’t ask any of them to break the rules or ride fast. Our average speed of riders is <25 km/h. No gig worker is doing anything that isn’t being done by others already. I see people in luxury cars drive on the wrong side to avoid going an extra 200 meters to take a U-turn. It’s easy to point fingers at gig workers because they’re distinctly visible.”
QComm business model: The drivers & the drags
The two biggest drivers of revenue are Average Order Value and Gross Margin/Take Rate. Companies are working towards pushing the AOVs higher through higher value products and a wider assortment, even as they look to increase margins or take-rates by pushing harder terms to new brands, and betting on in-house labels. Swiggy, for instance, offers some products under its in-house brand Noice.
Given the largely fixed nature of costs, an increase in AOV or Gross Margin/Take Rate results in faster improvement of contribution margins. For most of the industry, a gross average order value of ₹750 should result in a break-even at current rates.
Free Delivery above a low AOV is another important differentiator. As most players are incentivising customers by reducing the limit above which deliveries are free, this drastically reduces their shot at contribution profits. It would be interesting to get these players’ internal sensitivity analysis on how business is affected when the carrot of free delivery is removed. Delivery charges, while accounting for 2-3% of the AOV at higher levels, do tend to act as a psychological barrier for buyers.
The Anatomy of Q-Commerce
Similarly, the biggest drags for this model are payout to gig workers and dark store inefficiencies. The latter would be difficult to model as they are more granular, dynamic and case-specific. These are largely assumed to be controllable and therefore, the understanding is that most companies are using their best available tech and brains to keep it as tight as possible. The payout to gig workers, however, is market-determined. Any adverse change here due to market forces or any regulatory change could seriously impact profitability for the system.
Stress testing the QComm model
A stress test of the QComm model suggests that at current payout rates for gig workers, there is fragility when Average Order Values come in under ₹700. Blinkit and Instamart’s AOVs are near ₹700, and they’re either making a small contribution profit already, or just a few quarters away from doing so. Competitive intensity via deep discounting or free deliveries at low AOVs would be an important real-world differentiator between contribution profitability or loss. Such exercises are not sustainable in the long term.
Assuming all other things stay constant, a change in payouts to gig workers would make profitability that much harder to achieve for the system, even at higher Average Order Values.
For instance, at current costs, the model just about works at ₹700 Average Order Value. If gig worker payouts are increased by even ₹10 per order, the AOV would have to rise to ₹800 for a similar contribution margin. A further ₹10 hike would push the contribution profitability threshold further to ₹1,000 AOV.
Should AOVs fail to rise in keeping with higher gig worker payout, platforms may have to deal with substantial contribution losses, which would in turn lead to bigger dents in EBITDA and Net Incomes.
The Anatomy of Q-Commerce: Stress test
The Quick Commerce Model, while disruptive, has extremely fascinating unit economics. The next time someone asks you about it, you can use this as a ready reckoner.
Speaking for myself, the next time I place an order online I will certainly think about my order value and the cost of fulfilment. Also, I will ask my delivery partner how their day was, and what their earning per hour is looking like.
You must too!
To understand better how the economics of this eco-system works, I decided to dive into all the publicly available information and a series of calls with sources at Instamart, Blinkit, Big Basket & Zepto. Conversations with a few delivery partners who came home for orders further helped understand the model in a way that would give an approximation of the system.
The purpose of this is to broadly understand the system and not go down to the last decimal. Each of the large players may vary in some parameter or the other, but by no more than a couple of percentage points.
Let's go down the rabbit hole to see what happens when you hit "Place Order" on your favourite QComm app.
What the platform gets
Depending on whether it is an inventory model (Blinkit owns the inventory and makes a gross margin) or market place (Instamart and Zepto charge a take rate to sellers on the platform), there are three major heads of revenue. Two are paid for by the sellers (brands and companies) and one by customers, i.e. you and I.
One is either a gross margin or take rate that varies between 15-18% of the order value. This is paid by customers, and calculated before any discounts are added (Net Order Value).
The second, which generally ranges between 3% and 5% of the order value, is the fees platforms charge brands for advertising/product placement on the app.
The third (and this is paid for by us, the customers) is typically a delivery & handling fee which amounts to roughly 2-3% of the Net Order Value. Increasingly, however, this is being waived by most apps if order values exceed ₹299. This is an exercise in pushing purchasing behaviour.
Q2 data from Blinkit & Instamart suggest Average Gross Order Values stand at ₹693 and ₹697 respectively, with Net Order Values being about 25% lower. For the sake of simplicity I’ve taken the base average order value at ₹600.
At this price, here’s what the QComm player's revenue looks like.
The Anatomy of Q-Commerce: Revenue per order
(Average
Order Value: ₹600)
| Head | % Rate | Amount (₹) |
|---|---|---|
| Gross Margin / Take Rate | 15% | 90 |
| Brand Commission | 3% | 18 |
| Delivery + Other Fees | 2% | 12 |
| Total | 20% | 120 |
Where the platform spends
Typically, the platform has 3 major expenditure heads per order:
- Pay-out to Rider
- Dark Store Operations (Picking, Packing, Middle-Mile Logistics)
- Dark Store & Warehouse Rents
Rider Expenses, Dark Store Operations and Rent account for 85-90% of a platform’s fulfilment cost.
The other costs include that of a central customer support system, rider on-boarding charges, wastage/shrinkage expenses, and other fixed costs that are spread over the average order value.
What these platforms get versus what they spend here typically makes up for the contribution profit or loss for each of these companies.
Below the contribution level are 3 major expense heads: Talent, Marketing, and Tech. A few other corporate overheads also come in. For the sake of brevity, I’ll keep that bit out for now.
So here’s what the expense table looks like for a Qcomm player, and its resultant Contribution Profit/Loss. For reference, Blinkit’s Sep 2025 Contribution Profit margin was 3.7% of Gross Order Value (GOV) while Instamart made a Contribution Loss of 2.6%.
Our approximation model, at an AOV of ₹600, gets us to a 2% Contribution Loss.
The Anatomy of Q-Commerce: Expenses
(Average Order Value: ₹600)
| Head | % Rate | Amount (₹) |
|---|---|---|
| Dark Store Operations | 7% | 42 |
| Dark Store Rent | 4% | 24 |
| Rider Payout | — | 50 |
| Rider Onboarding / Training | — | 5 |
| Wastage / Shrinkage | 1% | 6 |
| Central Support Systems | — | 5 |
| Total Fulfilment Cost | — | 132 |
The Anatomy of Q-Commerce
| Head | Value |
|---|---|
| Average Order Value | ₹600 |
| Revenue per Order | ₹120 |
| Fulfilment Cost | ₹132 |
| Contribution Profit / Loss | ₹ -12 |
| Contribution Profit / Loss Margin | -2% |
What a gig worker gets
Typically, a gig worker is paid ₹40-50 per quick commerce delivery and ₹50-70 per order for food delivery. However, food deliveries are made over longer distances compared to Qcomm deliveries, so the earnings per hour for food & QComm end up being similar.
So the important metric to track here is Earnings Per Hour and not necessarily the payout per order.
Gig workers & company insiders tell me they typically end up making 1.8-2 food deliveries an hour, and 2.3-2.5 QComm deliveries per hour. This roughly translates to an hourly gross of ₹90-150/hour. Let's assume a fair approximation of fuel/electric and maintenance costs comes to 20% of gross earnings. This implies net earnings per hour of ₹70-120.
As per the companies, a gig worker on average, logs in for about 9-10 hours a day for 25-26 days a month. This works out to a monthly net earnings of ₹16,000-31,000. This doesn’t take into account the tips given to delivery partners.
While companies don’t penalise workers for late deliveries, there are incentives for daily targets. For example, some platforms offer an additional ₹50-100/order if the worker completes 10 orders in a day.
While most platforms offer insurance for the delivery partner, some even offer insurance cover for their immediate families as an added incentive for regular attendance.
The Anatomy of Q-Commerce: Gig worker earnings
| Orders per Hour | Payout per Order (₹) | Earnings per Hour (₹) | Cost (₹) | Net Earnings per Hour (₹) | Monthly Earnings (₹) |
|---|---|---|---|---|---|
| 1.5 | 50 | 75 | 15 | 60 | 14,040 |
| 2.0 | 50 | 100 | 20 | 80 | 18,720 |
| 2.5 | 50 | 125 | 25 | 100 | 23,400 |
| 3.0 | 50 | 150 | 30 | 120 | 28,080 |
When I asked a delivery partner what prompted him to take up gig work, the answer was insightful. “I’m not even a 10th Pass. For the same hours at any company as an office boy, I won’t get more than ₹12,000-13,000 per month. Here, I log in as per my own convenience and make twice the amount,” he said, adding, "All I need is a vehicle, an Aadhar card and a valid license.”
While Quick Commerce players tom-tom their gig-worker-friendly policies and how they’re the best among others, one did say on condition of anonymity that the hustle of gig workers makes them take short-cuts, “We deactivate gig workers mainly for two reasons. One, when they steal a customer's cash. On an average, the industry loses about ₹100 crores annually to gig workers who run away with cash collected at the time of delivery. The second is when they’re working on fake IDs or using someone else's license. A simple check for this is that delivery partners have to upload a selfie each day they log-in for work.”
Through conversations across the chain, I’ve figured out two things. One: The business models of Quick Commerce firms are based on a steady supply of these partners across the country. So these platforms have no option but to keep expanding their coverage and increase their pace of growth, given their valuation multiples and a tough competitive environment.
A higher payout to gig workers, who are the backbone of the business, would probably be prudent given the intense competition. For that, however, the supply of riders has to keep pace with demand. That's not happening. To demonstrate this, I've done a stress test of the model with higher rider charges a little further down this piece.
Two: all efficiency metrics boil down to a gig worker’s earnings per hour. A higher earnings per hour means better order frequency, which implies good dark store locations, a wider product assortment, and service fulfilment along with efficient route planning. More importantly, higher earnings per hour would keep a gig worker engaged with one app and take away the incentive to look for alternatives. That’s a business moat.
In the long run, given the demand for partners, I believe it does not make business sense for companies to run roughshod over these workers. Those who may be doing so for a few basis points of profit are actually borrowing from the future.
When I asked a delivery partner about rash driving, he said, “The more orders I deliver a day, the more money I make. I don’t think I’m risking my life.” One official I spoke to at a quick commerce firm had this to say: “We don’t ask any of them to break the rules or ride fast. Our average speed of riders is <25 km/h. No gig worker is doing anything that isn’t being done by others already. I see people in luxury cars drive on the wrong side to avoid going an extra 200 meters to take a U-turn. It’s easy to point fingers at gig workers because they’re distinctly visible.”
QComm business model: The drivers & the drags
The two biggest drivers of revenue are Average Order Value and Gross Margin/Take Rate. Companies are working towards pushing the AOVs higher through higher value products and a wider assortment, even as they look to increase margins or take-rates by pushing harder terms to new brands, and betting on in-house labels. Swiggy, for instance, offers some products under its in-house brand Noice.
Given the largely fixed nature of costs, an increase in AOV or Gross Margin/Take Rate results in faster improvement of contribution margins. For most of the industry, a gross average order value of ₹750 should result in a break-even at current rates.
Free Delivery above a low AOV is another important differentiator. As most players are incentivising customers by reducing the limit above which deliveries are free, this drastically reduces their shot at contribution profits. It would be interesting to get these players’ internal sensitivity analysis on how business is affected when the carrot of free delivery is removed. Delivery charges, while accounting for 2-3% of the AOV at higher levels, do tend to act as a psychological barrier for buyers.
The Anatomy of Q-Commerce
| Average Order Value (₹) | Contribution (%) |
|---|---|
| 350 | -9.10% |
| 450 | -5.50% |
| 500 | -4.00% |
| 550 | -3.00% |
| 600 | -2.00% |
| 650 | -1.20% |
| 700 | -0.60% |
| 750 | 0.00% |
| 800 | 0.50% |
| 1,000 | 2.00% |
Similarly, the biggest drags for this model are payout to gig workers and dark store inefficiencies. The latter would be difficult to model as they are more granular, dynamic and case-specific. These are largely assumed to be controllable and therefore, the understanding is that most companies are using their best available tech and brains to keep it as tight as possible. The payout to gig workers, however, is market-determined. Any adverse change here due to market forces or any regulatory change could seriously impact profitability for the system.
Stress testing the QComm model
A stress test of the QComm model suggests that at current payout rates for gig workers, there is fragility when Average Order Values come in under ₹700. Blinkit and Instamart’s AOVs are near ₹700, and they’re either making a small contribution profit already, or just a few quarters away from doing so. Competitive intensity via deep discounting or free deliveries at low AOVs would be an important real-world differentiator between contribution profitability or loss. Such exercises are not sustainable in the long term.
Assuming all other things stay constant, a change in payouts to gig workers would make profitability that much harder to achieve for the system, even at higher Average Order Values.
For instance, at current costs, the model just about works at ₹700 Average Order Value. If gig worker payouts are increased by even ₹10 per order, the AOV would have to rise to ₹800 for a similar contribution margin. A further ₹10 hike would push the contribution profitability threshold further to ₹1,000 AOV.
Should AOVs fail to rise in keeping with higher gig worker payout, platforms may have to deal with substantial contribution losses, which would in turn lead to bigger dents in EBITDA and Net Incomes.
The Anatomy of Q-Commerce: Stress test
(Contribution %)
| Payout per Order (₹) | ₹400 AOV | ₹500 AOV | ₹600 AOV | ₹700 AOV | ₹800 AOV | ₹1,000 AOV |
|---|---|---|---|---|---|---|
| 40 | -4.50% | -2.00% | -0.30% | 0.90% | 1.80% | 3.00% |
| 50 | -7.00% | -4.00% | -2.00% | -0.60% | 0.50% | 2.00% |
| 60 | -9.50% | -6.00% | -3.70% | -2.00% | -0.80% | 1.00% |
| 70 | -12.00% | -8.00% | -5.30% | -3.40% | -2.00% | 0.00% |
| 80 | -14.50% | -10.00% | -7.00% | -4.90% | -3.30% | -1.00% |
The Quick Commerce Model, while disruptive, has extremely fascinating unit economics. The next time someone asks you about it, you can use this as a ready reckoner.
Speaking for myself, the next time I place an order online I will certainly think about my order value and the cost of fulfilment. Also, I will ask my delivery partner how their day was, and what their earning per hour is looking like.
You must too!
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