Updated 23 September 2026. Every number on this page carries one of four provenance labels: Verified external (named third-party source), Lab calculation (arithmetic we ran, inputs stated), Lab convention (our operating standard, not an industry statistic), Illustrative (a realistic example, never a benchmark). The full reference is downloadable at the end.
Unit economics is the arithmetic of one order: what one sale leaves behind after every cost that rides on it. In India that arithmetic has three extra passengers most global guides skip: COD collection fees, a 7-15 day cash lag, and RTO. This page is the working reference: definitions, formulas, India reference numbers with provenance, a fully worked example, the CAC you can afford, LTV, break-even, and a sensitivity grid showing exactly when the model stops working.
Two numbers run the whole model. CM1 (net revenue minus COGS, shipping, payment fees and packaging) must clear 50-60% for an ads-led launch to breathe [Lab convention]. CM2 (CM1 minus acquisition cost) between 15-25% at steady state is what pays your fixed costs [Lab convention]. RTO is the silent third: at a 25% return rate it takes roughly ₹142 per delivered order off the table in our worked example [Lab calculation]. Positive CM2 after RTO drag is the honest definition of "profitable per order".
Definitions and formulas: the whole model in nine lines
- Net revenue = gross sales - discounts - returns.
- CM1 = net revenue - COGS - shipping - payment fees - packaging. Product viability, before any marketing.
- CM2 = CM1 - performance marketing CAC. What is left to pay fixed costs.
- Blended CAC = total marketing spend / total new customers, across all channels. The only CAC that cannot lie to you.
- RTO drag = rate/(1-rate) x (forward + reverse + packaging + burnt CAC), charged per DELIVERED order [Lab convention].
- CM2 after RTO = CM2 - RTO drag. The honest per-order profit in a COD market.
- Max affordable CAC before RTO = CM1 - (target CM2 share x AOV). Recalculate after allowing for RTO costs.
- LTV (12-month, margin-based) = expected orders in 12 months x CM1 per order. Margin-based, never revenue-based.
- Break-even orders = monthly fixed costs / CM2 per order.
India reference numbers, with provenance
| Metric | Number | Provenance | Source |
|---|---|---|---|
| Payment gateway fee | Razorpay standard platform fee: 2% + GST; zero MDR on standard bank-to-bank UPI does not remove gateway platform fees. Check your merchant plan. | Verified external | Razorpay published pricing and MDR/platform-fee explanation |
| COD collection fee | ₹25-50 or 1.5-2.5% per order | Verified external | aggregator rate cards (Shiprocket et al) |
| COD remittance lag | 7-15 days standard; 2-4 days early for a fee | Verified external | aggregator terms |
| Courier first slab (0-0.5 kg) | ₹30-45 zone-local rising to ₹65-85+ national | Verified external | Delhivery via ClickPost, 2026 |
| COD RTO band, unmanaged | 20-40% (fashion at the top); prepaid under 2-5% | Verified external | Shipway ShipNotes FY25 (Unicommerce) |
| COD share of Indian ecommerce | ~55-65% of orders, declining slowly | Verified external | industry trackers, 2026 |
| CM1 floor for an ads-led launch | 50-60% of net revenue | Lab convention | our operating standard across 100+ playbooks |
| CM2 target at steady state | 15-25% of net revenue | Lab convention | our operating standard |
| First-purchase CAC ceiling | 30-40% of AOV in replenishment categories; lower where repeat is weak | Lab convention | our operating standard |
| LTV:CAC minimum | 3:1 on margin-based LTV (never revenue-based) | Lab convention | our operating standard |
| RTO drag formula | rate/(1-rate) x (forward + reverse + packaging + burnt CAC) per delivered order | Lab convention | our costing convention (Margin Waterfall) |
The distinction in the third column is the point. The fee and RTO rows are externally verifiable facts. The CM1 floor, CM2 target, CAC ceiling and LTV:CAC minimum are our operating standards, built and stress-tested across 100+ category playbooks: adopt them or adapt them, but do not quote them as industry statistics, because that is not what they are.
The worked example [Illustrative]
One realistic skincare hero SKU. These inputs are chosen to be representative, not measured from any single brand; never read them as benchmarks. AOV ₹799, COGS 30% (₹240), shipping ₹90, gateway 2% (₹16), packaging ₹25:
- CM1 = 799 - 240 - 90 - 16 - 25 = ₹428 (53.6%). Clears the 50-60% convention floor.
- CM2 at ₹250 CAC = ₹178 (22.3%). Inside the 15-25% steady-state band.
- RTO drag at 25% COD RTO = 0.25/0.75 x (₹80+₹80+₹15+₹250) = ₹142 per delivered order.
- CM2 after RTO = ₹178 - ₹142 = ₹36 (4.5%). Still positive, barely: this is why RTO management is a P&L line, not an ops chore.
Judging profitability on platform ROAS. A 3x ROAS on a ₹799 AOV looks like winning until CM1 maths runs: after COGS, shipping, fees, packaging and the RTO drag, that 3x can be a per-order loss. The dashboards report revenue against ad spend; your bank account runs on CM2 after RTO. Track blended CAC and CM2 weekly and let ROAS be a creative-testing signal, not a P&L.
What CAC can you afford? [Lab calculation]
Work backwards from the CM2 you need, on the same illustrative SKU: max CAC = CM1 - (target CM2 share x AOV). Before RTO costs, holding a 15% CM2 target: ₹428 - (0.15 x ₹799) = ₹308. Holding 20%: ₹268. If Meta cannot deliver customers under that number after 3-4 weeks of disciplined testing, the fix is usually AOV (bundles) or COGS (supplier terms), not more ad spend; the testing method is in Meta ads for D2C and the pricing lever in how to price a product.
LTV and payback [Lab convention]
Our model is deliberately conservative: 12-month horizon, margin-based. LTV = expected orders in 12 months x CM1. On the illustrative SKU, a replenishment category doing 2.2 orders in year one: 2.2 x ₹428 = ₹942, an LTV:CAC of 3.8:1 against the ₹250 CAC, above the 3:1 convention minimum. Revenue-based LTV flatters every one of these numbers by 2x or more; never raise money or set CAC ceilings on it. Repeat rate is the lever: the mechanics are in customer retention for D2C.
Break-even [Lab calculation]
Break-even orders = monthly fixed costs / CM2 per order. The illustrative SKU against ₹40,000/month of fixed costs: 40,000 / ₹178 = 225 orders a month, about 8 a day. Use CM2 after RTO (₹36) and the same fixed costs need 1112 orders a month: roughly 5x the naive number. Both are correct arithmetic; only the second one predicts your bank balance in a COD-heavy category, and the gap between the two is the entire case for treating RTO as a P&L line with an owner.
Three unit-economics archetypes [Lab convention]
Categories cluster into three shapes, and the shape decides which number you watch first.
- Replenishment (skincare, ayurveda, supplements, coffee): moderate AOV, high repeat (30-40% achievable in skincare). First-purchase CM2 can run thin because LTV carries the model, but only once repeat is PROVEN on your own cohort data, never assumed on launch day.
- Fashion and returns-heavy (clothing, ethnic wear, footwear, sarees): healthy CM1 on paper, then the top of the RTO band (fashion runs 25-40% returns) takes it back. CM2 after RTO is the only number that matters here, and size-and-fit content is unit economics work, not marketing work.
- Considered, high-AOV (jewellery above ₹2,000, home decor, electronics): one-shot economics. No repeat rescue, so first-order CM2 must be healthy on its own, and volumetric weight often surprises the shipping input. Prepaid share is usually higher, which softens RTO.
The four distortions that flatter a broken model
Revenue-based LTV (counts turnover as value; always margin-based). Platform CAC instead of blended (Meta reports its wins, not your spend on everything else). Ignoring the COD collection fee and remittance lag (a ₹35 fee on a ₹499 order is 7% of revenue). And modelling GST on the tag price instead of transaction value, which misprices apparel near the ₹2,500 threshold. Every one of these makes a dying model look alive for one more quarter, which is exactly how founders end up scaling a loss.
Sensitivity: where the model breaks [Lab calculation]
CM2 after RTO drag as a share of AOV, on the illustrative SKU (CM1 ₹428), across the two levers you actually control. Bold is healthy (15%+), italic is the danger zone (under 5%):
| RTO rate ↓ / CAC → | ₹150 | ₹250 | ₹350 |
|---|---|---|---|
| 15% | 27.6% | 12.9% | -1.8% |
| 25% | 21.2% | 4.5% | -12.1% |
| 35% | 12.9% | -6.4% | -25.6% |
Read it as a map, not a prediction. At 15% RTO, a ₹350 CAC produces a loss of about 1.8% of AOV. At 35% RTO, a ₹150 CAC leaves about 12.9%, below our 15% convention target. This is why the same product with the same ads dies in one founder's hands and compounds in another's: the difference is rarely the creative, it is the RTO line; the playbook is how to reduce RTO on COD orders.
At Atomberg I watched revenue triple while the finance conversation stayed about the same three lines: contribution margin, working capital, returns. D2C founders inherit the same physics at a smaller scale. Every brand I have seen die with "great ROAS" died on CM2 after RTO; every one that compounded knew its break-even in orders per day and treated RTO as a P&L line with an owner. Model the unit before you scale the spend. I have never seen a brand fix a negative contribution per order with better creative. At a ₹350 CAC the model has to work before the campaign can.
Is your brand actually profitable? The five-minute diagnostic
- Compute CM1 on your hero SKU with real quotes, not list prices. Under 50%? Fix price, COGS or shipping before touching ads [Lab convention].
- Compute blended CAC from last month's actual spend and actual new customers. Not platform CAC.
- Apply the RTO drag formula with your real return rate. Subtract it from CM2.
- CM2 after RTO still positive? You are profitable per order. Now check break-even: fixed costs / CM2 after RTO = orders needed.
- Check CAC against max affordable CAC at a 15% CM2 target. Over it? The problem is the offer or the AOV, not the ad account.
- Re-run monthly. Fees, couriers and return rates drift; the model is only as honest as its inputs.
Methodology, sources and the data
Externally verified rows carry their named sources in the table: gateway and COD fee ranges per published aggregator and gateway pricing, courier slabs per Delhivery data via ClickPost (2026), RTO and prepaid bands per Shipway ShipNotes FY25 built on Unicommerce data. Conventions are our operating standards from 100+ fact-checked category playbooks and are labeled as such wherever they appear. Worked arithmetic uses the stated illustrative inputs. The sensitivity table and downloadable dataset should be checked together when assumptions change. This page pairs with how much it costs to start a D2C brand, which uses the same illustrative SKU, and with the courier comparison that sets the shipping input.
Download the reference: formulas, provenance-labeled reference numbers, the worked example and the sensitivity grid, free to reuse with attribution (CC BY 4.0): JSON · CSV. Cite d2c-acquisitionlab.com.
Margin Waterfall™: selling price minus product cost, packaging, shipping and gateway fee gives contribution per delivered order. Apply your blended RTO rate to get contribution per shipped order. Only then subtract CAC. Founders who skip the middle step scale a number that was never real.
Next action: run one real order through the waterfall
Put your own price, product cost, shipping, packaging and gateway fee in, then apply your real RTO rate before you look at CAC. Check each input at source: Razorpay publishes a standard 2% platform fee plus GST; standard UPI has zero MDR but can still incur this platform fee. Verify your merchant plan on Razorpay's pricing page, courier plans are published by NimbusPost and Delhivery, and ShipNotes FY25 puts COD RTO at about 26% against under 2% on prepaid. If contribution per shipped order comes out negative, stop and fix price or cost before spending another rupee on ads.
The conventions on this page come from Ravikant Tyagi's operating system, built across nine years of supply chain and D2C operations. If you'd like the complete execution system, including the 9 live calculators that run this exact arithmetic on your numbers, continue inside D2C Acquisition.Lab.
