Margin
The costs nobody has put in the model — RTO, reverse freight, COD fees, the working-capital float.

Where Indian D2C brands lose money, and the arithmetic to find yours
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Every rupee passes through all seven between the ad click and your bank account. Each one is invisible in a platform report, and six of them are invisible in a P&L too.
The costs nobody has put in the model — RTO, reverse freight, COD fees, the working-capital float.
The largest marketing spend in most brands, and the only one that never appears in the marketing budget.
A fifth to a third of the traffic you just paid for, dying between the product page and the payment confirmation.
Budget split so thin across so many tests that the platform never optimises and every decision is a coin flip.
No engine behind the second order, in a market where first-order profit needs four things right at once.
Paying an algorithm to buy your customers using data you have quietly degraded.
Six leaks that sit between two functions each, so they belong to nobody and surface only as a crisis.
Two of the sixteen figures. The worksheets at the end of each part turn them into your own numbers, which is the entire method.


Written for you if
Not for you if
Twenty-five chapters, seven worksheets and four appendices, in the order money moves through a D2C business.
The thesis
Leak one · Margin
Leak two · Discount
Leak three · Store
Leak four · Acquisition
Leak five · Retention
Leak six · Signal
Leak seven · System
Putting it together
Appendices
Twelve numbers from your last complete month: sessions, add to carts, checkouts initiated, orders, gross revenue, total discount value, AOV, COD share, RTO rate, 90-day repeat rate, plus your ad spend and live ad set count. If you cannot produce the discount, RTO or repeat figure, that is itself a finding — three chapters are about exactly those.
Specific. COD and return-to-origin economics, UPI’s zero merchant discount rate, WhatsApp template pricing and the payment-mix maths are the spine of the book. Where no credible India-specific dataset exists, global figures are used and labelled as global rather than dressed up as local.
Every figure carries a grade next to it: [A] audited filings, platform documentation or peer-reviewed research; [B] real data from a company with something to sell; [C] commonly cited with no traceable source; [OWN] from engagements the author ran, always labelled as a sample of one business. Appendix B lists the eleven places where no credible published number exists, rather than inventing one.
Yes. Chapter two, “Shrinking efficiently”, is the full chapter as a PDF — the pattern where revenue falls while ROAS improves, and a five-month engagement where discount depth went from 45% to 16% and revenue went up 4.9×.
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