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We have no logos to show yet. So here is how we think.
Convra is new. We will not borrow someone else's case study, invent results, or paste logos we did not earn. What we can show you is our method — applied to three anonymised, generic D2C situations we see across the Indian market. Treat everything below as illustrative methodology, not reported outcomes.
T1A typical fashion D2C on Shopify, high COD share
The pattern: traffic is fine, add-to-cart is fine, but a large share of orders are cash-on-delivery and a meaningful slice never gets delivered. Revenue looks healthy in the dashboard and thin in the bank.
What we would look at first:
- Checkout COD friction. Whether COD is the path of least resistance because prepaid offers nothing in return. We model a prepaid incentive (small discount or free shipping) against the RTO cost it removes.
- RTO concentration. Which pincodes, price bands, and product categories drive most returns-to-origin, so the fix is surgical rather than a blanket COD ban that kills volume.
- Address & intent quality. Whether address capture and an order-confirmation step (OTP or a confirmation nudge) would filter low-intent COD orders before they ship.
- PDP-to-checkout honesty. Whether sizing, fabric, and delivery expectations are clear enough that the buyer who reaches checkout actually wants the parcel when it arrives.
What we would measure: prepaid share, RTO rate by cohort, and net delivered contribution per session — not just front-end conversion rate.
T2A beauty / personal-care brand with strong ad spend, weak checkout
The pattern: paid acquisition is the growth engine, blended CAC is climbing, and the team keeps buying more traffic to hit the same revenue. The leak is usually after the click, not before it.
What we would look at first:
- Checkout step analysis. Where sessions drop between cart, contact, shipping, and payment — and whether the drop is forced account creation, a surprise shipping fee, or a slow payment redirect.
- Mobile reality. How the checkout behaves on a mid-range Android over a patchy connection, since that is most of the real audience.
- Offer clarity. Whether discounts, bundles, and free-gift thresholds are legible at the moment of decision, or buried where they cannot do their job.
- Trust signals. Whether returns policy, authenticity, and delivery timelines are present where doubt actually occurs.
What we would measure: checkout completion rate by device and step, and revenue recovered per fixed leak — so spend can be reallocated instead of simply increased.
T3A repeat-purchase brand (food / supplements) leaking on retention
The pattern: first orders are won at a loss, the product is genuinely good, but second and third orders do not happen often enough to make the economics work. The problem is rarely acquisition; it is the gap after the first parcel.
What we would look at first:
- Repeat-rate cohorts. What share of first-time buyers return within their natural reorder window, and where that curve flattens.
- Reorder friction.Whether reordering takes more taps than it should, and whether a subscription or one-tap repeat would fit the product's consumption rhythm.
- Post-purchase flow. Whether the window between delivery and the next decision is used at all — and whether the message earns the next order rather than nagging for it.
- Lifetime-value math. Whether CAC is judged against first-order revenue or against realistic delivered LTV, because that single choice changes what spend is sane.
What we would measure: 60- and 90-day repeat rate, delivered LTV by cohort, and contribution after the second order.
These are frameworks, not forecasts. The real numbers only exist once we look at your store, your orders, and your funnel — which is exactly what the free audit does.