What you get: A week-by-week quick-commerce launch plan with stock and margin worked out. Copy it, fill in the parts in [BRACKETS], and run it in Kriyā or any AI assistant.
You are a category manager who has launched brands on Indian quick-commerce apps. Plan my launch. Brand and product: [PRODUCT] Price and pack: [PRICE] Cities: [CITIES] Budget: [BUDGET] Give: 1. Which app first and why 2. Listing: what the platform takes (commission, listing fees, ads), and what it means for my margin 3. Stock: how many units for the first 30 days per dark-store cluster, and how I worked it out 4. Ads and in-app visibility: what to spend on in week 1, 2, 3, 4 5. The weekly numbers to watch (sell-through, out-of-stock, repeat) 6. When to add the second app Rules: - Use the India figures listed below and name the source and month next to each one you use. - If you need a figure that is not listed, write "Assumption:" and give your reasoning instead of a number you cannot source. - Plain words. Short sections with headings. No filler. India figures to use (from productgrowth.in/numbers): - India's quick commerce market is about Rs 1.15 lakh crore (US$ 13 billion) in FY26 (Google and Redseer). Source: https://productgrowth.in/numbers/quick-commerce-market-india/ - India had about 9,000 D2C brands in June 2023, the most recent published count (Redseer). Source: https://productgrowth.in/numbers/d2c-brands-india/
| Replace | With | Example (used for the sample answer) |
|---|---|---|
[PRODUCT] | Brand and product | A cold-brew coffee brand, 200 ml cans |
[PRICE] | Price and pack | ₹120 per can, packs of 4 at ₹440 |
[CITIES] | Launch cities | Bengaluru and Mumbai |
[BUDGET] | Launch budget | ₹10 lakh for the first quarter |
The prompt carries these figures so the answer starts from dated, sourced numbers instead of guesses. Each page shows the source, the month and the history.
Sample answer from Kriyā, 26 October 2026, for the example inputs above. Kept as it came back; links added beside each figure.
Our sources have no commission rates, margin data or dark-store velocity for quick commerce. So most numbers below are labelled Assumption: with reasoning. Verify each with your category manager (KAM) before committing the ₹10 lakh. Kriyā's estimate
Figures used: Quick commerce is about ₹1.15 lakh crore (US$13B) in FY26 (Google and Redseer, as listed on productgrowth.in/numbers). About 9,000 D2C brands existed in June 2023 (Redseer, the latest published count). That is a crowded shelf, so visibility will be your real cost. source Kriyā's estimate
Assumption: Start with Blinkit in Bengaluru and Mumbai. Reasoning: it is widely seen as the largest by orders, and cold coffee is an impulse and office-hours buy, so dense urban dark-store clusters matter more than reach. Swiggy Instamart is the likely second. Confirm current market share with the platform KAM; I have no sourced share data.
Assumption: I cannot source current commission, listing fees or ad terms. Typical negotiated ranges are 15-30% commission on FMCG, plus a new-brand listing or visibility fee. Ask for the rate card in writing. Kriyā's estimate
Worked margin on the ₹120 can (all assumed): your input
At ₹110 per can in the 4-pack (₹440), contribution drops to ~₹17. Ads must come out of this, so ads above ~12-15% of sales make you lose money per can. Check GST on beverages with your CA. your input Kriyā's estimate
Assumption: Velocity for a new, unknown brand is 3-5 cans per store per day. Not sourced; it is my planning figure.
Reserve ~₹4 lakh for ads; the rest covers stock, sampling and a buffer. Assumption: the split below. Kriyā's estimate
Add it when, for 3 consecutive weeks, you have sell-through above 60%, stockouts below 5% and ad spend within 15% of sales. Likely week 6-8. Before that you'd split a thin budget across two ranking battles. Kriyā's estimate
Next action: Ask the Blinkit KAM for the written rate card (commission, listing fee, ad minimums) this week, then rerun the margin math above. Which app are you already in talks with?
Beside each figure: source links the number page it comes from, your input means it came from the example inputs, and Kriyā's estimate marks a figure Kriyā worked out or assumed with no source given; check those before you use them. Your own run will differ.