Food & beverage case study: ₹30,000 to ₹36,446 monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is reaching new buyers, so the opening month on this smaller store goes there. We set basket-size offers that reward a second and third item in the cart. We map the festive and wedding calendar before spending, with seasonal collections ready in advance.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 6 months, from ₹30,000 to ₹15L (50x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem in this scenario is reaching new buyers. Higher order value lowers the share of each order that goes to ads. Demand in this category rises and falls with the festive calendar.

   How it works

   Offers are tuned to the order values that already sell. Seasonal campaigns are prepared ahead of each peak.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We set the path from ₹30,000 to ₹15L as written monthly numbers, and read every review against the month so far. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   With no return on ad spend reported, the case study begins at the level measured food and beverage stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed month shows up early instead of at the end of the case study.

   How it works

   The sheet is read before each budget change. Each budget step is argued against the written number. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and static image first, rising to about a dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. We give each lead product its own campaign and read it weekly. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   Broad delivery lets the algorithm find buyers for an everyday product. Products that do not sell show up inside a week, not after a month of shared budget. Early spend buys learning, not scale.

   How it works

   A video that takes off gets the budget; tests that do not convert are cut. Budget follows the products that sell. Spend steps up only after orders confirm at the low budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push toward ₹15L: the budget rises gently while return holds, and Instagram Reels carries the most spend and Facebook Reels the next. Once purchases are steady, we add lookalikes of past buyers next to broad prospecting. We cut regional-language versions of the winning video for the best-selling states.

   Why

   Across Month 2 to Month 4, the budget rises gently, while return on spend holds. Budget is part-stepped in three of these months, taking only what return can carry. Return holds through these months because each budget step stops where return starts to slip. Past buyers are the clearest signal of who buys next.

   How it works

   Lookalikes are read against broad on the same creative. Regional cuts run beside the original winner. Most of the final month's spend sits on Instagram Reels, then Facebook Reels. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we hold the gains and push toward ₹15L only as far as return allows. We show new arrivals to past buyers first, and build lookalikes from the ones who came back. We use WhatsApp for abandoned checkouts and for past buyers' next order.

   Why

   From Month 5 growth slows while revenue is still under ₹15L. Budget keeps rising, more slowly than in the scaling months. An order from a returning buyer costs the least. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   New arrivals go to past buyers first, before broad prospecting. Messages run beside retargeting ads.

1. Month 1

   Learning month: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and ad-platform revenue are checked side by side. The next festive collection is briefed and ready.

   • Revenue ₹30,446
   • ROAS 1.87x
   • Ad spend ₹16,295
2. Month 2

   The scaling phase opens: regional-language versions of the winner go live. The step is sized by what return can bear: as budget holds, return on spend holds.

   • Revenue ₹32,799
   • ROAS 1.93x
   • Ad spend ₹17,019
3. Month 3

   New arrivals go to past buyers first.

   • Revenue ₹34,870
   • ROAS 2.00x
   • Ad spend ₹17,397
4. Month 4

   Repeat-order messages go to past buyers.

   • Revenue ₹35,302
   • ROAS 1.94x
   • Ad spend ₹18,198
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back.

   • Revenue ₹35,792
   • ROAS 1.90x
   • Ad spend ₹18,859
6. Month 6

   The weekly product read pauses the products that are not selling. The step is sized by what return can bear: as budget holds, return on spend holds. Revenue ends below ₹15L, the number this scenario calls for, because budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase.

   • Revenue ₹36,446
   • ROAS 1.89x
   • Ad spend ₹19,329

1. 1

   Put 95% of the Meta budget behind videos of the core products, including a customer testimonial

2. 2

   Front-loaded 30–35% of each month's budget into the first eight days

3. 3

   Cost per order stayed well above what a low-price consumable can carry.