Food & beverage case study: ₹2L to ₹2.5L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We set up the base for a smaller food and beverage store before any budget rises. We open with three documents: a website audit, a creative brief and a media schedule by month. We use cart-value tiers so larger baskets earn a better offer.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 3 months, from ₹2L to ₹20L (10x). 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 brand in this scenario named no single problem, so the case study starts from the numbers. Later budget calls need something written to be judged against. Higher order value lowers the share of each order that goes to ads.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We write month-by-month revenue numbers with the brand's team that climb from ₹2L to ₹20L, and open every review with the month-to-date number against them. We log store orders every day beside what the ad platform claims. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured food and beverage stores of that size hold. A shortfall is caught in the month it happens, not at the end. The ad platform's own count runs high, so the store's count is the one that moves budget.

   How it works

   Written monthly numbers make each scaling decision explicit. Every budget call starts from the store's orders. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and UGC and creator video first, rising to about two dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We run every lead product in a campaign of its own, read every week. We put most of the budget behind one broad video campaign of the core products, including a customer testimonial. We work in creative batches with a fixed read window each.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Broad delivery lets the algorithm find buyers for an everyday product. A fixed window stops spend chasing a creative before it has been read.

   How it works

   Losing products are paused within a week and budget moves to the winners. A video that takes off gets the budget; tests that do not convert are cut. Losing batches stop; winning ads take their budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 toward ₹20L as the budget rises gently and return dips, with Instagram Reels taking the largest share of spend and Facebook Reels the next. We front-load about a third of each month's budget into the first week. We let return decide budget: more while it holds, less when it slips.

   Why

   Across Month 2 to Month 3, the budget rises gently, and return on spend dips. Budget is part-stepped in two of these months, taking only what return can carry. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. Early-month demand is stronger for repeat consumables.

   How it works

   The monthly budget curve is weighted to the opening days. There is no fixed ramp; each month's budget follows the return of the last. In the final month, Instagram Reels takes the most spend and Facebook Reels the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and platform revenue are reconciled in the shared sheet. The website audit, creative brief and media schedule are shared.

   • Revenue ₹1.9L
   • ROAS 2.14x
   • Ad spend ₹88,803
2. Month 2

   The scaling phase opens: the budget decision is taken on the return the last step earned. The budget step stops where return starts to slip: budget steps up and return on spend falls.

   • Revenue ₹2.5L
   • ROAS 1.86x
   • Ad spend ₹1.3L
3. Month 3

   This batch's read is done: winners stay, the rest are cut. The budget step stops where return starts to slip: budget holds and return on spend holds. The case study finishes short of ₹20L: budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹2.5L
   • ROAS 1.90x
   • Ad spend ₹1.3L

1. 1

   Tested UGC for one product and a UGC lookalike audience, then cut both

2. 2

   Tracked Meta and Shopify side by side, day by day, in a shared sheet

3. 3

   Monthly review calls that start with what has not worked