Food & beverage case study: ₹20,000 to ₹30,013 monthly revenue in 2–3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with turning visits into orders, which the brand in this scenario named first, before anything else on this smaller store. We get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront. 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 2–3 months, from ₹20,000 to ₹1L (5x). 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, holds budget where return starts to slip, and does not force the number. The main problem in this scenario is turning visits into orders. No budget returns more than the store converts. A larger basket spreads the cost of each order over more revenue.

   How it works

   Site fixes are handed over in the first weeks, before budget rises. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We write month-by-month revenue numbers with the brand's team that climb from ₹20,000 to ₹1L, and open every review with the month-to-date number against them. 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. The brand in this scenario also wants a return on ad spend of 10x, and the case study finishes below it. Return falls as budget grows, so holding that return means a smaller budget and less revenue. The ad platform's own count runs high, so the store's count is the one that moves budget. A shortfall is caught in the month it happens, not at the end.

   How it works

   The sheet is read before each budget change. Written monthly numbers make each scaling decision explicit. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and UGC and creator video, building to about a dozen new ads a month by the final month, with every format close to the account's return. We separate the lead products into their own campaigns and review each one weekly. We run creator, customer-feedback and founder-led video. We put most of the budget behind one broad video campaign of the core products, including a customer testimonial.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Trust-carrying video held return in most measured accounts. Broad delivery lets the algorithm find buyers for an everyday product.

   How it works

   Losing products are paused within a week and budget moves to the winners. Low-quality UGC is pulled and founder-led video takes its place. The video that takes off is scaled; the tests around it are cut. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 as far toward ₹1L as return allows 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 tie every budget increase to return: we step up while it holds, and step back when it drops.

   Why

   Across Month 2 to Month 3, the budget rises gently, and return on spend dips. In two of these months the step is trimmed to the size return can hold. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. 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. Most of the final month's spend sits on Instagram Reels, then Facebook Reels. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   Learning month: tracking is checked against store orders and the first ads go live on a small daily budget. The cart-value tiers are live. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹19,394
   • ROAS 1.99x
   • Ad spend ₹9,751
2. Month 2

   Scaling begins: new creator and customer-feedback videos join the account. Only the part of the step that return supports is taken: with budget steps up, return on spend dips.

   • Revenue ₹26,026
   • ROAS 1.90x
   • Ad spend ₹13,734
3. Month 3

   Each budget move is read against the return it bought. Only the part of the step that return supports is taken: with budget steps up, return on spend holds. ₹1L is not reached in the time, because budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase. Against the 10x this scenario calls for, the case study finishes below.

   • Revenue ₹30,013
   • ROAS 1.87x
   • Ad spend ₹16,045

1. 1

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

2. 2

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

3. 3

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