Food & beverage case study: ₹30,000–₹40,000 to ₹47,834 monthly revenue in 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. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive. 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 ₹30,000–₹40,000 to ₹20L (57.1x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The main problem was turning visits into orders, followed by marketing and social presence. Conversion rate caps what any ad budget can return. A larger basket spreads the cost of each order over more revenue.

   How it works

   Fixes are listed and handed over early, so later budget lands on a store that converts. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We log store orders every day beside what the ad platform claims. We agree a written number for every month on the way from ₹30,000–₹40,000 to ₹20L, and start each review with the month-to-date figure against it. We raise budget in a month only while return holds; where it slips too far, we hold 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. Ad platforms claim more orders than stores record, so the store number decides budget. A missed month shows up early instead of at the end of the case study.

   How it works

   Every budget call starts from the store's orders. Each budget step is argued against the written number. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to about a 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 lead with video that carries trust: creators, customer feedback and founder-led pieces. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Video built on trust was the format that 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. Weak UGC is swapped for founder-led video rather than scaled. A video that takes off gets the budget; tests that do not convert are cut. 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 Feed the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We front-load about a third of each month's budget into the first week.

   Why

   Across Month 2 to Month 3, the budget rises gently, and return on spend dips. In two of these months budget goes up only as far as return allows, so revenue grows more slowly than the brand's number needs. 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

   Budget follows return month to month instead of a fixed ramp. The monthly budget curve is weighted to the opening days. Instagram Reels carries the largest share of spend in the final month, with Facebook Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Store fixes go live: reviews, trust pointers and the prepaid offer. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹35,573
   • ROAS 2.13x
   • Ad spend ₹16,666
2. Month 2

   Scaling starts: a fresh round of creator and customer-feedback video goes live. Budget goes up only as far as return can carry it: return dips as the budget dips.

   • Revenue ₹44,051
   • ROAS 1.93x
   • Ad spend ₹22,814
3. Month 3

   Budget shifts to the products that sold this period. Budget goes up only as far as return can carry it: return holds as the budget holds. The case study finishes short of ₹20L: budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase.

   • Revenue ₹47,834
   • ROAS 1.88x
   • Ad spend ₹25,438

1. 1

   Refreshed tired ads by mixing old and new creatives; one product ad returned to 24.8x

2. 2

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

3. 3

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