Food & beverage case study: ₹20,000 to ₹22,384 monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is keeping return on spend while scaling, so the opening month on this smaller store goes there. We split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. 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 ₹20,000 to ₹6L (30x). 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 was keeping return on spend while scaling, followed by scaling ad spend. Without layers, retargeting quietly eats the prospecting budget. Higher order value lowers the share of each order that goes to ads.

   How it works

   Warm layers run beside prospecting, not instead of it. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We agree a written number for every month on the way from ₹20,000 to ₹6L, 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. Platform attribution over-counts, so budget decisions sit on the store-side number. A shortfall is caught in the month it happens, not at the end.

   How it works

   The sheet is read before each budget change. The month's number is on the page at every review. 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 catalogue ads, video and static image, building to a handful of new ads a month by the final month, with every format close to the account's return. We run every lead product in a campaign of its own, read every week. The learning month runs with a deliberately small daily budget until orders prove the buyer. We work in creative batches with a fixed read window each.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Spend in the learning phase pays for information. A fixed window stops spend chasing a creative before it has been read.

   How it works

   Budget follows the products that sell. The low budget stays until orders confirm the buyer. Batches that do not convert are cut; winners get the budget. New ads rise with the budget, most of them catalogue ads, then video.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹6L: the budget rises gently while return holds, and Instagram Reels carries the most spend and Facebook Reels 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, while return on spend holds. In two of these months the step is trimmed to the size return can hold. Each budget step here is sized so that return stays close to where it was. Early-month demand is stronger for repeat consumables.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. The monthly budget curve is weighted to the opening days. 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

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The layered account is in place, with retargeting beside prospecting. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹18,624
   • ROAS 1.90x
   • Ad spend ₹9,806
2. Month 2

   The scaling phase opens: the budget decision is taken on the return the last step earned. Budget is raised only as far as return allows: budget holds and return on spend holds.

   • Revenue ₹20,401
   • ROAS 1.92x
   • Ad spend ₹10,631
3. Month 3

   The weekly product read pauses the products that are not selling. Budget is raised only as far as return allows: budget holds and return on spend holds. Revenue ends below ₹6L, 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 ₹22,384
   • ROAS 1.92x
   • Ad spend ₹11,687

1. 1

   Kept remarketing on add-to-cart and checkout audiences (11.2x)

2. 2

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

3. 3

   Moved a third of spend into one broad video campaign at 3.6x