Food & beverage case study: ₹50,000 to ₹1.1L monthly revenue in 4 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at scaling ad spend, the brand’s main problem, on a smaller base. 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 add cart-value offers that step up at set basket sizes to lift order value.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 4 months, from ₹50,000 to ₹3.5L (7x). That is faster than nine in ten of our measured accounts grew over the same time, so the case study below is on what that fastest tenth reached and shows where it lands against the number this scenario calls for. The main problem in this scenario is scaling ad spend. Without layers, retargeting quietly eats the prospecting budget. A larger basket spreads the cost of each order over more revenue.

   How it works

   Warm layers run beside prospecting, not instead of it. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 4

   What we do

   We set the path from ₹50,000 to ₹3.5L as written monthly numbers, and read every review against the month so far. We keep a shared daily sheet with the ad platform's revenue next to real store orders. 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. 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

   The month's number is on the page at every review. Every budget call starts from the store's orders. 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, with carousel watched closely since it returns less than the rest. We separate the lead products into their own campaigns and review each one weekly. The learning month runs on a small daily budget, stepping up only once orders confirm. We make trust the subject of the video: creator reels, customer feedback and founder-led clips.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Early spend buys learning, not scale. In most accounts we measured, video that carried trust held its return.

   How it works

   A product that does not sell is paused inside the week. Spend steps up only after orders confirm at the low budget. Weak UGC is swapped for founder-led video rather than scaled. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹3.5L as return allows: the budget climbs in steps and return falls, 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 make regional-language versions of the winning video for the states that buy most.

   Why

   Across Month 2 to Month 3, the budget climbs in steps, and return on spend falls as it does. Budget is part-stepped in one of these months, taking only what return can carry. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Buyers respond to the same idea in their own language.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Regional cuts run beside the original winner. Instagram Reels carries the largest share of spend in the final month, with Facebook Reels next. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 4

   What we do

   From Month 4, we protect the revenue already built and move toward ₹3.5L where return permits. We hold a creative bank and swap tired ads out before click-through falls. We show new arrivals to past buyers first, and build lookalikes from the ones who came back.

   Why

   From Month 4 growth slows while revenue is still under ₹3.5L. Spend still grows, at a slower pace than during scaling. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional. An order from a returning buyer costs the least.

   How it works

   The bank means a tired ad is replaced the week it tires. New arrivals go to past buyers first, before broad prospecting.

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 ad-platform revenue are checked side by side.

   • Revenue ₹48,329
   • ROAS 2.36x
   • Ad spend ₹20,439
2. Month 2

   The scaling phase opens: ads with falling click-through are swapped from the bank. Return dips as the budget steps up sharply.

   • Revenue ₹66,410
   • ROAS 2.16x
   • Ad spend ₹30,774
3. Month 3

   Products that do not sell are paused and budget moves to the winners. Budget is raised only as far as return allows: return dips as the budget steps up sharply.

   • Revenue ₹98,422
   • ROAS 1.93x
   • Ad spend ₹50,979
4. Month 4

   From here the work shifts to keeping ads fresh and bringing buyers back. Budget is raised only as far as return allows: return holds as the budget holds. The case study finishes short of ₹3.5L: budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹1.1L
   • ROAS 1.90x
   • Ad spend ₹55,708

1. 1

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

2. 2

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

3. 3

   We front-load about a third of each month's budget into the first week.