Food & beverage case study: ₹1L to ₹1L monthly revenue in 45 days

New ads per month

1. 1

   Research & offer · Week 1 to 2

   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 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 45 days, from ₹1L to ₹1.5Cr (150x). 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. No single problem was named; the case study works from the figures instead. Later budget calls need something written to be judged against. A larger basket spreads the cost of each order over more revenue.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Week 1 to 7

   What we do

   We log store orders every day beside what the ad platform claims. We write week-by-week revenue numbers with the brand's team that climb from ₹1L to ₹1.5Cr, and open every review with the week-to-date number against them. We set a written rule: no budget step in a week 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. The ad platform's own count runs high, so the store's count is the one that moves budget. Written monthly numbers expose a slow week while there is still time to act.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The week's number is on the page at every review. Where return starts to slip too far, the week keeps last week's budget.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to about two dozen new ads a month by the last four weeks, keeping carousel on a short leash because its return trails the account. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. We separate the lead products into their own campaigns and review each one weekly. The four learning weeks run at a low daily budget that moves up only when orders come through.

   Why

   An everyday product sells best when the algorithm is free to find its buyers. A product nobody buys is visible within a week when it has its own campaign. Spend in the learning phase pays for information.

   How it works

   The video that takes off is scaled; the tests around it are cut. Losing products are paused within a week and budget moves to the winners. The low budget stays until orders confirm the buyer. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Week 5 to 7

   What we do

   Through Week 5 to Week 7, we push toward ₹1.5Cr: the budget stays close to the learning level while return holds, and Instagram Reels carries the most 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 make regional-language versions of the winning video for the states that buy most.

   Why

   In Week 5 to Week 7 the budget stays close to the learning level, while return on spend holds. In three of these weeks budget goes up only as far as return allows, so revenue grows more slowly than the final number needs. Return holds through these weeks because each budget step stops where return starts to slip. Buyers respond to the same idea in their own language.

   How it works

   Each week's budget is set by the return the last one earned. Regional cuts run beside the original winner. In the last four weeks, Instagram Reels takes the most spend and Facebook Feed the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The audit, brief and media schedule are agreed with the brand's team. Basket-size offers switch on at checkout.

   • Revenue ₹23,306
   • ROAS 1.92x
   • Ad spend ₹12,109
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Spend holds, and return holds.

   • Revenue ₹23,004
   • ROAS 1.93x
   • Ad spend ₹11,948
3. Week 3

   Offers and store fixes switch on as the first orders confirm.

   • Revenue ₹23,488
   • ROAS 1.87x
   • Ad spend ₹12,545
4. Week 4

   By the end of learning, the buyer is known and one ad has won.

   • Revenue ₹24,065
   • ROAS 1.90x
   • Ad spend ₹12,646
5. Week 5

   The scaling phase opens: budget is reviewed against return before the next step. The step is sized by what return can bear: spend holds, and return holds.

   • Revenue ₹25,248
   • ROAS 1.91x
   • Ad spend ₹13,220
6. Week 6

   Regional-language versions of the winner go live.

   • Revenue ₹26,160
   • ROAS 1.93x
   • Ad spend ₹13,553
7. Week 7

   The weekly product read pauses the products that are not selling. The step is sized by what return can bear: spend holds, and return holds. The case study finishes short of ₹1.5Cr: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹26,054
   • ROAS 1.88x
   • Ad spend ₹13,835

1. 1

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

2. 2

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

3. 3

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