Food & beverage case study: ₹1.5L to ₹10L monthly revenue in 2 months

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 put the website audit, the creative brief and the monthly media schedule in place before spend moves. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 2 months, from ₹1.5L to ₹10L (6.7x). 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. No single problem was named; the case study works from the figures instead. Without a brief, each creative and budget decision starts from scratch. A larger basket spreads the cost of each order over more revenue.

   How it works

   All three are shared with the brand's team before any budget step. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Week 1 to 9

   What we do

   We set the path from ₹1.5L to ₹10L as written weekly numbers, and read every review against the week so far. We track ad-platform revenue beside store orders in a shared daily sheet. We read results in three-to-four-day windows, since the case study runs week by week.

   Why

   The return on ad spend it reported sits above what measured food and beverage stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. A missed week shows up early instead of at the end of the case study. The ad platform's own count runs high, so the store's count is the one that moves budget.

   How it works

   Written monthly numbers make each scaling decision explicit. The sheet is read before each budget change. Each window is read on purchases and cost per purchase before the next move.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We lead the testing layer with video, catalogue ads and UGC and creator video, building to a few dozen new ads a month by the last four weeks, keeping carousel on a short leash because its return trails the account. We put most of the budget behind one broad video campaign of the core products, including a customer testimonial. The four learning weeks run 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

   An everyday product sells best when the algorithm is free to find its buyers. Spend in the learning phase pays for information. In most accounts we measured, video that carried trust held its return.

   How it works

   A video that takes off gets the budget; tests that do not convert are cut. Spend steps up only after orders confirm at the low budget. UGC that underperforms is replaced by founder-led video, not given more budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Week 5 to 9

   What we do

   We scale through Week 5 to Week 9 toward ₹10L as the budget climbs steeply and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We scale into the festive and wedding calendar with seasonal collection campaigns. We raise budget only while return on spend holds, and cut it when return drops.

   Why

   Across Week 5 to Week 9, the budget climbs steeply, and return on spend falls as it does. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Demand is seasonal and regional, so spend moves with the calendar.

   How it works

   Spend shifts into each season and between regions with the calendar. Budget follows return week to week instead of a fixed ramp. In the last four weeks, Instagram Reels takes the most spend and Instagram Feed the next most. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Week 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and platform revenue are reconciled in the shared sheet. Cart-value offers go live at checkout.

   • Revenue ₹36,264
   • ROAS 2.87x
   • Ad spend ₹12,656
2. Week 2

   First creative read: ads that do not convert are cut. Spend holds, and return holds.

   • Revenue ₹35,680
   • ROAS 2.77x
   • Ad spend ₹12,892
3. Week 3

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

   • Revenue ₹34,482
   • ROAS 2.65x
   • Ad spend ₹13,008
4. Week 4

   Learning phase closes: the buyer found and the winning creative picked.

   • Revenue ₹34,880
   • ROAS 2.68x
   • Ad spend ₹13,037
5. Week 5

   The scaling phase opens: budget is reviewed against return before the next step. Spend steps up, and return holds.

   • Revenue ₹39,703
   • ROAS 2.72x
   • Ad spend ₹14,621
6. Week 6

   A fresh round of creator and customer-feedback video goes live. Spend more than doubles, and return falls.

   • Revenue ₹74,169
   • ROAS 2.27x
   • Ad spend ₹32,709
7. Week 7

   A seasonal collection campaign takes a larger share of budget.

   • Revenue ₹1.9L
   • ROAS 2.17x
   • Ad spend ₹87,134
8. Week 8

   The budget decision is taken on the return the last step earned. Spend steps up sharply, and return dips. Revenue over the last four weeks passes the halfway point between ₹1.5L and ₹10L.

   • Revenue ₹3.4L
   • ROAS 1.93x
   • Ad spend ₹1.8L
9. Week 9

   New creator and customer-feedback videos join the account. Spend steps up, and return holds. Four-week revenue reaches ₹10L, the number this scenario calls for. Each order costs more by the end than it did while learning.

   • Revenue ₹4.3L
   • ROAS 1.95x
   • Ad spend ₹2.2L

1. 1

   Cost per order stayed well above what a low-price consumable can carry.

2. 2

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

3. 3

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