Food & beverage case study: ₹1L to ₹1.3L monthly revenue in 3 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 build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. 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 3 months, from ₹1L to ₹10L (10x). 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 was scaling ad spend. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Higher order value lowers the share of each order that goes to ads.

   How it works

   Retargeting sits next to prospecting and never replaces it. 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 write month-by-month revenue numbers with the brand's team that climb from ₹1L to ₹10L, and open every review with the month-to-date number against them. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   With no return on ad spend reported, the case study begins at the level measured food and beverage stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. A shortfall is caught in the month it happens, not at the end.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The month's number is on the page at every review. 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 a few dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We separate the lead products into their own campaigns and review each one weekly. We put most of the budget behind one broad video campaign of the core products, including a customer testimonial. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. An everyday product sells best when the algorithm is free to find its buyers. The first rupees are for finding the buyer, not for volume.

   How it works

   A product that does not sell is paused inside the week. The video that takes off is scaled; the tests around it are cut. Only confirmed orders at the low budget unlock the next step. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹10L: the budget rises gently and return dips, and Instagram Reels carries the most spend and Facebook Reels the next. We front-load about a third of each month's budget into the first week. We let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the budget rises gently, and return on spend dips. In two of these months the step is trimmed to the size return can hold. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Early-month demand is stronger for repeat consumables.

   How it works

   The monthly budget curve is weighted to the opening days. There is no fixed ramp; each month's budget follows the return of the last. Most of the final month's spend sits on Instagram Reels, then Facebook Reels. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each 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. Basket-size offers switch on at checkout. The layered account is in place, with retargeting beside prospecting.

   • Revenue ₹1L
   • ROAS 2.09x
   • Ad spend ₹48,649
2. Month 2

   The scaling phase opens: products that do not sell are paused and budget moves to the winners. The step is sized by what return can bear: budget steps up and return on spend dips.

   • Revenue ₹1.2L
   • ROAS 1.89x
   • Ad spend ₹65,691
3. Month 3

   Budget is reviewed against return before the next step. The step is sized by what return can bear: budget holds and return on spend holds. Revenue ends below ₹10L, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹1.3L
   • ROAS 1.92x
   • Ad spend ₹68,189

1. 1

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

2. 2

   Put most of the budget behind one broad video campaign of the core products, and scale a video that takes off.

3. 3

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