Food & beverage case study: ₹2L to ₹4L monthly revenue in 12 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is return on ad spend, so the opening month on this smaller store goes there. We add cart-value offers that step up at set basket sizes to lift order value. We fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. 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.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 12 months, from ₹2L to ₹17L–₹18L (8.8x). 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 return on ad spend. Higher order value lowers the share of each order that goes to ads. Conversion rate caps what any ad budget can return.

   How it works

   The tiers follow real order values, not round numbers. Site fixes are handed over in the first weeks, before budget rises. Warm layers run beside prospecting, not instead of it.

2. 2

   Measurement & reviews · Month 1 to 12

   What we do

   We write month-by-month revenue numbers with the brand's team that climb from ₹2L to ₹17L–₹18L, and open every review with the month-to-date number against them. We log store orders every day beside what the ad platform claims. We set the numbers on net sales after returns and cancellations, not on logged revenue.

   Why

   No starting return on ad spend was given; the starting return is what measured food and beverage stores of that size hold. Written monthly numbers expose a slow month while there is still time to act. Platform attribution over-counts, so budget decisions sit on the store-side number.

   How it works

   The month's number is on the page at every review. Budget decisions are read off store numbers, not the ad platform alone. Each review opens with net sales against the month's number.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and UGC and creator video first, rising 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 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 put most of the budget behind one broad video campaign of the core products, including a customer testimonial.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Early spend buys learning, not scale. Broad delivery lets the algorithm find buyers for an everyday product.

   How it works

   Budget follows the products that sell. Spend steps up only after orders confirm at the low budget. A video that takes off gets the budget; tests that do not convert are cut. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 9

   What we do

   Through Month 2 to Month 9, we push toward ₹17L–₹18L: the budget climbs in steps while return holds, and Instagram Reels carries the most spend and Instagram Feed 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. We put seasonal collection campaigns in front of each festive and wedding peak.

   Why

   In Month 2 to Month 9 the budget climbs in steps, while return on spend holds. Four of these months stop their budget step before return slips too far. Return holds through these months because each budget step stops where return starts to slip. 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. Budget moves between regions as the calendar turns. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

5. 5

   Steady state · Month 10 to 12

   What we do

   From Month 10, we hold the gains and push toward ₹17L–₹18L only as far as return allows. We stop pushing budget once return has peaked. We keep a bank of ready creatives and rotate them in as ads tire.

   Why

   Growth slows from Month 10, still short of ₹17L–₹18L. Budget stays about level over these months. Return on spend fell from its peak month in most of our engagements; peaks do not hold. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Budget is held while return is at its best and cut back when it slips. The bank means a tired ad is replaced the week it tires.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and ad-platform revenue are checked side by side. The store fix list is live, with reviews on product pages and a prepaid offer.

   • Revenue ₹2L
   • ROAS 2.03x
   • Ad spend ₹96,849
2. Month 2

   The scaling phase opens: budget is reviewed against return before the next step. Return holds as the budget holds.

   • Revenue ₹2.1L
   • ROAS 2.06x
   • Ad spend ₹1L
3. Month 3

   Budget is held at the level where return peaked.

   • Revenue ₹2.2L
   • ROAS 2.16x
   • Ad spend ₹1L
4. Month 4

   Ads with falling click-through are swapped from the bank. Return dips as the budget steps up.

   • Revenue ₹2.7L
   • ROAS 2.03x
   • Ad spend ₹1.3L
5. Month 5

   A seasonal collection campaign takes a larger share of budget.

   • Revenue ₹3.2L
   • ROAS 1.92x
   • Ad spend ₹1.7L
6. Month 6

   Budget is reviewed against return before the next step. Budget rises to the point where return starts to give way: return rises as the budget rises.

   • Revenue ₹3.9L
   • ROAS 2.00x
   • Ad spend ₹1.9L
7. Month 7

   Each budget move is read against the return it bought. Budget rises to the point where return starts to give way: return holds as the budget holds.

   • Revenue ₹4L
   • ROAS 1.92x
   • Ad spend ₹2.1L
8. Month 8

   Budget is held at the level where return peaked.

   • Revenue ₹4L
   • ROAS 1.94x
   • Ad spend ₹2.1L
9. Month 9

   Products that do not sell are paused and budget moves to the winners.

   • Revenue ₹4L
   • ROAS 1.95x
   • Ad spend ₹2L
10. Month 10

    From here the work shifts to keeping ads fresh and bringing buyers back.

    • Revenue ₹4L
    • ROAS 1.90x
    • Ad spend ₹2.1L
11. Month 11

    Ads with falling click-through are swapped from the bank.

    • Revenue ₹4L
    • ROAS 1.95x
    • Ad spend ₹2L
12. Month 12

    A seasonal collection campaign takes a larger share of budget. Budget rises to the point where return starts to give way: return holds as the budget holds. The case study finishes short of ₹17L–₹18L: budget stops rising where return starts to slip. Each order costs about what it did while learning.

    • Revenue ₹4L
    • ROAS 1.94x
    • Ad spend ₹2.1L

1. 1

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

2. 2

   15 UGC videos, including regional-language cuts for the two best-selling states

3. 3

   Facebook Reels carries the largest share of spend in this industry's measured accounts.