Fashion & apparel case study: ₹5.5L to ₹7.6L monthly revenue in 2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   We open with set-up work on the mid-sized fashion store, since the brand in this scenario named no single problem. We start with a website audit, a creative brief and a monthly media schedule in the first week. 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

   A mid-sized fashion brand grows monthly revenue in 2 months, from ₹5.5L to ₹20L (3.6x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The brand in this scenario named no single problem, so the case study starts from the numbers. Later budget calls need something written to be judged against. Without layers, retargeting quietly eats the prospecting budget.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. Each layer keeps its own budget line.

2. 2

   Measurement & reviews · Week 1 to 9

   What we do

   We set the path from ₹5.5L to ₹20L as written weekly numbers, and read every review against the week so far. We keep a shared daily sheet with the ad platform's revenue next to real store orders. We check the reported return on spend against store revenue before any budget moves.

   Why

   The return on ad spend it reported sits close to what measured fashion stores of that size hold, and the case study starts from it. A shortfall is caught in the week it happens, not at the end. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   Each budget step is argued against the written number. Budget decisions are read off store numbers, not the ad platform alone. Both returns are reviewed together each week.

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 a few dozen new ads a month by the last four weeks, with carousel watched closely since it returns less than the rest. The four learning weeks run with a deliberately small daily budget until orders prove the buyer. We buy creative in batches and give each batch a fixed read window before buying more. We separate the lead products into their own campaigns and review each one weekly.

   Why

   Early spend buys learning, not scale. Buying more before a batch is read means paying for guesses. Products that do not sell show up inside a week, not after a month of shared budget.

   How it works

   Only confirmed orders at the low budget unlock the next step. Batches that do not convert are cut; winners get the budget. Losing products are paused within a week and budget moves to the winners. 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 as far toward ₹20L as return allows as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. We let return decide budget: more while it holds, less when it slips.

   Why

   In Week 5 to Week 9 the budget climbs in steps, and return on spend falls as it does. Two of these weeks stop their budget step where return starts to slip too far. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. Budget follows return week to week instead of a fixed ramp. Instagram Reels carries the largest share of spend in the last four weeks, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Week 1

   First, the set-up: tracking is checked against store orders and the first ads go live on a small daily budget. The store-side return is now the one budget follows. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹1.3L
   • ROAS 2.87x
   • Ad spend ₹44,698
2. Week 2

   First creative read: ads that do not convert are cut. Return on spend rises while budget holds.

   • Revenue ₹1.3L
   • ROAS 3.00x
   • Ad spend ₹42,411
3. Week 3

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

   • Revenue ₹1.3L
   • ROAS 3.03x
   • Ad spend ₹41,822
4. Week 4

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

   • Revenue ₹1.3L
   • ROAS 3.12x
   • Ad spend ₹41,170
5. Week 5

   The scaling phase opens: the weekly product read pauses the products that are not selling. Return on spend dips while budget holds.

   • Revenue ₹1.3L
   • ROAS 2.96x
   • Ad spend ₹44,124
6. Week 6

   Bid-cap and cost-cap versions run beside open bidding. Return on spend dips while budget steps up.

   • Revenue ₹1.5L
   • ROAS 2.69x
   • Ad spend ₹57,367
7. Week 7

   Budget is reviewed against return before the next step.

   • Revenue ₹1.8L
   • ROAS 2.50x
   • Ad spend ₹72,169
8. Week 8

   A new batch goes live after the last one is read. Budget goes up only as far as return can carry it: return on spend dips while budget steps up.

   • Revenue ₹2.1L
   • ROAS 2.35x
   • Ad spend ₹88,958
9. Week 9

   Budget shifts to the products that sold this period. Budget goes up only as far as return can carry it: return on spend dips while budget holds. ₹20L is not reached in the time, because the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹2.2L
   • ROAS 2.24x
   • Ad spend ₹97,354

1. 1

   Cut budgets during a major marketplace sale and moved spend to niche premium audiences

2. 2

   Scaling spend raised cost per order and lowered return on spend.

3. 3

   We pause styles with high return-to-origin and test replacement styles in their own campaigns.