Fashion & apparel case study: ₹1L–₹2L to ₹1.6L monthly revenue in 1.5–2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   The first problem in this scenario is results that swing from month to month, so the opening week on this smaller store goes there. 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. We map the festive and wedding calendar before spending, with seasonal collections ready in advance.

   Why

   In this case study, a smaller fashion brand grows monthly revenue in 1.5–2 months, from ₹1L–₹2L to ₹5L (3.3x). 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 results that swing from month to month, followed by return on ad spend. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Buyers in this category shop around festivals and weddings.

   How it works

   Warm layers run beside prospecting, not instead of it. Seasonal campaigns are prepared ahead of each peak.

2. 2

   Measurement & reviews · Week 1 to 9

   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–₹2L to ₹5L, and open every review with the week-to-date number against them. We reconcile the return on spend the brand in this scenario reports with store revenue before the first budget change.

   Why

   The return on ad spend it reported is below the level where raising budget pays for a store of that size, so budget does not rise until return climbs. The brand in this scenario also asked for a return on ad spend of 2.5–3x; the case study ends below that. Return falls as budget grows, so holding that return means a smaller budget and less revenue. 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

   Every budget call starts from the store's orders. Each budget step is argued against the written number. The store-side return becomes the number every review uses.

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. 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. The four learning weeks run with a deliberately small daily budget until orders prove the buyer.

   Why

   A fixed window stops spend chasing a creative before it has been read. Shared campaigns hide weak products; separate ones expose them fast. The first rupees are for finding the buyer, not for volume.

   How it works

   Batches that do not convert are cut; winners get the budget. Losing products are paused within a week and budget moves to the winners. Spend steps up only after orders confirm at the low budget. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Week 5 to 9

   What we do

   Through Week 5 to Week 9, we push as far toward ₹5L as return allows: the budget stays close to the learning level while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. We let return decide budget: more while it holds, less when it slips. We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   Across Week 5 to Week 9, the budget stays close to the learning level, while return on spend holds. Four of these weeks stop their budget step where return starts to slip too far. Each budget step here is sized so that return stays close to where it was. Marketplace sales inflate auction costs and pull price-led buyers away.

   How it works

   Budget follows return week to week instead of a fixed ramp. Spend is reduced and redirected for each event window. Instagram Reels carries the largest share of spend in the last four weeks, with Instagram Feed next. Cold audiences take most of the budget; warm audiences 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. The store-side return is now the one budget follows. Seasonal collections are prepared for the next peak.

   • Revenue ₹36,309
   • ROAS 1.94x
   • Ad spend ₹18,716
2. Week 2

   First creative read: ads that do not convert are cut. Budget holds and return on spend holds.

   • Revenue ₹35,000
   • ROAS 1.90x
   • Ad spend ₹18,377
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Revenue ₹33,871
   • ROAS 1.91x
   • Ad spend ₹17,766
4. Week 4

   By the end of learning, the buyer is known and one ad has won. Budget steps up and return on spend holds.

   • Revenue ₹37,409
   • ROAS 1.87x
   • Ad spend ₹19,972
5. Week 5

   The scaling phase opens: during a marketplace sale window, spend moves to narrower premium audiences. Budget holds and return on spend holds.

   • Revenue ₹36,955
   • ROAS 1.90x
   • Ad spend ₹19,399
6. Week 6

   Budget is reviewed against return before the next step. Only the part of the step that return supports is taken: budget steps up and return on spend holds.

   • Revenue ₹40,361
   • ROAS 1.87x
   • Ad spend ₹21,638
7. Week 7

   Products that do not sell are paused and budget moves to the winners. Only the part of the step that return supports is taken: budget holds and return on spend holds.

   • Revenue ₹40,795
   • ROAS 1.88x
   • Ad spend ₹21,687
8. Week 8

   A new batch goes live after the last one is read.

   • Revenue ₹41,646
   • ROAS 1.92x
   • Ad spend ₹21,743
9. Week 9

   During a marketplace sale window, spend moves to narrower premium audiences. Only the part of the step that return supports is taken: budget holds and return on spend holds. Revenue ends below ₹5L, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs about what it did while learning. Against the 2.5–3x this scenario calls for, the case study finishes below.

   • Revenue ₹41,526
   • ROAS 1.90x
   • Ad spend ₹21,877

1. 1

   Best-sellers on product pages, trousers and co-ords as lead categories, a monthly creative bank

2. 2

   Tracked Meta spend, revenue and Shopify orders daily in a shared sheet

3. 3

   Store revenue per rupee of ad spend fell from 8.2x to 5.5x as monthly spend nearly doubled