Fashion & apparel case study: ₹7L to ₹12.5L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is keeping return on spend while scaling, so the opening month on this mid-sized store goes there. We layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive.

   Why

   A mid-sized fashion brand grows monthly revenue in 3 months, from ₹7L to ₹30L (4.3x). 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 main problem was keeping return on spend while scaling. Without layers, retargeting quietly eats the prospecting budget. No budget returns more than the store converts.

   How it works

   Each layer keeps its own budget line. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We log store orders every day beside what the ad platform claims. We set the path from ₹7L to ₹30L as written monthly numbers, and read every review against the month so far.

   Why

   It did not report a return on ad spend, so the case study starts from what measured fashion stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. Written monthly numbers expose a slow month 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.

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 give each lead product its own campaign and read it weekly. The learning month runs on a small daily budget, stepping up only once orders confirm. We work in creative batches with a fixed read window each.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Early spend buys learning, not scale. Buying more before a batch is read means paying for guesses.

   How it works

   A product that does not sell is paused inside the week. Spend steps up only after orders confirm at the low budget. Losing batches stop; winning ads take their budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹30L as return allows: the budget climbs in steps and return falls, and Instagram Feed carries the most spend and Instagram Reels 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 Month 2 to Month 3 the budget climbs in steps, and return on spend falls as it does. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. Each month's budget is set by the return the last one earned. Most of the final month's spend sits on Instagram Feed, then Instagram Reels. Most spend reaches people who have not bought yet; past visitors return more per rupee.

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 platform revenue are reconciled in the shared sheet. The store fix list is live, with reviews on product pages and a prepaid offer.

   • Revenue ₹6.7L
   • ROAS 2.86x
   • Ad spend ₹2.3L
2. Month 2

   The scaling phase opens: each budget move is read against the return it bought. Spend steps up sharply, and return falls.

   • Revenue ₹9.4L
   • ROAS 2.40x
   • Ad spend ₹3.9L
3. Month 3

   A new batch goes live after the last one is read. Spend steps up, and return dips. The case study finishes short of ₹30L: the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹12.5L
   • ROAS 2.14x
   • Ad spend ₹5.8L

1. 1

   Keep Video as the main format: it carried most creative spend and also reached the top creative tier most often in this industry's measured accounts.

2. 2

   Find the buyer on a small daily budget first, then step spend up in stages.

3. 3

   Track ad-platform revenue beside store revenue (and net sales) daily or weekly, and set the numbers on the lower figure.