Fashion & apparel case study: ₹6L to ₹11L 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 growth that has stalled, so the opening month on this mid-sized 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 get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront.

   Why

   A mid-sized fashion brand grows monthly revenue in 3 months, from ₹6L to ₹10L–₹15L (2.1x). 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 in this scenario is growth that has stalled, with scaling ad spend close behind. Without layers, retargeting quietly eats the prospecting budget. No budget returns more than the store converts.

   How it works

   Retargeting sits next to prospecting and never replaces it. Site fixes are handed over in the first weeks, before budget rises.

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 ₹6L to ₹10L–₹15L, and open every review with the month-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 sits above what measured fashion stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. The ad platform's own count runs high, so the store's count is the one that moves budget. A missed month shows up early instead of at the end of the case study.

   How it works

   The sheet is read before each budget change. The month's number is on the page at every review. The reported return and the store-side return are read side by side every week.

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, with carousel watched closely since it returns less than the rest. 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 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. The first rupees are for finding the buyer, not for volume. A fixed window stops spend chasing a creative before it has been read.

   How it works

   Losing products are paused within a week and budget moves to the winners. Only confirmed orders at the low budget unlock the next step. Only ads that convert inside the window keep running. 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 as far toward ₹10L–₹15L 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. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. 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. In the final month, Instagram Feed takes the most spend and Instagram Reels the next most. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. Reported and store-side returns are reconciled. The layered account is in place, with retargeting beside prospecting.

   • Revenue ₹6L
   • ROAS 5.73x
   • Ad spend ₹1L
2. Month 2

   Scaling starts: bid-cap and cost-cap versions run beside open bidding. With budget steps up sharply, return on spend falls.

   • Revenue ₹8.5L
   • ROAS 4.97x
   • Ad spend ₹1.7L
3. Month 3

   Budget is reviewed against return before the next step. With budget steps up sharply, return on spend falls. Halfway from ₹6L to ₹10L–₹15L is passed. The case study finishes short of ₹10L–₹15L: the case study follows what the fastest tenth of our measured accounts reached. Each order costs more by the end than it did while learning.

   • Revenue ₹11L
   • ROAS 4.14x
   • Ad spend ₹2.7L

1. 1

   Tested each colour of the lead product in its own campaign

2. 2

   Meta in four layers: creative testing, scaling, mid-funnel and catalogue retargeting

3. 3

   Return on spend fell from its peak month in most engagements; peaks do not hold.