Fashion & apparel case study: ₹15L to ₹27.8L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with creative and content, which the brand in this scenario named first, before anything else on this mid-sized store. We put best-sellers on product pages and lead with the one or two categories that already sell. We run short, dated bundle sales with an awareness lead-in instead of permanent discounts.

   Why

   A mid-sized fashion brand grows monthly revenue in 3 months, from ₹15L to ₹1Cr+ (6.7x). 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 creative and content, with marketing and social presence close behind. Lead categories give prospecting ads a proven entry point. A dated offer concentrates demand without training buyers to wait for a sale.

   How it works

   Lead categories carry the first scaling months. Awareness ads run ahead of each short sale window.

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 ₹15L to ₹1Cr+, 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. Platform attribution over-counts, so budget decisions sit on the store-side number. A shortfall is caught in the month it happens, not at the end.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The month's number is on the page at every review. Both returns are reviewed together each 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, keeping carousel on a short leash because its return trails the account. We lead with video that carries trust: creators, customer feedback and founder-led pieces. We commission ads in batches, and read each batch for a set window before ordering the next. We keep creators producing UGC video on a steady schedule, and cut the winners into regional languages.

   Why

   In most accounts we measured, video that carried trust held its return. Buying more before a batch is read means paying for guesses. Regular UGC keeps testing going, and a regional cut stretches a winning idea further.

   How it works

   Weak UGC is swapped for founder-led video rather than scaled. Losing batches stop; winning ads take their budget. Regional cuts of a winner come before new ideas. 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 ₹1Cr+ as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We test cost caps, bid caps and CBO against ABO side by side before each budget step. We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   Across 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. Spend is reduced and redirected for each event window. 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.

1. Month 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 ₹14.7L
   • ROAS 3.97x
   • Ad spend ₹3.7L
2. Month 2

   Scaling begins: regional-language cuts of the winning UGC go live. Return on spend falls while budget steps up sharply.

   • Revenue ₹21.8L
   • ROAS 3.34x
   • Ad spend ₹6.5L
3. Month 3

   Bid-cap and cost-cap versions run beside open bidding. Return on spend falls while budget steps up. ₹1Cr+ is not reached in the time, because 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 ₹27.8L
   • ROAS 2.85x
   • Ad spend ₹9.8L

1. 1

   We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

2. 2

   Tracked net sales beside Shopify revenue and set the numbers on the lower figure

3. 3

   Ran CBO and ABO versions side by side to see which held cost