Fashion & apparel case study: ₹40,000 to ₹76,679 monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at cash-on-delivery returns and cancellations, the brand’s main problem, on a smaller base. We tilt orders to prepaid with a prepaid discount and a cash-on-delivery charge that covers its cost. 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

   In this case study, a smaller fashion brand grows monthly revenue in 3 months, from ₹40,000 to ₹1.5L (3.8x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem in this scenario is cash-on-delivery returns and cancellations, with turning visits into orders close behind. Returned cash-on-delivery orders turn attributed revenue into a loss. No budget returns more than the store converts.

   How it works

   The prepaid offer shows on product pages and at checkout. The fix list goes to the brand's team in the opening weeks, ahead of any budget step.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We set the numbers on net sales after returns and cancellations, not on logged revenue. We log store orders every day beside what the ad platform claims.

   Why

   It did not report a return on ad spend, so the case study starts from what measured fashion stores of that size hold. Returns and cancellations cut into logged revenue before it reaches the bank. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   Each review opens with net sales against the month's number. Budget decisions are read off store numbers, not the ad platform alone.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to about a dozen new ads a month by the final month, with every format close to the account's return. We give each lead product its own campaign and read it weekly. We make trust the subject of the video: creator reels, customer feedback and founder-led clips. We buy creative in batches and give each batch a fixed read window before buying more.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Video built on trust was the format that held return in most measured accounts. A fixed window stops spend chasing a creative before it has been read.

   How it works

   Budget follows the products that sell. Weak UGC is swapped for founder-led video rather than scaled. 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 ₹1.5L as return allows: the budget climbs in steps while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. We pause styles with high return-to-origin and test replacement styles in their own campaigns. We recover abandoned checkouts with WhatsApp messages as traffic grows.

   Why

   Across Month 2 to Month 3, the budget climbs in steps, while return on spend holds. Return holds through these months because each budget step stops where return starts to slip. Returned cash-on-delivery orders turn attributed revenue into a loss.

   How it works

   High-RTO lines are paused and replacements tested separately. WhatsApp recovery runs alongside paid retargeting, not instead of it. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. The prepaid offer and the cash-on-delivery charge go live. Store orders and ad-platform revenue are checked side by side.

   • Revenue ₹39,451
   • ROAS 1.94x
   • Ad spend ₹20,353
2. Month 2

   Scaling begins: abandoned checkouts get WhatsApp follow-ups. Spend steps up, and return holds.

   • Revenue ₹55,183
   • ROAS 1.92x
   • Ad spend ₹28,711
3. Month 3

   A new batch goes live after the last one is read. Spend steps up, and return rises. The case study finishes short of ₹1.5L: the case study follows what the fastest tenth of our measured accounts reached. Each order costs about what it did while learning.

   • Revenue ₹76,679
   • ROAS 2.00x
   • Ad spend ₹38,407

1. 1

   Net sales ran below logged store revenue after returns, cancellations and tax.

2. 2

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

3. 3

   Launched a wedding-season catalogue campaign and a dedicated scaling campaign