Fashion & apparel case study: ₹5L to ₹8.7L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at reaching new buyers, the brand’s main problem, on a mid-sized base. We run short, dated bundle sales with an awareness lead-in instead of permanent discounts. We map the festive and wedding calendar before spending, with seasonal collections ready in advance.

   Why

   A mid-sized fashion brand grows monthly revenue in 3 months, from ₹5L to ₹30L (6x). 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 reaching new buyers. A dated offer concentrates demand without training buyers to wait for a sale. Demand in this category rises and falls with the festive calendar.

   How it works

   Awareness ads run ahead of each short sale window. Seasonal campaigns are prepared ahead of each peak.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We agree a written number for every month on the way from ₹5L to ₹30L, and start each review with the month-to-date figure against it.

   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. 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. Written monthly numbers make each scaling decision explicit.

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 buy creative in batches and give each batch a fixed read window before buying more. We lead with video that carries trust: creators, customer feedback and founder-led pieces. We give each lead product its own campaign and read it weekly.

   Why

   The read window keeps creative spending tied to evidence. Video built on trust was the format that held return in most measured accounts. Products that do not sell show up inside a week, not after a month of shared budget.

   How it works

   Batches that do not convert are cut; winners get the budget. Weak UGC is swapped for founder-led video rather than scaled. Losing products are paused within a week and budget moves to the winners. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 as far toward ₹30L as return allows as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Once purchases are steady, we add lookalikes of past buyers next to broad prospecting. We scale into the festive and wedding calendar with seasonal collection campaigns.

   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. Past buyers are the clearest signal of who buys next.

   How it works

   Lookalikes are read against broad on the same creative. Spend shifts into each season and between regions with the calendar. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Month 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. Store orders and ad-platform revenue are checked side by side. Seasonal collections are prepared for the next peak.

   • Revenue ₹5.1L
   • ROAS 3.19x
   • Ad spend ₹1.6L
2. Month 2

   The scaling phase opens: the seasonal collection campaign leads this period's spend mix. Budget steps up and return on spend dips.

   • Revenue ₹6.5L
   • ROAS 2.83x
   • Ad spend ₹2.3L
3. Month 3

   Products that do not sell are paused and budget moves to the winners. Budget steps up and return on spend dips. ₹30L 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 ₹8.7L
   • ROAS 2.68x
   • Ad spend ₹3.2L

1. 1

   Rebuilt the account: a new-audience campaign excluding past audiences, past-buyer retargeting, one campaign per winning product

2. 2

   Run Google Search and Shopping beside Meta, and use Google's demand data to shape Meta.

3. 3

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