Fashion & apparel case study: ₹4.5L to ₹8.7L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with results that swing from month to month, which the brand in this scenario named first, before anything else on this smaller store. We build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. We mark the festive and wedding dates first and have seasonal collections ready ahead of them.

   Why

   In this case study, a smaller fashion brand grows monthly revenue in 3 months, from ₹4.5L to ₹20L (4.4x). 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 was results that swing from month to month, followed by return on ad spend. Without layers, retargeting quietly eats the prospecting budget. Demand in this category rises and falls with the festive calendar.

   How it works

   Warm layers run beside prospecting, not instead of it. Seasonal campaigns are prepared ahead of each peak.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We agree a written number for every month on the way from ₹4.5L to ₹20L, and start each review with the month-to-date figure against it. We keep a shared daily sheet with the ad platform's revenue next to real store orders. We reconcile the return on spend the brand in this scenario reports with store revenue before the first budget change.

   Why

   Its reported return on ad spend is higher than measured fashion stores of that size hold, so the case study starts there and lets part of it ease as budget grows. Written monthly numbers expose a slow month while there is still time to act. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   Each budget step is argued against the written number. Budget decisions are read off store numbers, not the ad platform alone. 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 buy creative in batches and give each batch a fixed read window before buying more. We run every lead product in a campaign of its own, read every week. We lead with video that carries trust: creators, customer feedback and founder-led pieces.

   Why

   A fixed window stops spend chasing a creative before it has been read. Products that do not sell show up inside a week, not after a month of shared budget. In most accounts we measured, video that carried trust held its return.

   How it works

   Batches that do not convert are cut; winners get the budget. A product that does not sell is paused inside the week. Weak UGC is swapped for founder-led video rather than scaled. 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 ₹20L 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. We tie every budget increase to return: we step up while it holds, and step back when it drops. 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. Marketplace sales inflate auction costs and pull price-led buyers away.

   How it works

   Each month's budget is set by the return the last one earned. 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. Cold audiences take most of the budget; warm audiences 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. The next festive collection is briefed and ready. The layered account is in place, with retargeting beside prospecting.

   • Revenue ₹4.7L
   • ROAS 6.96x
   • Ad spend ₹67,999
2. Month 2

   Scaling begins: a new batch goes live after the last one is read. Return on spend falls while budget steps up sharply.

   • Revenue ₹6.6L
   • ROAS 5.44x
   • Ad spend ₹1.2L
3. Month 3

   Budget shifts to the products that sold this period. Return on spend falls while budget steps up sharply. The case study finishes short of ₹20L: 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 4.73x
   • Ad spend ₹1.8L

1. 1

   Raise budget only while return on spend holds; cut it when return drops.

2. 2

   Run creator, customer-feedback and founder-led video; replace low-quality UGC with founder-led video when sales soften.

3. 3

   Give each product (or colour) its own campaign, read weekly, and move budget to the winner.