Fashion & apparel case study: ₹4L to ₹6.3L 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 results that swing from month to month, so the opening month on this smaller store goes there. 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 ₹4L to ₹15L (3.8x). 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

   Each layer keeps its own budget line. Each peak has its campaign ready before it starts.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We write month-by-month revenue numbers with the brand's team that climb from ₹4L to ₹15L, and open every review with the month-to-date number against them. We track ad-platform revenue beside store orders in a shared daily sheet. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   With no return on ad spend reported, the case study begins at the level measured fashion stores of that size hold. A shortfall is caught in the month it happens, not at the end. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   The month's number is on the page at every review. The sheet is read before each budget change. Where return slips too far, the month keeps last month's budget.

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 work in creative batches with a fixed read window each. 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

   The read window keeps creative spending tied to evidence. A product nobody buys is visible within a week when it has its own campaign. Video built on trust was the format that held return in most measured accounts.

   How it works

   Only ads that convert inside the window keep running. Losing products are paused within a week and budget moves to the winners. Weak UGC is swapped for founder-led video rather than scaled. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹15L as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We raise budget only while return on spend holds, and cut it when return 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 one of these months the step is trimmed to the size return can hold. 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. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. The account runs in layers: testing, scaling and retargeting. Store orders and ad-platform revenue are checked side by side.

   • Revenue ₹4.1L
   • ROAS 2.55x
   • Ad spend ₹1.6L
2. Month 2

   The scaling phase opens: during a marketplace sale window, spend moves to narrower premium audiences. Spend steps up sharply, and return falls.

   • Revenue ₹5.2L
   • ROAS 2.10x
   • Ad spend ₹2.5L
3. Month 3

   A fresh round of creator and customer-feedback video goes live. Only the part of the step that return supports is taken: spend steps up, and return dips. The case study finishes short of ₹15L: budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹6.3L
   • ROAS 1.93x
   • Ad spend ₹3.3L

1. 1

   Scaling spend raised cost per order and lowered return on spend.

2. 2

   Tested each colour of the lead product in its own campaign

3. 3

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