Fashion & apparel case study: plan for ₹3L to ₹2.8L monthly revenue in 1 month

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we'd do

   Aim the first week at keeping return on spend while scaling, the problem named at booking, on a smaller base. Split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. Put best-sellers on product pages and lead with the one or two categories that already sell.

   Why

   The enquiry came from a smaller fashion brand that wants to grow monthly revenue in 1 month, from ₹3L to ₹30L (10x). That is faster than nine in ten of our measured accounts grew over the same time, so the plan below is modelled on what that fastest tenth reached and shows where it lands against the target. The main problem named at booking was keeping return on spend while scaling, followed by return on ad spend. Without layers, retargeting quietly eats the prospecting budget. Lead categories give prospecting ads a proven entry point.

   How it works

   Each layer keeps its own budget line. Lead categories carry the first scaling months.

2. 2

   Measurement & targets · Week 1 to 4

   What we'd do

   Log store orders every day beside what the ad platform claims. Agree a written target for every week on the way from ₹3L to ₹30L, and start each review with the week-to-date figure against it. Check the reported return on spend against store revenue before any budget moves.

   Why

   The return on ad spend it reported sits above what measured fashion stores of that size hold; the plan starts from it and expects some of it to give way as spend rises. Ad platforms claim more orders than stores record, so the store number decides budget. Written targets expose a slow week while there is still time to act.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The target for the week is on the page at every review. The reported return and the store-side return are read side by side every week.

3. 3

   Creative testing · Week 1 to 4

   What we'd do

   Lead the testing layer with video, catalogue ads and carousel, building to about two dozen new ads a month by the last four weeks, with carousel watched closely since it returns less than the rest. Give each lead product its own campaign and read it weekly. The four learning weeks run on a small daily budget, stepping up only once orders confirm. Work in creative batches with a fixed read window each.

   Why

   A product nobody buys is visible within a week when it has its own campaign. The first rupees are for finding the buyer, not for volume. Buying more before a batch is read means paying for guesses.

   How it works

   Budget follows the products that sell. The low budget stays until orders confirm the buyer. Batches that do not convert are cut; winners get the budget. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Week 4

   What we'd do

   Scale through Week 4 toward ₹30L as the budget stays close to the learning level while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Let return decide budget: more while it holds, less when it slips. Cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   In Week 4 the modelled budget stays close to the learning level, while return on spend holds. Each budget step here is sized so that return stays close to where it was. Marketplace sales inflate auction costs and pull price-led buyers away.

   How it works

   Budget follows return week to week instead of a fixed ramp. Spend is reduced and redirected for each event window. In the last four weeks, 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. Week 1

   Learning starts: a small daily budget carries the first ads, and store orders are matched to tracking. The account runs in layers: testing, scaling and retargeting. The daily sheet now matches platform revenue to store orders.

   • Projected revenue ₹71,292
   • Projected ROAS 5.94x
   • Planned ad spend ₹11,998
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Return holds as the budget holds.

   • Projected revenue ₹69,013
   • Projected ROAS 5.84x
   • Planned ad spend ₹11,817
3. Week 3

   Store fixes and offers go live while orders confirm. Return falls as the budget steps up.

   • Projected revenue ₹72,064
   • Projected ROAS 4.98x
   • Planned ad spend ₹14,464
4. Week 4

   Learning phase closes: the buyer found and the winning creative picked. Return holds as the budget holds. The plan finishes short of ₹30L: budget stops rising where return would slip. Each order costs about what it did while learning.

   • Projected revenue ₹70,065
   • Projected ROAS 4.97x
   • Planned ad spend ₹14,101

1. 01

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

2. 02

   Read Meta against Shopify every week

3. 03

   Tracked Meta spend, revenue and Shopify orders daily in a shared sheet