Fashion & apparel case study: plan for ₹3.5L to ₹5.2L monthly revenue in 45 days

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we'd do

   Start with results that swing from month to month, which the booking named first, before anything else on this smaller store. Layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart retargeting. Mark the festive and wedding dates first and have seasonal collections ready ahead of them.

   Why

   A smaller fashion brand came to us to grow monthly revenue in 45 days, from ₹3.5L to ₹10L (2.9x). 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 problem named at booking was results that swing from month to month. Without layers, retargeting quietly eats the prospecting budget. Demand in this category rises and falls with the festive calendar.

   How it works

   Retargeting sits next to prospecting and never replaces it. Each peak has its campaign ready before it starts.

2. 2

   Measurement & targets · Week 1 to 7

   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 ₹3.5L to ₹10L, and start each review with the week-to-date figure against it. Reconcile the return on spend the brand 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 plan starts there and lets part of it ease as budget grows. The ad platform's own count runs high, so the store's count is the one that moves budget. A missed week shows up early instead of at the end of the plan.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. Each budget step is argued against the written target. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Week 1 to 4

   What we'd do

   Lead the testing layer with video, catalogue ads and static image, building to a few dozen new ads a month by the last four weeks, keeping carousel on a short leash because its return trails the account. Work in creative batches with a fixed read window each. Test static images beside video. Separate the lead products into their own campaigns and review each one weekly.

   Why

   The read window keeps creative spending tied to evidence. In measured fashion accounts, video reached the top creative tier most often. 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. Statics are added after a video wins. A product that does not sell is paused inside the week. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Week 5 to 7

   What we'd do

   Scale through Week 5 to Week 7 as far toward ₹10L 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. Tie every budget increase to return: step up while it holds, step back when it drops. Cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   In Week 5 to Week 7 the modelled 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. 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. Most of the last four weeks's spend sits on Instagram Reels, then Instagram Feed. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Week 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The store-side return is now the one budget follows. The account runs in layers: testing, scaling and retargeting.

   • Projected revenue ₹79,692
   • Projected ROAS 6.06x
   • Planned ad spend ₹13,149
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Budget steps up and return on spend dips.

   • Projected revenue ₹81,214
   • Projected ROAS 5.35x
   • Planned ad spend ₹15,188
3. Week 3

   Store fixes and offers go live while orders confirm. Budget holds and return on spend dips.

   • Projected revenue ₹82,282
   • Projected ROAS 4.95x
   • Planned ad spend ₹16,621
4. Week 4

   Learning phase closes: the buyer found and the winning creative picked.

   • Projected revenue ₹83,166
   • Projected ROAS 4.91x
   • Planned ad spend ₹16,947
5. Week 5

   The scaling phase opens: budget shifts to the products that sold this period. Budget steps up and return on spend holds.

   • Projected revenue ₹92,190
   • Projected ROAS 4.72x
   • Planned ad spend ₹19,538
6. Week 6

   During a marketplace sale window, spend moves to narrower premium audiences. Budget steps up sharply and return on spend falls.

   • Projected revenue ₹1.4L
   • Projected ROAS 3.93x
   • Planned ad spend ₹36,067
7. Week 7

   The creative batch is read and the winners keep the budget. Budget steps up and return on spend dips. Revenue ends below ₹10L, the target set at enquiry, because the plan follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹2L
   • Projected ROAS 3.72x
   • Planned ad spend ₹53,279

1. 01

   Ran CBO and ABO versions side by side to see which held cost

2. 02

   Ask for a seasonal landing page, new ad formats and delivery banners ahead of the festive peak.

3. 03

   Read Meta against Shopify every week