Fashion & apparel case study: plan for ₹17L to ₹31.6L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at growth that has stalled, the problem named at booking, on a mid-sized 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. Fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive.

   Why

   The enquiry came from a mid-sized fashion brand that wants to grow monthly revenue in 3 months, from ₹17L to ₹50L (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 main problem named at booking was growth that has stalled, followed by scaling ad spend. Without layers, retargeting quietly eats the prospecting budget. Every rupee of ads is capped by how well the store turns visits into orders.

   How it works

   Retargeting sits next to prospecting and never replaces it. The fix list goes to the brand's team in the opening weeks, ahead of any budget step.

2. 2

   Measurement & targets · Month 1 to 3

   What we'd do

   Log store orders every day beside what the ad platform claims. Set the path from ₹17L to ₹50L as written monthly targets, and read every review against the month so far.

   Why

   It did not report a return on ad spend, so the plan starts from what measured fashion stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. Written targets expose a slow month while there is still time to act.

   How it works

   Every budget call starts from the store's orders. Written targets make each scaling decision explicit.

3. 3

   Creative testing · Month 1

   What we'd do

   Lead the testing layer with video, catalogue ads and carousel, 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. Give each lead product its own campaign and read it weekly. Work in creative batches with a fixed read window each. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   A product nobody buys is visible within a week when it has its own campaign. The read window keeps creative spending tied to evidence. Spend in the learning phase pays for information.

   How it works

   A product that does not sell is paused inside the week. Batches that do not convert are cut; winners get the budget. Spend steps up only after orders confirm at the low budget. 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'd do

   Through Month 2 to Month 3, push as far toward ₹50L as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. Run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. Tie every budget increase to return: step up while it holds, step back when it drops.

   Why

   In Month 2 to Month 3 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. Controls show which setup holds cost per purchase as spend rises.

   How it works

   The version that keeps cost per purchase lowest stays. Each month's budget is set by the return the last one earned. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Month 1

   First, the set-up: tracking is checked against store orders and the first ads go live on a small daily budget. Testing, scaling and retargeting now run as separate layers. Store orders and platform revenue are reconciled in the shared sheet.

   • Projected revenue ₹17.3L
   • Projected ROAS 4.54x
   • Planned ad spend ₹3.8L
2. Month 2

   The scaling phase opens: this batch's read is done: winners stay, the rest are cut. With budget steps up sharply, return on spend falls.

   • Projected revenue ₹24.9L
   • Projected ROAS 3.85x
   • Planned ad spend ₹6.5L
3. Month 3

   The weekly product read pauses the products that are not selling. With budget steps up, return on spend holds. ₹50L is not reached in the time, 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 ₹31.6L
   • Projected ROAS 3.75x
   • Planned ad spend ₹8.4L

1. 01

   Find the buyer on a small daily budget first, then step spend up in stages.

2. 02

   Run short bundle sales with an awareness lead-in rather than permanent discounts.

3. 03

   Tested products one by one, each in its own campaign