Fashion & apparel case study: plan for ₹5.5L to ₹7.7L monthly revenue in 45–60 days

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we'd do

   Aim the first week at return on ad spend, the problem named at booking, on a mid-sized base. Get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront. Put best-sellers on product pages and lead with the one or two categories that already sell.

   Why

   A mid-sized fashion brand came to us to grow monthly revenue in 45–60 days, from ₹5.5L to ₹10L (+82%). Nine in ten of the accounts we measured grew more slowly than that in the same time; the plan is built on what the fastest tenth reached, and shows the gap to the target honestly. At booking, the brand named return on ad spend as its main problem. Conversion rate caps what any ad budget can return. Lead categories give prospecting ads a proven entry point.

   How it works

   Site fixes are handed over in the first weeks, before budget rises. Lead categories carry the first scaling months.

2. 2

   Measurement & targets · Week 1 to 9

   What we'd do

   Keep a shared daily sheet with the ad platform's revenue next to real store orders. Set the path from ₹5.5L to ₹10L as written weekly targets, and read every review against the week so far. Reconcile the return on spend the brand reports with store revenue before the first budget change.

   Why

   The return on ad spend it reported sits below what measured fashion stores of that size hold, so the plan starts there and rebuilds return before budget rises. The ad platform's own count runs high, so the store's count is the one that moves budget. A shortfall is caught in the week it happens, not at the end.

   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. Both returns are reviewed together each week.

3. 3

   Creative testing · Week 1 to 4

   What we'd do

   Test with video, catalogue ads and static image first, rising to a few dozen new ads a month by the last four weeks, with carousel watched closely since it returns less than the rest. Run every lead product in a campaign of its own, read every week. Run creator, customer-feedback and founder-led video. The four learning weeks run on a small daily budget, stepping up only once orders confirm.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Video built on trust was the format that held return in most measured accounts. Early spend buys learning, not scale.

   How it works

   A product that does not sell is paused inside the week. Weak UGC is swapped for founder-led video rather than scaled. Only confirmed orders at the low budget unlock the next step. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Week 5 to 9

   What we'd do

   Scale through Week 5 to Week 9 as far toward ₹10L as return allows as the budget climbs in steps while return holds, with Instagram Feed taking the largest share of spend and Instagram Reels the next. Raise budget only while return on spend holds, and cut it when return drops. Run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up.

   Why

   Across Week 5 to Week 9, the modelled budget climbs in steps, while return on spend holds. Return holds through these weeks because each budget step stops where return would slip. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   Budget follows return week to week instead of a fixed ramp. Controls run as parallel versions and the one that holds cost is kept. In the last four weeks, Instagram Feed takes the most spend and Instagram Reels the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. The store-side return is now the one budget follows. Store fixes go live: reviews, trust pointers and the prepaid offer.

   • Projected revenue ₹1.3L
   • Projected ROAS 2.51x
   • Planned ad spend ₹50,837
2. Week 2

   First creative read: ads that do not convert are cut. Budget holds and return on spend holds.

   • Projected revenue ₹1.3L
   • Projected ROAS 2.44x
   • Planned ad spend ₹53,344
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Projected revenue ₹1.3L
   • Projected ROAS 2.67x
   • Planned ad spend ₹48,060
4. Week 4

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

   • Projected revenue ₹1.3L
   • Projected ROAS 2.52x
   • Planned ad spend ₹51,084
5. Week 5

   Scaling starts: products that do not sell are paused and budget moves to the winners. Budget holds and return on spend rises.

   • Projected revenue ₹1.3L
   • Projected ROAS 2.65x
   • Planned ad spend ₹49,272
6. Week 6

   Bid-cap and cost-cap versions run beside open bidding. Budget steps up and return on spend holds.

   • Projected revenue ₹1.5L
   • Projected ROAS 2.60x
   • Planned ad spend ₹58,541
7. Week 7

   Budget is reviewed against return before the next step.

   • Projected revenue ₹1.8L
   • Projected ROAS 2.48x
   • Planned ad spend ₹71,000
8. Week 8

   A fresh round of creator and customer-feedback video goes live.

   • Projected revenue ₹2.1L
   • Projected ROAS 2.51x
   • Planned ad spend ₹84,868
9. Week 9

   Products that do not sell are paused and budget moves to the winners. Budget steps up and return on spend holds. ₹10L is not reached in the time, because the plan follows what the fastest tenth of our measured accounts reached. Each order costs about what it did while learning.

   • Projected revenue ₹2.3L
   • Projected ROAS 2.47x
   • Planned ad spend ₹93,544

1. 01

   Best-sellers on product pages, trousers and co-ords as lead categories, a monthly creative bank

2. 02

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

3. 03

   Read Meta against Shopify every week