Fashion & apparel case study: plan for ₹1L to ₹1.1L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at results that swing from month to month, the problem named at booking, on a smaller base. Build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. Map the festive and wedding calendar before spending, with seasonal collections ready in advance.

   Why

   A smaller fashion store asked us how to grow monthly revenue in 3 months, from ₹1L to ₹3L–₹5L (4x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the plan aims at the pace of that top tenth, holds budget where return would slip, and does not force the target. The problem named at booking was results that swing from month to month. Separate layers stop prospecting and retargeting competing for the same budget. Buyers in this category shop around festivals and weddings.

   How it works

   Warm layers run beside prospecting, not instead of it. Seasonal campaigns are prepared ahead of each peak.

2. 2

   Measurement & targets · Month 1 to 3

   What we'd do

   Keep a shared daily sheet with the ad platform's revenue next to real store orders. Agree a written target for every month on the way from ₹1L to ₹3L–₹5L, and start each review with the month-to-date figure against it. Match the return the brand reports to what the store actually took in, before touching budget.

   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. Written targets expose a slow month while there is still time to act.

   How it works

   The sheet is read before each budget change. The target for the month is on the page at every review. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Month 1

   What we'd do

   Lead the testing layer with video, catalogue ads and static image, building to about a dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. Work in creative batches with a fixed read window each. Make trust the subject of the video: creator reels, customer feedback and founder-led clips. Run every lead product in a campaign of its own, read every week.

   Why

   A fixed window stops spend chasing a creative before it has been read. Trust-carrying video held return in most measured accounts. Shared campaigns hide weak products; separate ones expose them fast.

   How it works

   Only ads that convert inside the window keep running. Weak UGC is swapped for founder-led video rather than scaled. Losing products are paused within a week and budget moves to the winners. 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

   Scale through Month 2 to Month 3 as far toward ₹3L–₹5L as return allows as the budget rises gently while return holds, 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 Month 2 to Month 3 the modelled budget rises gently, while return on spend holds. Two of these months stop their budget step where return would slip too far. Return holds through these months because each budget step stops where return would slip. Marketplace sales inflate auction costs and pull price-led buyers away.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Spend is reduced and redirected for each event window. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 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. Store orders and ad-platform revenue are checked side by side.

   • Projected revenue ₹94,218
   • Projected ROAS 2.06x
   • Planned ad spend ₹45,672
2. Month 2

   Scaling starts: budget is reviewed against return before the next step. The step is sized by what return will bear: budget steps up and return on spend dips.

   • Projected revenue ₹1.1L
   • Projected ROAS 1.95x
   • Planned ad spend ₹56,439
3. Month 3

   A new batch goes live after the last one is read. The step is sized by what return will bear: budget holds and return on spend holds. ₹3L–₹5L is not reached in the time, because budget stops rising where return would slip. Each order costs about what it did while learning.

   • Projected revenue ₹1.1L
   • Projected ROAS 2.01x
   • Planned ad spend ₹55,180

1. 01

   Run creator, customer-feedback and founder-led video; replace low-quality UGC with founder-led video when sales soften.

2. 02

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

3. 03

   Read Meta against Shopify every week