Fashion & apparel case study: plan for ₹3.5L to ₹4.7L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Start with keeping return on spend while scaling, 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. 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 3 months, 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 aims at that fastest tenth's pace, holds budget where return would slip, and shows where it lands against the target. At booking, the brand named keeping return on spend while scaling as its main problem, with scaling ad spend close behind. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Lead categories give prospecting ads a proven entry point.

   How it works

   Retargeting sits next to prospecting and never replaces it. Lead categories carry the first scaling months.

2. 2

   Measurement & targets · Month 1 to 3

   What we'd do

   Log store orders every day beside what the ad platform claims. Agree a written target for every month on the way from ₹3.5L to ₹10L, 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

   The return on ad spend it reported sits close to what measured fashion stores of that size hold, and the plan starts from it. Platform attribution over-counts, so budget decisions sit on the store-side number. A shortfall is caught in the month 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 month is on the page at every review. Both returns are reviewed together each week.

3. 3

   Creative testing · Month 1

   What we'd do

   Test with video, catalogue ads and static image first, rising to a few dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. Separate the lead products into their own campaigns and review each one weekly. The learning month runs at a low daily budget and moves up only when orders come through. Lead with video that carries trust: creators, customer feedback and founder-led pieces.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Spend in the learning phase pays for information. In most accounts we measured, video that carried trust held its return.

   How it works

   Losing products are paused within a week and budget moves to the winners. Only confirmed orders at the low budget unlock the next step. Low-quality UGC is pulled and founder-led video takes its place. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we'd do

   Scale through Month 2 to Month 3 as far toward ₹10L as return allows as the budget rises gently and return dips, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Raise budget only while return on spend holds, and cut it when return drops. Scale into the festive and wedding calendar with seasonal collection campaigns.

   Why

   In Month 2 to Month 3 the modelled budget rises gently, and return on spend dips. Two of these months stop their budget step where return would slip too far. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Demand is seasonal and regional, so spend moves with the calendar.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Spend shifts into each season and between regions with the calendar. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Month 1

   Learning month: the first ads run on a small daily budget while tracking is checked against store orders. Lead categories get their own campaigns. The reported return is checked against the store-side return.

   • Projected revenue ₹3.3L
   • Projected ROAS 2.02x
   • Planned ad spend ₹1.7L
2. Month 2

   The scaling phase opens: budget shifts to the products that sold this period. Budget goes up only as far as return can carry it: budget steps up and return on spend dips.

   • Projected revenue ₹4.5L
   • Projected ROAS 1.90x
   • Planned ad spend ₹2.4L
3. Month 3

   Budget is reviewed against return before the next step. Budget goes up only as far as return can carry it: budget holds and return on spend holds. The plan finishes short of ₹10L: budget stops rising where return would slip. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹4.7L
   • Projected ROAS 1.89x
   • Planned ad spend ₹2.5L

1. 01

   Give each product (or colour) its own campaign, read weekly, and move budget to the winner.

2. 02

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

3. 03

   Tested products one by one, each in its own campaign