Fashion & apparel case study: ₹1.5L to ₹3.1L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is return on ad spend, so the opening month on this smaller store goes there. We open with three documents: a website audit, a creative brief and a media schedule by month. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive.

   Why

   In this case study, a smaller fashion brand grows monthly revenue in 6 months, from ₹1.5L to ₹10L (6.7x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The main problem in this scenario is return on ad spend, with a clear strategy close behind. Later budget calls need something written to be judged against. No budget returns more than the store converts.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. The fix list goes to the brand's team in the opening weeks, ahead of any budget step.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We write month-by-month revenue numbers with the brand's team that climb from ₹1.5L to ₹10L, and open every review with the month-to-date number against them.

   Why

   It did not report a return on ad spend, so the case study starts from what measured fashion stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   The sheet is read before each budget change. Written monthly numbers make each scaling decision explicit.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and carousel first, rising to about two dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We run every lead product in a campaign of its own, read every week. The learning month runs at a low daily budget and moves up only when orders come through. We commission ads in batches, and read each batch for a set window before ordering the next.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Spend in the learning phase pays for information. Buying more before a batch is read means paying for guesses.

   How it works

   Budget follows the products that sell. Only confirmed orders at the low budget unlock the next step. Losing batches stop; winning ads take their budget. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 as far toward ₹10L as return allows as the budget rises gently while return rises, with Instagram Reels taking the largest share of spend and Facebook Feed the next. We let return decide budget: more while it holds, less when it slips. We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   Across Month 2 to Month 4, the budget rises gently, while return on spend rises. Return improves over these months, moving from the starting level toward the level of measured stores of that size. Marketplace sales inflate auction costs and pull price-led buyers away.

   How it works

   Each month's budget is set by the return the last one earned. Spend is reduced and redirected for each event window. In the final month, Instagram Reels takes the most spend and Facebook Feed the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we hold the gains and push toward ₹10L only as far as return allows. We stop pushing budget once return has peaked. We ask for a seasonal landing page, new ad formats and delivery banners ahead of the festive peak.

   Why

   Growth slows from Month 5, still short of ₹10L. Spend continues to grow. Most of our engagements saw return fall back from its best month. Season-specific pages convert season traffic better than the default collection.

   How it works

   Budget is held while return is at its best and cut back when it slips. The seasonal page is requested before the season, with the catalogue campaign.

1. Month 1

   First, the set-up: tracking is checked against store orders and the first ads go live on a small daily budget. The daily sheet now matches platform revenue to store orders. Store fixes go live: reviews, trust pointers and the prepaid offer.

   • Revenue ₹1.4L
   • ROAS 1.88x
   • Ad spend ₹76,165
2. Month 2

   Scaling starts: each budget move is read against the return it bought. Return on spend holds while budget holds.

   • Revenue ₹1.6L
   • ROAS 1.91x
   • Ad spend ₹82,226
3. Month 3

   This batch's read is done: winners stay, the rest are cut. Return on spend holds while budget steps up.

   • Revenue ₹1.8L
   • ROAS 1.92x
   • Ad spend ₹92,868
4. Month 4

   The seasonal landing page is briefed ahead of the peak.

   • Revenue ₹2.3L
   • ROAS 2.00x
   • Ad spend ₹1.2L
5. Month 5

   Steady phase begins: creative refresh and repeat orders take on more of the work.

   • Revenue ₹2.9L
   • ROAS 1.88x
   • Ad spend ₹1.5L
6. Month 6

   The seasonal landing page is briefed ahead of the peak. Return on spend holds while budget holds. ₹10L is not reached in the time, because the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends close to the learning phase.

   • Revenue ₹3.1L
   • ROAS 1.90x
   • Ad spend ₹1.6L

1. 1

   Return on spend fell from its peak month in most engagements; peaks do not hold.

2. 2

   Tested products one by one, each in its own campaign

3. 3

   Tracked Meta spend, revenue and Shopify orders daily in a shared sheet