Fashion & apparel case study: ₹3L to ₹44L monthly revenue in 2–3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is scaling ad spend, so the opening month on this smaller store goes there. We split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. We put best-sellers on product pages and lead with the one or two categories that already sell.

   Why

   In this case study, a smaller fashion brand grows monthly revenue in 2–3 months, from ₹3L to ₹1Cr (33.3x). That is faster than nine in ten of our measured accounts grew over the same time, so the case study below is on what that fastest tenth reached and shows where it lands against the number this scenario calls for. The main problem was scaling ad spend. Separate layers stop prospecting and retargeting competing for the same budget. Lead categories give prospecting ads a proven entry point.

   How it works

   Warm layers run beside prospecting, not instead of it. Lead categories carry the first scaling months.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We agree a written number for every month on the way from ₹3L to ₹1Cr, and start each review with the month-to-date figure against it. We reconcile the return on spend the brand in this scenario reports with store revenue before the first budget change.

   Why

   Its reported return on ad spend is higher than measured fashion stores of that size hold, so the case study starts there and lets part of it ease as budget grows. Ad platforms claim more orders than stores record, so the store number decides budget. A missed month shows up early instead of at the end of the case study.

   How it works

   Every budget call starts from the store's orders. Each budget step is argued against the written number. Both returns are reviewed together each week.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to several dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. 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 test static images beside video.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Early spend buys learning, not scale. Among the fashion accounts we measured, video was the format most often in the top creative tier.

   How it works

   Budget follows the products that sell. Only confirmed orders at the low budget unlock the next step. Statics join once a winning video is found. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹1Cr: the budget climbs steeply and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We scale into the festive and wedding calendar with seasonal collection campaigns. We tie every budget increase to return: we step up while it holds, and step back when it drops.

   Why

   Across Month 2 to Month 3, the budget climbs steeply, and return on spend falls as it does. One of these months stops its budget step where return starts to slip too far. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. Seasonal demand moves by region, so the spend does too.

   How it works

   Spend shifts into each season and between regions with the calendar. Budget follows return month to month instead of a fixed ramp. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. Lead categories get their own campaigns. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹3.2L
   • ROAS 3.93x
   • Ad spend ₹80,322
2. Month 2

   The scaling phase opens: the budget decision is taken on the return the last step earned. Return falls as the budget more than doubles.

   • Revenue ₹18.3L
   • ROAS 2.70x
   • Ad spend ₹6.8L
3. Month 3

   The seasonal collection campaign leads this period's spend mix. The step is sized by what return can bear: return falls as the budget more than doubles. Revenue ends below ₹1Cr, the number this scenario calls for, because budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹44L
   • ROAS 2.30x
   • Ad spend ₹19.2L

1. 1

   Net sales ran below logged store revenue after returns, cancellations and tax.

2. 2

   Tested each colour of the lead product in its own campaign

3. 3

   Run Google Search and Shopping beside Meta, and use Google's demand data to shape Meta.