Fashion & apparel case study: ₹1L–₹1.5L to ₹1.6L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We open with set-up work on the smaller fashion store, since the brand in this scenario named no single problem. We put the website audit, the creative brief and the monthly media schedule in place before spend moves. We run short, dated bundle sales with an awareness lead-in instead of permanent discounts.

   Why

   In this case study, a smaller fashion brand grows monthly revenue in 6 months, from ₹1L–₹1.5L to ₹10L (8x). 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. No single problem was named; the case study works from the figures instead. Without a brief, each creative and budget decision starts from scratch. A dated offer concentrates demand without training buyers to wait for a sale.

   How it works

   All three are shared with the brand's team before any budget step. Awareness ads run ahead of each short sale window.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We write month-by-month revenue numbers with the brand's team that climb from ₹1L–₹1.5L to ₹10L, and open every review with the month-to-date number against them. We log store orders every day beside what the ad platform claims. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   With no return on ad spend reported, the case study begins at the level measured fashion stores of that size hold. A missed month shows up early instead of at the end of the case study. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   The month's number is on the page at every review. Every budget call starts from the store's orders. A month whose return slips past that point keeps its budget instead.

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. The learning month runs at a low daily budget and moves up only when orders come through. We run every lead product in a campaign of its own, read every week. We test static images beside video.

   Why

   Spend in the learning phase pays for information. Products that do not sell show up inside a week, not after a month of shared budget. In measured fashion accounts, video reached the top creative tier most often.

   How it works

   The low budget stays until orders confirm the buyer. A product that does not sell is paused inside the week. Statics are added after a video wins. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push toward ₹10L: the budget rises gently while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. We raise budget only while return on spend holds, and cut it when return drops. We put seasonal collection campaigns in front of each festive and wedding peak.

   Why

   In Month 2 to Month 4 the budget rises gently, while return on spend holds. In three of these months the step is trimmed to the size return can hold. Return holds through these months because each budget step stops where return starts to slip. Demand is seasonal and regional, so spend moves with the calendar.

   How it works

   Each month's budget is set by the return the last one earned. Budget moves between regions as the calendar turns. 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.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we protect the revenue already built and move toward ₹10L where return permits. We recover abandoned checkouts and lift repeat orders with WhatsApp messages. 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 holds roughly steady from here. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout. Season-specific pages convert season traffic better than the default collection.

   How it works

   WhatsApp runs alongside paid retargeting, not instead of it. The seasonal page is requested before the season, with the catalogue campaign.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and ad-platform revenue are checked side by side. The website audit, creative brief and media schedule are shared.

   • Revenue ₹1.2L
   • ROAS 1.90x
   • Ad spend ₹63,203
2. Month 2

   Scaling starts: the weekly product read pauses the products that are not selling. Budget rises to the point where return starts to give way: return on spend holds while budget steps up.

   • Revenue ₹1.4L
   • ROAS 1.88x
   • Ad spend ₹72,175
3. Month 3

   The seasonal landing page is briefed ahead of the peak. Budget rises to the point where return starts to give way: return on spend holds while budget holds.

   • Revenue ₹1.5L
   • ROAS 1.95x
   • Ad spend ₹74,560
4. Month 4

   Each budget move is read against the return it bought.

   • Revenue ₹1.5L
   • ROAS 1.89x
   • Ad spend ₹79,342
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back.

   • Revenue ₹1.5L
   • ROAS 1.90x
   • Ad spend ₹81,343
6. Month 6

   Statics go live next to the winning video. Because a larger budget does not earn its keep at this return, return on spend holds while budget holds. The case study finishes short of ₹10L: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹1.6L
   • ROAS 1.96x
   • Ad spend ₹80,294

1. 1

   Tested products one by one, each in its own campaign

2. 2

   Test bidding controls (cost caps, bid caps, CBO against ABO) side by side before scaling.

3. 3

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