Fashion & apparel case study: ₹12,000 to ₹12,754 monthly revenue in 2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   The first problem in this scenario is turning visits into orders, so the opening week on this smaller store goes there. We get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront. 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 months, from ₹12,000 to ₹1L (8.3x). 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 turning visits into orders. Conversion rate caps what any ad budget can return. Lead categories give prospecting ads a proven entry point.

   How it works

   Fixes are listed and handed over early, so later budget lands on a store that converts. Lead categories carry the first scaling months.

2. 2

   Measurement & reviews · Week 1 to 9

   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 week on the way from ₹12,000 to ₹1L, and start each review with the week-to-date figure against it. We read results in three-to-four-day windows, since the case study runs week by week.

   Why

   The return on ad spend it reported is below the level where raising budget pays for a store of that size, so budget does not rise until return climbs. Ad platforms claim more orders than stores record, so the store number decides budget. A missed week shows up early instead of at the end of the case study.

   How it works

   The sheet is read before each budget change. The week's number is on the page at every review. Each window is read on purchases and cost per purchase before the next move.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We lead the testing layer with catalogue ads, video and static image, building to a handful of new ads a month by the last four weeks, with no format far behind the account's return. We give each lead product its own campaign and read it weekly. We run creator, customer-feedback and founder-led video. The four learning weeks run with a deliberately small daily budget until orders prove the buyer.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Trust-carrying video held return in most measured accounts. Early spend buys learning, not scale.

   How it works

   A product that does not sell is paused inside the week. Weak UGC is swapped for founder-led video rather than scaled. Spend steps up only after orders confirm at the low budget. More spend buys more new ads, led by catalogue ads ahead of video.

4. 4

   Scaling · Week 5 to 9

   What we do

   Through Week 5 to Week 9, we push toward ₹1L: the budget rises gently while return holds, and Facebook Feed carries the most spend and Instagram Reels the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. We tie every budget increase to return: we step up while it holds, and step back when it drops.

   Why

   Across Week 5 to Week 9, the budget rises gently, while return on spend holds. In four of these weeks budget goes up only as far as return allows, so revenue grows more slowly than the final number needs. Budget does not rise in one of these weeks: at that return, more spend does not pay. Return holds through these weeks because each budget step stops where return starts to slip. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   WhatsApp recovery runs alongside paid retargeting, not instead of it. Each week's budget is set by the return the last one earned. Most of the last four weeks's spend sits on Facebook Feed, then Instagram Reels. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Week 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. The store fix list is live, with reviews on product pages and a prepaid offer. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹2,808
   • ROAS 1.91x
   • Ad spend ₹1,471
2. Week 2

   First creative read: ads that do not convert are cut. Budget holds and return on spend holds.

   • Revenue ₹2,896
   • ROAS 1.93x
   • Ad spend ₹1,502
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Revenue ₹2,805
   • ROAS 1.96x
   • Ad spend ₹1,433
4. Week 4

   By the end of learning, the buyer is known and one ad has won.

   • Revenue ₹2,892
   • ROAS 1.93x
   • Ad spend ₹1,502
5. Week 5

   Scaling starts: budget shifts to the products that sold this period. Only the part of the step that return supports is taken: budget holds and return on spend holds.

   • Revenue ₹3,078
   • ROAS 1.93x
   • Ad spend ₹1,597
6. Week 6

   Abandoned checkouts get WhatsApp follow-ups.

   • Revenue ₹3,083
   • ROAS 1.90x
   • Ad spend ₹1,620
7. Week 7

   Customer-feedback and creator videos are refreshed.

   • Revenue ₹3,136
   • ROAS 1.89x
   • Ad spend ₹1,657
8. Week 8

   Each budget move is read against the return it bought.

   • Revenue ₹3,217
   • ROAS 1.90x
   • Ad spend ₹1,696
9. Week 9

   The weekly product read pauses the products that are not selling. Because a larger budget does not earn its keep at this return, budget holds and return on spend holds. The case study finishes short of ₹1L: budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹3,318
   • ROAS 1.97x
   • Ad spend ₹1,682

1. 1

   Tested products one by one, each in its own campaign

2. 2

   Read Meta against Shopify every week

3. 3

   Tracked net sales beside Shopify revenue and set the numbers on the lower figure