Fashion & apparel case study: ₹50,000 to ₹69,543 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 keeping return on spend while scaling, so the opening week 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 months, from ₹50,000 to ₹1.5L–₹3L (4.5x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem was keeping return on spend while scaling. Without layers, retargeting quietly eats the prospecting budget. 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 & reviews · Week 1 to 9

   What we do

   We log store orders every day beside what the ad platform claims. We agree a written number for every week on the way from ₹50,000 to ₹1.5L–₹3L, and start each review with the week-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

   The return on ad spend it reported sits above what measured fashion stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. Platform attribution over-counts, so budget decisions sit on the store-side number. 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. Each budget step is argued against the written number. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We test with video, catalogue ads and static image first, rising to about a dozen new ads a month by the last four weeks, keeping carousel on a short leash because its return trails the account. We separate the lead products into their own campaigns and review each one weekly. We lead with video that carries trust: creators, customer feedback and founder-led pieces. We run static images next to the video ads.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Video built on trust was the format that held return in most measured accounts. In measured fashion accounts, video reached the top creative tier most often.

   How it works

   A product that does not sell is paused inside the week. Low-quality UGC is pulled and founder-led video takes its place. Statics are added after a video wins. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Week 5 to 9

   What we do

   We scale through Week 5 to Week 9 as far toward ₹1.5L–₹3L as return allows as the budget climbs in steps and return dips, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We let return decide budget: more while it holds, less when it slips. We recover abandoned checkouts with WhatsApp messages as traffic grows.

   Why

   Across Week 5 to Week 9, the budget climbs in steps, and return on spend dips. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   There is no fixed ramp; each week's budget follows the return of the last. WhatsApp recovery runs alongside paid retargeting, not instead of it. In the last four weeks, Instagram Reels takes the most spend and Instagram Feed the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The layered account is in place, with retargeting beside prospecting. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹11,284
   • ROAS 2.51x
   • Ad spend ₹4,499
2. Week 2

   First creative read: ads that do not convert are cut. Return holds as the budget holds.

   • Revenue ₹11,196
   • ROAS 2.47x
   • Ad spend ₹4,524
3. Week 3

   Offers and store fixes switch on as the first orders confirm.

   • Revenue ₹11,271
   • ROAS 2.39x
   • Ad spend ₹4,719
4. Week 4

   Learning phase closes: the buyer found and the winning creative picked. Return dips as the budget steps up.

   • Revenue ₹11,550
   • ROAS 2.21x
   • Ad spend ₹5,228
5. Week 5

   Scaling starts: each budget move is read against the return it bought. Return rises as the budget holds.

   • Revenue ₹11,594
   • ROAS 2.33x
   • Ad spend ₹4,981
6. Week 6

   Budget shifts to the products that sold this period. Return dips as the budget steps up.

   • Revenue ₹13,820
   • ROAS 2.13x
   • Ad spend ₹6,501
7. Week 7

   Customer-feedback and creator videos are refreshed.

   • Revenue ₹16,751
   • ROAS 2.10x
   • Ad spend ₹7,960
8. Week 8

   Abandoned checkouts get WhatsApp follow-ups.

   • Revenue ₹19,321
   • ROAS 1.94x
   • Ad spend ₹9,938
9. Week 9

   Static images are tested beside the winning video. Return holds as the budget holds. ₹1.5L–₹3L is not reached in the time, because the case study follows what the fastest tenth of our measured accounts reached. Each order costs about what it did while learning.

   • Revenue ₹19,651
   • ROAS 1.99x
   • Ad spend ₹9,861

1. 1

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

2. 2

   Rebuilt the account: a new-audience campaign excluding past audiences, past-buyer retargeting, one campaign per winning product

3. 3

   Scaling spend raised cost per order and lowered return on spend.