Fashion & apparel case study: ₹1L to ₹1.2L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at scaling ad spend, the brand’s main problem, on a smaller base. 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 6 months, from ₹1L to ₹10L–₹12L (11x). 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 scaling ad spend. Without layers, retargeting quietly eats the prospecting budget. Lead categories give prospecting ads a proven entry point.

   How it works

   Each layer keeps its own budget line. Lead categories carry the first scaling months.

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 to ₹10L–₹12L, and open every review with the month-to-date number against them. We keep a shared daily sheet with the ad platform's revenue next to real store orders. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   It did not report a return on ad spend, so the case study starts from what 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. Budget decisions are read off store numbers, not the ad platform alone. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to about two dozen new ads a month by the final month, 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. The learning month runs with a deliberately small daily budget until orders prove the buyer. We buy creative in batches and give each batch a fixed read window before buying more.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Early spend buys learning, not scale. A fixed window stops spend chasing a creative before it has been read.

   How it works

   Losing products are paused within a week and budget moves to the winners. Spend steps up only after orders confirm at the low budget. Losing batches stop; winning ads take their budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 toward ₹10L–₹12L as the budget rises gently while return holds, 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

   In Month 2 to Month 4 the budget rises gently, while return on spend holds. Budget is part-stepped in three of these months, taking only what return can carry. Return holds through these months because each budget step stops where return starts to slip. Marketplace sales inflate auction costs and pull price-led buyers away.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Spend is reduced and redirected for each event window. Instagram Reels carries the largest share of spend in the final month, with Facebook Feed next. 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–₹12L where return permits. We refresh tired ads by mixing old and new creatives, and keep a creative bank. We recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   From Month 5 growth slows while revenue is still under ₹10L–₹12L. Budget keeps rising, more slowly than in the scaling months. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   New creatives mix with proven ones rather than replacing them all at once. WhatsApp runs alongside paid retargeting, not instead of it.

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 layered account is in place, with retargeting beside prospecting.

   • Revenue ₹95,363
   • ROAS 1.96x
   • Ad spend ₹48,613
2. Month 2

   Scaling starts: the budget decision is taken on the return the last step earned. Budget rises to the point where return starts to give way: spend steps up, and return holds.

   • Revenue ₹1L
   • ROAS 1.91x
   • Ad spend ₹53,647
3. Month 3

   Tired ads are refreshed from the creative bank. Budget rises to the point where return starts to give way: spend holds, and return holds.

   • Revenue ₹1.1L
   • ROAS 1.90x
   • Ad spend ₹58,048
4. Month 4

   Budget shifts to the products that sold this period.

   • Revenue ₹1.1L
   • ROAS 1.96x
   • Ad spend ₹58,084
5. Month 5

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

   • Revenue ₹1.1L
   • ROAS 1.89x
   • Ad spend ₹60,294
6. Month 6

   During a marketplace sale window, spend moves to narrower premium audiences. Budget rises to the point where return starts to give way: spend holds, and return holds. The case study finishes short of ₹10L–₹12L: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹1.2L
   • ROAS 1.89x
   • Ad spend ₹61,724

1. 1

   SEO alongside: product titles, collection copy, feed and technical audit

2. 2

   Ran CBO and ABO versions side by side to see which held cost

3. 3

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