Fashion & apparel case study: ₹60,000 to ₹1.7L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with keeping return on spend while scaling, which the brand in this scenario named first, before anything else on this smaller store. We layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing. 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.

   Why

   In this case study, a smaller fashion brand grows monthly revenue in 3 months, from ₹60,000 to ₹30L (50x). 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 in this scenario is keeping return on spend while scaling, with return on ad spend close behind. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Conversion rate caps what any ad budget can return.

   How it works

   Each layer keeps its own budget line. Site fixes are handed over in the first weeks, before budget rises.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We set the path from ₹60,000 to ₹30L as written monthly numbers, and read every review against the month so far. We set a written rule: no budget step in a month where return falls too far to pay for 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. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed month shows up early instead of at the end of the case study.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The month's number is on the page at every review. 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 static image first, rising to a few 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 on a small daily budget, stepping up only once orders confirm. We test static images beside video.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. 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

   Losing products are paused within a week and budget moves to the winners. Spend steps up only after orders confirm at the low budget. Statics join once a winning video is found. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹30L: the budget climbs in steps and return dips, and Instagram Reels carries the most spend and Instagram Feed the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   In Month 2 to Month 3 the budget climbs in steps, and return on spend dips. One of these months stops its budget step where return starts to slip too far. In one of these months budget holds, because return is below the level where more budget pays. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. 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. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   Learning month: tracking is checked against store orders and the first ads go live on a small daily budget. Store orders and platform revenue are reconciled in the shared sheet. Reviews, trust pointers and the prepaid incentive are now on the store.

   • Revenue ₹60,395
   • ROAS 2.09x
   • Ad spend ₹28,926
2. Month 2

   Scaling begins: during a marketplace sale window, spend moves to narrower premium audiences. The budget step stops where return starts to slip: spend more than doubles, and return dips.

   • Revenue ₹1.7L
   • ROAS 1.92x
   • Ad spend ₹90,415
3. Month 3

   The budget decision is taken on the return the last step earned. With return short of where a budget step pays, spend holds, and return holds. The case study finishes short of ₹30L: budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹1.7L
   • ROAS 1.89x
   • Ad spend ₹88,095

1. 1

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

2. 2

   We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

3. 3

   Put best-sellers on product pages, lead with the one or two categories that sell, and keep a monthly creative bank.