Fashion & apparel case study: ₹35,000–₹40,000 to ₹48,059 monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We focus the first month on keeping return on spend while scaling, the brand’s main problem, on a smaller base. We layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing. 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 3 months, from ₹35,000–₹40,000 to ₹2.5L–₹3L (7.3x). That is faster than nine in ten of our measured accounts grew over the same time, so the case study below is on what that fastest tenth reached and shows where it lands against the number this scenario calls for. The main problem was keeping return on spend while scaling, followed by creative and content. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. A dated offer concentrates demand without training buyers to wait for a sale.

   How it works

   Warm layers run beside prospecting, not instead of it. Awareness ads run ahead of each short sale window.

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 raise budget in a month only while return holds; where it slips too far, we hold it. We set the path from ₹35,000–₹40,000 to ₹2.5L–₹3L as written monthly numbers, and read every review against the month so far.

   Why

   No starting return on ad spend was given; the starting return is what measured fashion stores of that size hold. The ad platform's own count runs high, so the store's count is the one that moves budget. Measured store step-ups often gave back a quarter of their return, so the case study takes a step only while return is holding.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. Where return slips too far, the month keeps last month's budget. Written monthly numbers make each scaling decision explicit.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and static image first, rising to about a dozen new ads a month by the final month, with every format close to the account's return. We commission ads in batches, and read each batch for a set window before ordering the next. We run creator, customer-feedback and founder-led video. We run static images next to the video ads.

   Why

   A fixed window stops spend chasing a creative before it has been read. In most accounts we measured, video that carried trust held its return. Among the fashion accounts we measured, video was the format most often in the top creative tier.

   How it works

   Losing batches stop; winning ads take their budget. Weak UGC is swapped for founder-led video rather than scaled. 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 · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 toward ₹2.5L–₹3L as the budget rises gently while return holds, 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 scale into the festive and wedding calendar with seasonal collection campaigns.

   Why

   Across Month 2 to Month 3, the budget rises gently, while return on spend holds. Two of these months stop their budget step where return slips too far. Each budget step here is sized so that return stays close to where it was. Seasonal demand moves by region, so the spend does too.

   How it works

   Budget follows return month to month instead of a fixed ramp. Budget moves between regions as the calendar turns. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. Testing, scaling and retargeting now run as separate layers. Store orders and ad-platform revenue are checked side by side.

   • Revenue ₹38,841
   • ROAS 1.92x
   • Ad spend ₹20,198
2. Month 2

   The scaling phase opens: new creator and customer-feedback videos join the account. Budget is raised only as far as return allows: with budget steps up, return on spend holds.

   • Revenue ₹43,885
   • ROAS 1.87x
   • Ad spend ₹23,414
3. Month 3

   Budget is reviewed against return before the next step. Budget is raised only as far as return allows: with budget holds, return on spend rises. Revenue ends below ₹2.5L–₹3L, the number this scenario calls for, because budget stops rising where return starts to slip. Cost per purchase ends lower than in the learning phase.

   • Revenue ₹48,059
   • ROAS 1.99x
   • Ad spend ₹24,206

1. 1

   Run short bundle sales with an awareness lead-in rather than permanent discounts.

2. 2

   Launched a wedding-season catalogue campaign and a dedicated scaling campaign

3. 3

   Tested products one by one, each in its own campaign