Fashion & apparel case study: ₹50L to ₹97.3L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with buyer trust, which the brand in this scenario named first, before anything else on this established store. We fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. We put best-sellers on product pages and lead with the one or two categories that already sell.

   Why

   An established fashion store grows monthly revenue in 3 months, from ₹50L to ₹3Cr–₹5Cr (8x). 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 buyer trust. Every rupee of ads is capped by how well the store turns visits into orders. Lead categories give prospecting ads a proven entry point.

   How it works

   Site fixes are handed over in the first weeks, before budget rises. Lead categories carry the first scaling months.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We report net sales after returns, cancellations and tax next to store revenue and platform revenue. We write month-by-month revenue numbers with the brand's team that climb from ₹50L to ₹3Cr–₹5Cr, and open every review with the month-to-date number against them. 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. Returns, cancellations and tax cut logged revenue, so the numbers are set on net sales. A shortfall is caught in the month it happens, not at the end.

   How it works

   Net sales, not platform revenue, decide each budget step. 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 several dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We make trust the subject of the video: creator reels, customer feedback and founder-led clips. We commission ads in batches, and read each batch for a set window before ordering the next. The learning month runs on a small daily budget, stepping up only once orders confirm.

   Why

   Video built on trust was the format that held return in most measured accounts. The read window keeps creative spending tied to evidence. Early spend buys learning, not scale.

   How it works

   UGC that underperforms is replaced by founder-led video, not given more budget. Batches that do not convert are cut; winners get the budget. Only confirmed orders at the low budget unlock the next step. 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

   Through Month 2 to Month 3, we push toward ₹3Cr–₹5Cr: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We raise budget only while return on spend holds, and cut it when return drops. We test cost caps, bid caps and CBO against ABO side by side before each budget step.

   Why

   In Month 2 to Month 3 the budget climbs in steps, and return on spend falls as it does. In two of these months the step is trimmed to the size return can hold. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   Each month's budget is set by the return the last one earned. Controls run as parallel versions and the one that holds cost is kept. 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

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Store fixes go live: reviews, trust pointers and the prepaid offer. Net sales are reconciled against logged revenue.

   • Revenue ₹50.9L
   • ROAS 3.82x
   • Ad spend ₹13.3L
2. Month 2

   The scaling phase opens: a fresh round of creator and customer-feedback video goes live. Budget is raised only as far as return allows: budget steps up sharply and return on spend falls.

   • Revenue ₹84.6L
   • ROAS 2.97x
   • Ad spend ₹28.5L
3. Month 3

   Bid-cap and cost-cap versions run beside open bidding. Budget is raised only as far as return allows: budget steps up and return on spend dips. The case study finishes short of ₹3Cr–₹5Cr: budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹97.3L
   • ROAS 2.75x
   • Ad spend ₹35.4L

1. 1

   We pause styles with high return-to-origin and test replacement styles in their own campaigns.

2. 2

   Run creator, customer-feedback and founder-led video; replace low-quality UGC with founder-led video when sales soften.

3. 3

   Cut budgets during a major marketplace sale and moved spend to niche premium audiences