Fashion & apparel case study: ₹20L to ₹29.6L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We focus the first month on growth that has stalled, the brand’s main problem, on an established base. We run short, dated bundle sales with an awareness lead-in instead of permanent discounts. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive.

   Why

   An established fashion store grows monthly revenue in 3 months, from ₹20L to ₹40L (2x). 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 growth that has stalled. A dated offer concentrates demand without training buyers to wait for a sale. Conversion rate caps what any ad budget can return.

   How it works

   Awareness ads run ahead of each short sale window. The fix list goes to the brand's team in the opening weeks, ahead of any budget step.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We agree a written number for every month on the way from ₹20L to ₹40L, and start each review with the month-to-date figure against it. We match the return the brand in this scenario reports to what the store actually took in, before touching budget. We track ad-platform revenue beside store revenue and net sales after returns, cancellations and tax.

   Why

   The return on ad spend it reported sits close to what measured fashion stores of that size hold, and the case study starts from it. A shortfall is caught in the month it happens, not at the end. In measured accounts net sales ran below logged store revenue, so the numbers sit on the lower figure.

   How it works

   The month's number is on the page at every review. The store-side return becomes the number every review uses. Net sales, not platform revenue, decide each budget step.

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 separate the lead products into their own campaigns and review each one weekly. The learning month runs on a small daily budget, stepping up only once orders confirm. We run static images next to the video ads.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Spend in the learning phase pays for information. Among the fashion accounts we measured, video was the format most often in the top creative tier.

   How it works

   Budget follows the products that sell. Spend steps up only after orders confirm at the low budget. Statics join once a winning video is found. 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 as far toward ₹40L as return allows: the budget rises gently and return falls, and Instagram Feed carries the most spend and Instagram Reels the next. We test cost caps, bid caps and CBO against ABO side by side before each budget step. We raise budget only while return on spend holds, and cut it when return drops.

   Why

   Across Month 2 to Month 3, the budget rises gently, and return on spend falls as it does. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. Controls show which setup holds cost per purchase as spend rises.

   How it works

   The version that keeps cost per purchase lowest stays. Budget follows return month to month instead of a fixed ramp. In the final month, Instagram Feed takes the most spend and Instagram Reels the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Month 1

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

   • Revenue ₹20.3L
   • ROAS 4.84x
   • Ad spend ₹4.2L
2. Month 2

   The scaling phase opens: each budget move is read against the return it bought. With budget steps up, return on spend holds.

   • Revenue ₹23.7L
   • ROAS 4.76x
   • Ad spend ₹5L
3. Month 3

   Cost caps and bid caps are tested against open bidding. With budget steps up, return on spend falls. Revenue ends below ₹40L, the number this scenario calls for, because the case study follows what the fastest tenth of our measured accounts reached. Each order costs more by the end than it did while learning.

   • Revenue ₹29.6L
   • ROAS 4.09x
   • Ad spend ₹7.2L

1. 1

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

2. 2

   Net sales ran below logged store revenue after returns, cancellations and tax.

3. 3

   Store revenue per rupee of ad spend fell from 8.2x to 5.5x as monthly spend nearly doubled