Fashion & apparel case study: ₹10L to ₹30L monthly revenue in 12 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We set up the base for a mid-sized fashion store before any budget rises. We start with a website audit, a creative brief and a monthly media schedule in the first week. 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. We build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting.

   Why

   A mid-sized fashion brand grows monthly revenue in 1 year, from ₹10L to ₹30L (3x). It is a long case study: past the eighth month our measured engagements are too few, so the pace is extended from what they showed until then. No single problem was named; the case study works from the figures instead. A written brief gives every later budget decision a reference point. Conversion rate caps what any ad budget can return.

   How it works

   All three are shared with the brand's team before any budget step. Fixes are listed and handed over early, so later budget lands on a store that converts. Warm layers run beside prospecting, not instead of it.

2. 2

   Measurement & reviews · Month 1 to 12

   What we do

   We set the path from ₹10L to ₹30L as written monthly numbers, and read every review against the month so far. We track ad-platform revenue beside store orders in a shared daily sheet.

   Why

   It did not report a return on ad spend, so the case study starts from what measured fashion stores of that size hold. Written monthly numbers expose a slow month while there is still time to act. The ad platform's own count runs high, so the store's count is the one that moves budget.

   How it works

   Written monthly numbers make each scaling decision explicit. The sheet is read before each budget change.

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 run static images next to the video ads. We run every lead product in a campaign of its own, read every week. We work in creative batches with a fixed read window each.

   Why

   In measured fashion accounts, video reached the top creative tier most often. Shared campaigns hide weak products; separate ones expose them fast. The read window keeps creative spending tied to evidence.

   How it works

   Statics are added after a video wins. A product that does not sell is paused inside the week. Only ads that convert inside the window keep running. 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 9

   What we do

   We scale through Month 2 to Month 9 toward ₹30L as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. We put seasonal collection campaigns in front of each festive and wedding peak. We cut budgets during major marketplace sale events and move spend to narrower premium audiences.

   Why

   In Month 2 to Month 9 the budget climbs in steps, and return on spend falls as it does. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Controls show which setup holds cost per purchase as spend rises.

   How it works

   The version that keeps cost per purchase lowest stays. Budget moves between regions as the calendar turns. Spend is reduced and redirected for each event window. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

5. 5

   Steady state · Month 10 to 12

   What we do

   From Month 10, we push to reach ₹30L while keeping return where it is. We recover abandoned checkouts and lift repeat orders with WhatsApp messages. We hold a creative bank and swap tired ads out before click-through falls.

   Why

   By Month 10 monthly revenue is within reach of ₹30L. Spend still grows, at a slower pace than during scaling. A message to someone who nearly bought costs less than an ad. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Messages run beside retargeting ads. Refreshes are gradual: a few new ads at a time.

1. Month 1

   Learning month: a small daily budget carries the first ads, and store orders are matched to tracking. The daily sheet now matches platform revenue to store orders. Testing, scaling and retargeting now run as separate layers.

   • Revenue ₹9.9L
   • ROAS 4.02x
   • Ad spend ₹2.5L
2. Month 2

   Scaling starts: ads with falling click-through are swapped from the bank. Spend holds, and return dips.

   • Revenue ₹10.2L
   • ROAS 3.79x
   • Ad spend ₹2.7L
3. Month 3

   Products that do not sell are paused and budget moves to the winners.

   • Revenue ₹10.6L
   • ROAS 3.98x
   • Ad spend ₹2.7L
4. Month 4

   This batch's read is done: winners stay, the rest are cut. Spend steps up, and return dips.

   • Revenue ₹11.5L
   • ROAS 3.55x
   • Ad spend ₹3.2L
5. Month 5

   Bid-cap and cost-cap versions run beside open bidding.

   • Revenue ₹13.9L
   • ROAS 3.37x
   • Ad spend ₹4.1L
6. Month 6

   Repeat-order messages go to past buyers.

   • Revenue ₹15.8L
   • ROAS 3.37x
   • Ad spend ₹4.7L
7. Month 7

   A seasonal collection campaign takes a larger share of budget.

   • Revenue ₹18.7L
   • ROAS 3.57x
   • Ad spend ₹5.2L
8. Month 8

   During a marketplace sale window, spend moves to narrower premium audiences. Monthly revenue passes the halfway point between ₹10L and ₹30L.

   • Revenue ₹23.1L
   • ROAS 3.35x
   • Ad spend ₹6.9L
9. Month 9

   Statics go live next to the winning video.

   • Revenue ₹25.7L
   • ROAS 3.26x
   • Ad spend ₹7.9L
10. Month 10

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

    • Revenue ₹29.4L
    • ROAS 2.99x
    • Ad spend ₹9.9L
11. Month 11

    Ads with falling click-through are swapped from the bank. Spend holds, and return rises. The case study reaches ₹30L a month.

    • Revenue ₹30.2L
    • ROAS 3.37x
    • Ad spend ₹9L
12. Month 12

    The weekly product read pauses the products that are not selling. Spend holds, and return holds. Cost per purchase ends higher than in the learning phase.

    • Revenue ₹31.5L
    • ROAS 3.24x
    • Ad spend ₹9.7L

1. 1

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

2. 2

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

3. 3

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