Fashion & apparel case study: ₹14L to ₹27L monthly revenue in 6 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 put the website audit, the creative brief and the monthly media schedule in place before spend moves. We put best-sellers on product pages and lead with the one or two categories that already sell.

   Why

   A mid-sized fashion brand grows monthly revenue in 6 months, from ₹14L to ₹30L (2.1x). 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 brand in this scenario named no single problem, so the case study starts from the numbers. Without a brief, each creative and budget decision starts from scratch. Lead categories give prospecting ads a proven entry point.

   How it works

   All three are shared with the brand's team before any budget step. Lead categories carry the first scaling months.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We agree a written number for every month on the way from ₹14L to ₹30L, and start each review with the month-to-date figure against 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. The ad platform's own count runs high, so the store's count is the one that moves budget. A missed month shows up early instead of at the end of the case study.

   How it works

   Every budget call starts from the store's orders. The month's number is on the page at every review.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building 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 run every lead product in a campaign of its own, read every week. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   Trust-carrying video held return in most measured accounts. A product nobody buys is visible within a week when it has its own campaign. Early spend buys learning, not scale.

   How it works

   Weak UGC is swapped for founder-led video rather than scaled. Losing products are paused within a week and budget moves to the winners. Only confirmed orders at the low budget unlock the next step. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push as far toward ₹30L as return allows: the budget rises gently and return falls, and Instagram Feed carries the most spend and Instagram Reels the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We test cost caps, bid caps and CBO against ABO side by side before each budget step.

   Why

   Across Month 2 to Month 4, the budget rises gently, and return on spend falls as it does. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Budget follows return month to month instead of a fixed ramp. Controls run as parallel versions and the one that holds cost is kept. Most of the final month's spend sits on Instagram Feed, then Instagram Reels. Most spend reaches people who have not bought yet; past visitors return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we hold the gains and push toward ₹30L only as far as return allows. We keep a bank of ready creatives and rotate them in as ads tire. We ask for a seasonal landing page, new ad formats and delivery banners ahead of the festive peak.

   Why

   At Month 5 revenue is close to ₹30L. Budget keeps rising, more slowly than in the scaling months. In one account we measured, click-through fell before revenue did; refresh is how that is avoided. Season-specific pages convert season traffic better than the default collection.

   How it works

   New creatives mix with proven ones rather than replacing them all at once. The seasonal page is requested before the season, with the catalogue campaign.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Store orders and ad-platform revenue are checked side by side. The media schedule and creative brief are signed off.

   • Revenue ₹14L
   • ROAS 3.69x
   • Ad spend ₹3.8L
2. Month 2

   Scaling starts: cost caps and bid caps are tested against open bidding. Budget steps up and return on spend holds.

   • Revenue ₹15.1L
   • ROAS 3.55x
   • Ad spend ₹4.3L
3. Month 3

   The weekly product read pauses the products that are not selling.

   • Revenue ₹18.5L
   • ROAS 3.16x
   • Ad spend ₹5.9L
4. Month 4

   Budget is reviewed against return before the next step.

   • Revenue ₹20.3L
   • ROAS 3.01x
   • Ad spend ₹6.8L
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back. Monthly revenue passes the halfway point between ₹14L and ₹30L.

   • Revenue ₹23.9L
   • ROAS 2.80x
   • Ad spend ₹8.5L
6. Month 6

   Ads with falling click-through are swapped from the bank. Budget steps up and return on spend holds. Revenue ends below ₹30L, 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 ₹27L
   • ROAS 2.74x
   • Ad spend ₹9.9L

1. 1

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

2. 2

   Tracked net sales beside Shopify revenue and set the numbers on the lower figure

3. 3

   Tracked Meta spend, revenue and Shopify orders daily in a shared sheet