Fashion & apparel (Delhi NCR) case study: ₹2L–₹3L to ₹5.2L monthly revenue in 3–4 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with turning visits into orders, which the brand in this scenario named first, before anything else on this smaller 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

   A smaller fashion brand in Delhi NCR grows monthly revenue in 3–4 months, from ₹2L–₹3L to ₹10L–₹15L (5x). 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 turning visits into orders. No budget returns more than the store converts. 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 4

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We set the path from ₹2L–₹3L to ₹10L–₹15L 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. Platform attribution over-counts, so budget decisions sit on the store-side number. A shortfall is caught in the month it happens, not at the end.

   How it works

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

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and carousel first, rising to about two dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We run every lead product in a campaign of its own, read every week. We lead with video that carries trust: creators, customer feedback and founder-led pieces. We run static images next to the video ads.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Trust-carrying video held return in most measured accounts. Among the fashion accounts we measured, video was the format most often in the top creative tier.

   How it works

   A product that does not sell is paused inside the week. Weak UGC is swapped for founder-led video rather than scaled. Statics are added after a video wins. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹10L–₹15L as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. We scale into the festive and wedding calendar with seasonal collection campaigns.

   Why

   Across Month 2 to Month 3, the budget climbs in steps, 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. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   WhatsApp recovery runs alongside paid retargeting, not instead of it. Budget moves between regions as the calendar turns. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

5. 5

   Steady state · Month 4

   What we do

   From Month 4, we hold the gains and push toward ₹10L–₹15L only as far as return allows. We ask for a seasonal landing page, new ad formats and delivery banners ahead of the festive peak. We recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   From Month 4 growth slows while revenue is still under ₹10L–₹15L. Budget keeps rising, more slowly than in the scaling months. Season-specific pages convert season traffic better than the default collection. A message to someone who nearly bought costs less than an ad.

   How it works

   The seasonal page is requested before the season, with the catalogue campaign. Messages run beside retargeting ads.

1. Month 1

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. Reviews, trust pointers and the prepaid incentive are now on the store. Lead categories get their own campaigns.

   • Revenue ₹2.4L
   • ROAS 2.61x
   • Ad spend ₹93,574
2. Month 2

   Scaling starts: a fresh round of creator and customer-feedback video goes live. With budget steps up, return on spend dips.

   • Revenue ₹3.2L
   • ROAS 2.32x
   • Ad spend ₹1.4L
3. Month 3

   Abandoned checkouts get WhatsApp follow-ups. With budget steps up sharply, return on spend dips.

   • Revenue ₹4.8L
   • ROAS 2.17x
   • Ad spend ₹2.2L
4. Month 4

   Steady phase begins: creative refresh and repeat orders take on more of the work. With budget steps up, return on spend dips. Revenue ends below ₹10L–₹15L, 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 ₹5.2L
   • ROAS 2.01x
   • Ad spend ₹2.6L

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

   Give each product (or colour) its own campaign, read weekly, and move budget to the winner.

3. 3

   Track ad-platform revenue beside store revenue (and net sales) daily or weekly, and set the numbers on the lower figure.