Fashion & apparel case study: plan for ₹10L+ to ₹19.3L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at turning visits into orders, the problem named at booking, on a mid-sized base. Fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. Put best-sellers on product pages and lead with the one or two categories that already sell.

   Why

   The enquiry came from a mid-sized fashion brand that wants to grow monthly revenue in 6 months, from ₹10L+ to ₹30L+ (3x). That is faster than nine in ten of our measured accounts grew over the same time, so the plan below is modelled on what that fastest tenth reached and shows where it lands against the target. At booking, the brand named turning visits into orders as its main problem, with marketing and social presence close behind. Conversion rate caps what any ad budget can return. Lead categories give prospecting ads a proven entry point.

   How it works

   The fix list goes to the brand's team in the opening weeks, ahead of any budget step. Lead categories carry the first scaling months.

2. 2

   Measurement & targets · Month 1 to 6

   What we'd do

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

   Why

   It did not report a return on ad spend, so the plan 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. Written targets expose a slow month while there is still time to act.

   How it works

   Every budget call starts from the store's orders. Written targets make each scaling decision explicit.

3. 3

   Creative testing · Month 1

   What we'd do

   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. Separate the lead products into their own campaigns and review each one weekly. Make trust the subject of the video: creator reels, customer feedback and founder-led clips. Test static images beside video.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Video built on trust was the format that held return in most measured accounts. In measured fashion accounts, video reached the top creative tier most often.

   How it works

   A product that does not sell is paused inside the week. Low-quality UGC is pulled and founder-led video takes its place. Statics are added after a video wins. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we'd do

   Through Month 2 to Month 4, push as far toward ₹30L+ as return allows: the budget rises gently and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. Recover abandoned checkouts with WhatsApp messages as traffic grows. Test cost caps, bid caps and CBO against ABO side by side before each budget step.

   Why

   Across Month 2 to Month 4, the modelled 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. 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. The version that keeps cost per purchase lowest stays. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. 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'd do

   From Month 5, hold the gains and push toward ₹30L+ only as far as return allows. Ask for a seasonal landing page, new ad formats and delivery banners ahead of the festive peak. Use WhatsApp for abandoned checkouts and for past buyers' next order.

   Why

   From Month 5 growth slows while revenue is still under ₹30L+. Spend still grows, at a slower pace than during scaling. 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

   Learning month: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and ad-platform revenue are checked side by side. Lead categories get their own campaigns.

   • Projected revenue ₹10.7L
   • Projected ROAS 3.84x
   • Planned ad spend ₹2.8L
2. Month 2

   The scaling phase opens: a fresh round of creator and customer-feedback video goes live. Return holds as the budget holds.

   • Projected revenue ₹10.8L
   • Projected ROAS 3.73x
   • Planned ad spend ₹2.9L
3. Month 3

   The seasonal landing page is briefed ahead of the peak. Return dips as the budget steps up.

   • Projected revenue ₹12.6L
   • Projected ROAS 3.45x
   • Planned ad spend ₹3.7L
4. Month 4

   Repeat-order messages go to past buyers.

   • Projected revenue ₹15.9L
   • Projected ROAS 3.37x
   • Planned ad spend ₹4.7L
5. Month 5

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

   • Projected revenue ₹18.7L
   • Projected ROAS 3.00x
   • Planned ad spend ₹6.2L
6. Month 6

   Abandoned checkouts get WhatsApp follow-ups. Return rises as the budget holds. Revenue ends below ₹30L+, the target set at enquiry, because the plan follows what the fastest tenth of our measured accounts reached. Each order costs more by the end than it did while learning.

   • Projected revenue ₹19.3L
   • Projected ROAS 3.18x
   • Planned ad spend ₹6.1L

1. 01

   Tested each colour of the lead product in its own campaign

2. 02

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

3. 03

   SEO alongside: product titles, collection copy, feed and technical audit