Fashion & apparel case study: plan for ₹8L to ₹15.5L monthly revenue in 5 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   The booking named turning visits into orders first, so the opening month on this mid-sized store goes there. 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

   A mid-sized fashion brand came to us to grow monthly revenue in 5 months, from ₹8L to ₹20L (2.5x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the plan follows the pace of that top tenth rather than forcing the target. The problem named at booking was turning visits into orders. Every rupee of ads is capped by how well the store turns visits into orders. Lead categories give prospecting ads a proven entry point.

   How it works

   Fixes are listed and handed over early, so later budget lands on a store that converts. Lead categories carry the first scaling months.

2. 2

   Measurement & targets · Month 1 to 5

   What we'd do

   Track ad-platform revenue beside store orders in a shared daily sheet. Agree a written target for every month on the way from ₹8L to ₹20L, and start each review with the month-to-date figure against it. Check the reported return on spend against store revenue before any budget moves.

   Why

   Its reported return on ad spend is in line with what measured fashion stores of that size hold, so the plan starts there. 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

   Budget decisions are read off store numbers, not the ad platform alone. The target for the month is on the page at every review. Both returns are reviewed together each week.

3. 3

   Creative testing · Month 1

   What we'd do

   Test with video, catalogue ads and static image first, rising to a few 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. Run creator, customer-feedback and founder-led video. The learning month runs on a small daily budget, stepping up only once orders confirm.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Video built on trust was the format that held return in most measured accounts. Early spend buys learning, not scale.

   How it works

   Losing products are paused within a week and budget moves to the winners. UGC that underperforms is replaced by founder-led video, not given more budget. The low budget stays until orders confirm the buyer. 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 ₹20L as return allows: the budget climbs in steps while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. Recover abandoned checkouts with WhatsApp messages as traffic grows. Run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up.

   Why

   In Month 2 to Month 4 the modelled budget climbs in steps, while return on spend holds. Each budget step here is sized so that return stays close to where it was. 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. Controls run as parallel versions and the one that holds cost is kept. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 5

   What we'd do

   From Month 5, hold the gains and push toward ₹20L only as far as return allows. Ask for a seasonal landing page, new ad formats and delivery banners ahead of the festive peak. Recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   At Month 5 revenue is close to ₹20L. Budget keeps rising, more slowly than in the scaling months. Season-specific pages convert season traffic better than the default collection. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   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: tracking is checked against store orders and the first ads go live on a small daily budget. The store fix list is live, with reviews on product pages and a prepaid offer. Reported and store-side returns are reconciled.

   • Projected revenue ₹7.3L
   • Projected ROAS 2.84x
   • Planned ad spend ₹2.6L
2. Month 2

   The scaling phase opens: new creator and customer-feedback videos join the account. Budget holds and return on spend holds.

   • Projected revenue ₹8.2L
   • Projected ROAS 2.94x
   • Planned ad spend ₹2.8L
3. Month 3

   Cost caps and bid caps are tested against open bidding. Budget steps up and return on spend holds.

   • Projected revenue ₹10.3L
   • Projected ROAS 2.85x
   • Planned ad spend ₹3.6L
4. Month 4

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

   • Projected revenue ₹13.8L
   • Projected ROAS 2.79x
   • Planned ad spend ₹4.9L
5. Month 5

   Steady phase begins: creative refresh and repeat orders take on more of the work. Budget steps up and return on spend holds. Monthly revenue passes the halfway point between ₹8L and ₹20L. Revenue ends below ₹20L, the target set at enquiry, because the plan follows what the fastest tenth of our measured accounts reached. Each order costs about what it did while learning.

   • Projected revenue ₹15.5L
   • Projected ROAS 2.74x
   • Planned ad spend ₹5.7L

1. 01

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

2. 02

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

3. 03

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