Fashion & apparel (Bengaluru) case study: plan for ₹7L to ₹12.6L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at return on ad spend, 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 in Bengaluru that wants to grow monthly revenue in 3 months, from ₹7L to ₹25L (3.6x). 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. The problem named at booking was return on ad spend. Conversion rate caps what any ad budget can return. 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 3

   What we'd do

   Write month-by-month targets with the brand's team that climb from ₹7L to ₹25L, and open every review with the month-to-date number against them. Keep a shared daily sheet with the ad platform's revenue next to real store orders. Set targets on net sales after returns and cancellations, not on logged revenue.

   Why

   Its reported return on ad spend is under what measured fashion stores of that size hold; the plan begins from it and rebuilds return first. Written targets expose a slow month while there is still time to act. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   Written targets make each scaling decision explicit. The sheet is read before each budget change. Each review opens with net sales against the target.

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, with carousel watched closely since it returns less than the rest. Run every lead product in a campaign of its own, read every week. Run creator, customer-feedback and founder-led video. The learning month runs on a small daily budget, stepping up only once orders confirm.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. In most accounts we measured, video that carried trust held its return. Early spend buys learning, not scale.

   How it works

   Budget follows the products that sell. Low-quality UGC is pulled and founder-led video takes its place. Only confirmed orders at the low budget unlock the next step. 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 3

   What we'd do

   Scale through Month 2 to Month 3 as far toward ₹25L as return allows as the budget climbs in steps while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Tie every budget increase to return: step up while it holds, step back when it drops. Run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up.

   Why

   In Month 2 to Month 3 the modelled budget climbs in steps, while return on spend holds. Return holds through these months because each budget step stops where return would slip. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Each month's budget is set by the return the last one earned. The version that keeps cost per purchase lowest stays. 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.

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 daily sheet now matches platform revenue to store orders. Store fixes go live: reviews, trust pointers and the prepaid offer.

   • Projected revenue ₹7L
   • Projected ROAS 1.97x
   • Planned ad spend ₹3.6L
2. Month 2

   The scaling phase opens: bid-cap and cost-cap versions run beside open bidding. Budget steps up and return on spend holds.

   • Projected revenue ₹9.7L
   • Projected ROAS 1.95x
   • Planned ad spend ₹5L
3. Month 3

   The budget decision is taken on the return the last step earned. Budget steps up and return on spend holds. The plan finishes short of ₹25L: the plan follows what the fastest tenth of our measured accounts reached. Cost per purchase ends close to the learning phase.

   • Projected revenue ₹12.6L
   • Projected ROAS 1.96x
   • Planned ad spend ₹6.4L

1. 01

   Test bidding controls (cost caps, bid caps, CBO against ABO) side by side before scaling.

2. 02

   Net sales ran below logged store revenue after returns, cancellations and tax.

3. 03

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