Beauty & skincare case study: plan for ₹60L to ₹84.5L monthly revenue in 1–2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we'd do

   Start with scaling ad spend, which the booking named first, before anything else on this established store. Build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. Fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive.

   Why

   An established beauty and skincare store asked us how to grow monthly revenue in 1–2 months, from ₹60L to ₹1.5Cr (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 scaling ad spend. Without layers, retargeting quietly eats the prospecting budget. Every rupee of ads is capped by how well the store turns visits into orders.

   How it works

   Retargeting sits next to prospecting and never replaces it. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & targets · Week 1 to 9

   What we'd do

   Report net sales after returns, cancellations and tax next to store revenue and platform revenue. Set the path from ₹60L to ₹1.5Cr as written weekly targets, and read every review against the week so far.

   Why

   It did not report a return on ad spend, so the plan starts from what measured stores of that size hold. Returns, cancellations and tax cut logged revenue, so targets are set on net sales. A missed week shows up early instead of at the end of the plan.

   How it works

   Budget decisions are read off net sales, not the ad platform alone. Written targets make each scaling decision explicit.

3. 3

   Creative testing · Week 1 to 4

   What we'd do

   Test with video, static image and catalogue ads first, rising to several dozen new ads a month by the last four weeks, with carousel watched closely since it returns less than the rest. Separate the lead products into their own campaigns and review each one weekly. The four learning weeks run on a small daily budget, stepping up only once orders confirm. Work in creative batches with a fixed read window each.

   Why

   A product nobody buys is visible within a week when it has its own campaign. The first rupees are for finding the buyer, not for volume. Buying more before a batch is read means paying for guesses.

   How it works

   A product that does not sell is paused inside the week. Spend steps up only after orders confirm at the low budget. Only ads that convert inside the window keep running. New ads rise with the budget, most of them video, then static image.

4. 4

   Scaling · Week 5 to 9

   What we'd do

   Scale through Week 5 to Week 9 as far toward ₹1.5Cr as return allows as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Test cost caps, bid caps and CBO against ABO side by side before each budget step. Let return decide budget: more while it holds, less when it slips.

   Why

   Across Week 5 to Week 9, the modelled budget climbs in steps, and return on spend falls as it does. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. Each week's budget is set by the return the last one earned. Instagram Reels carries the largest share of spend in the last four weeks, with Instagram Feed next. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   Learning starts: a small daily budget carries the first ads, and store orders are matched to tracking. Net sales after returns are matched to logged revenue. The account runs in layers: testing, scaling and retargeting.

   • Projected revenue ₹14L
   • Projected ROAS 3.90x
   • Planned ad spend ₹3.6L
2. Week 2

   First creative read: ads that do not convert are cut. With budget holds, return on spend holds.

   • Projected revenue ₹14.3L
   • Projected ROAS 3.94x
   • Planned ad spend ₹3.6L
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Projected revenue ₹14.4L
   • Projected ROAS 3.66x
   • Planned ad spend ₹3.9L
4. Week 4

   The learning phase ends with a buyer identified and a winning ad chosen.

   • Projected revenue ₹13.4L
   • Projected ROAS 3.55x
   • Planned ad spend ₹3.8L
5. Week 5

   The scaling phase opens: budget shifts to the products that sold this period. With budget holds, return on spend holds.

   • Projected revenue ₹14.2L
   • Projected ROAS 3.50x
   • Planned ad spend ₹4.1L
6. Week 6

   Budget is reviewed against return before the next step. With budget steps up, return on spend dips.

   • Projected revenue ₹16.8L
   • Projected ROAS 3.28x
   • Planned ad spend ₹5.1L
7. Week 7

   A new batch goes live after the last one is read.

   • Projected revenue ₹20.1L
   • Projected ROAS 3.01x
   • Planned ad spend ₹6.7L
8. Week 8

   Bid-cap and cost-cap versions run beside open bidding.

   • Projected revenue ₹23.6L
   • Projected ROAS 2.91x
   • Planned ad spend ₹8.1L
9. Week 9

   Budget shifts to the products that sold this period. With budget holds, return on spend holds. The plan finishes short of ₹1.5Cr: the plan follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹23.9L
   • Projected ROAS 2.80x
   • Planned ad spend ₹8.5L

1. 01

   Test UGC / creator video alongside the main format: it reached the top creative tier most often in this industry's measured accounts.

2. 02

   Rebuilt the account: a new-audience campaign excluding past audiences, past-buyer retargeting, one campaign per winning product

3. 03

   Add static images beside video once a winning video is found.