Beauty & skincare case study: plan for ₹10L–₹15L to ₹1.23Cr monthly revenue in 6–12 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Start with keeping return on spend while scaling, which the booking named first, before anything else on this mid-sized store. Split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. Use cart-value tiers so larger baskets earn a better offer. Fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive.

   Why

   The enquiry came from a mid-sized beauty and skincare brand that wants to grow monthly revenue in 6–12 months, from ₹10L–₹15L to ₹5Cr–₹10Cr (60x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the plan is built on what the fastest tenth reached, and shows the gap to the target honestly. At booking, the brand named keeping return on spend while scaling as its main problem, with return on ad spend close behind. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Each extra item in a basket is revenue the ad has already paid for.

   How it works

   Retargeting sits next to prospecting and never replaces it. Offers are tuned to the order values that already sell. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & targets · Month 1 to 12

   What we'd do

   Keep a shared daily sheet with the ad platform's revenue next to real store orders. Set a written rule: no budget step in a month where return would fall too far to pay for it. Set the path from ₹10L–₹15L to ₹5Cr–₹10Cr as written monthly targets, and read every review against the month so far.

   Why

   Its reported return on ad spend is higher than measured stores of that size hold, so the plan starts there and lets part of it ease as budget grows. Ad platforms claim more orders than stores record, so the store number decides budget. In the store accounts we measured, one budget step-up in four lost about a quarter of its return or more, so a step is taken only while return holds.

   How it works

   Every budget call starts from the store's orders. Where return would slip too far, the month keeps last month's budget. Each budget step is argued against the written target.

3. 3

   Creative testing · Month 1

   What we'd do

   Lead the testing layer with video, static image and catalogue ads, building to well over a hundred new ads a month by the final month, with carousel watched closely since it returns less than the rest. Give each lead product its own campaign and read it weekly. The learning month runs with a deliberately small daily budget until orders prove the buyer. Run creator, customer-feedback and founder-led video.

   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. Trust-carrying video held return in most measured accounts.

   How it works

   Budget follows the products that sell. The low budget stays until orders confirm the buyer. UGC that underperforms is replaced by founder-led video, not given more budget. More spend buys more new ads, led by video ahead of static image.

4. 4

   Scaling · Month 2 to 8

   What we'd do

   Through Month 2 to Month 8, push toward ₹5Cr–₹10Cr: the budget climbs steeply and return falls, and Instagram Reels carries the most 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. As spend rises, mix new creatives in with the proven ones before the old ones tire.

   Why

   In Month 2 to Month 8 the modelled budget climbs steeply, and return on spend falls as it does. In one of these months the step is trimmed to the size return can hold. Budget does not rise in one of these months: at that return, more spend would not pay. 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

   The version that keeps cost per purchase lowest stays. Each month's budget is set by the return the last one earned. Proven ads stay while new ones are added. 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 9 to 12

   What we'd do

   From Month 9, protect the revenue already built and move toward ₹5Cr–₹10Cr where return permits. Hold budget where return peaks instead of pushing past it. Keep a bank of ready creatives and rotate them in as ads tire.

   Why

   From Month 9 growth slows while revenue is still under ₹5Cr–₹10Cr. Budget stays about level over these months. Most of our engagements saw return fall back from its best month. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Budget is held while return is at its best and cut back when it slips. New creatives mix with proven ones rather than replacing them all at once.

1. Month 1

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. Cart-value offers go live at checkout. Store orders and ad-platform revenue are checked side by side.

   • Projected revenue ₹11.9L
   • Projected ROAS 4.44x
   • Planned ad spend ₹2.7L
2. Month 2

   Scaling starts: the weekly product read pauses the products that are not selling. Return on spend dips while budget steps up.

   • Projected revenue ₹14.9L
   • Projected ROAS 4.17x
   • Planned ad spend ₹3.6L
3. Month 3

   Ads with falling click-through are swapped from the bank.

   • Projected revenue ₹19.9L
   • Projected ROAS 3.96x
   • Planned ad spend ₹5L
4. Month 4

   Each budget move is read against the return it bought. Return on spend falls while budget steps up sharply.

   • Projected revenue ₹33.9L
   • Projected ROAS 3.38x
   • Planned ad spend ₹10L
5. Month 5

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

   • Projected revenue ₹58.8L
   • Projected ROAS 3.19x
   • Planned ad spend ₹18.4L
6. Month 6

   New creator and customer-feedback videos join the account. Return on spend falls while budget more than doubles.

   • Projected revenue ₹1.19Cr
   • Projected ROAS 2.80x
   • Planned ad spend ₹42.4L
7. Month 7

   New creator and customer-feedback videos join the account. Budget rises to the point where return would start to give way: return on spend holds while budget holds.

   • Projected revenue ₹1.2Cr
   • Projected ROAS 2.81x
   • Planned ad spend ₹42.6L
8. Month 8

   New creatives join the proven ones. Return is still below the level where more budget pays, so return on spend holds while budget holds.

   • Projected revenue ₹1.2Cr
   • Projected ROAS 2.86x
   • Planned ad spend ₹42L
9. Month 9

   Steady phase begins: creative refresh and repeat orders take on more of the work.

   • Projected revenue ₹1.23Cr
   • Projected ROAS 2.92x
   • Planned ad spend ₹42L
10. Month 10

    The weekly product read pauses the products that are not selling.

    • Projected revenue ₹1.27Cr
    • Projected ROAS 3.03x
    • Planned ad spend ₹41.8L
11. Month 11

    The creative bank supplies this period's refresh.

    • Projected revenue ₹1.21Cr
    • Projected ROAS 2.92x
    • Planned ad spend ₹41.5L
12. Month 12

    Each budget move is read against the return it bought. Return is still below the level where more budget pays, so return on spend holds while budget holds. Revenue ends below ₹5Cr–₹10Cr, the target set at enquiry, because budget stops rising where return would slip. Cost per purchase ends higher than in the learning phase.

    • Projected revenue ₹1.23Cr
    • Projected ROAS 2.97x
    • Planned ad spend ₹41.5L

1. 01

   Expect Instagram Reels to carry the largest share of spend in measured accounts across all industries.

2. 02

   Audiences built on the buyer's life and profession rather than health-condition interests

3. 03

   Relaunched campaigns on a new pixel the moment hostile comments appeared