Beauty & skincare case study: ₹4L to ₹6L–₹8L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with results that swing from month to month, which the brand in this scenario named first, before anything else on this smaller store. We split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. We use cart-value tiers so larger baskets earn a better offer.

   Why

   In this case study, a smaller beauty and skincare brand grows monthly revenue in 3 months, from ₹4L to ₹6L–₹8L (+75%). At least one in four of our measured accounts grew that fast over the same time. The main problem in this scenario is results that swing from month to month, with return on ad spend close behind. Without layers, retargeting quietly eats the prospecting budget. Each extra item in a basket is revenue the ad has already paid for.

   How it works

   Warm layers run beside prospecting, not instead of it. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We log store orders every day beside what the ad platform claims. We set the path from ₹4L to ₹6L–₹8L as written monthly numbers, and read every review against the month so far. We check the reported return on spend against store revenue before any budget moves.

   Why

   Its reported return on ad spend is higher than measured beauty and skincare stores of that size hold, so the case study 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. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   Every budget call starts from the store's orders. The month's number is on the page at every review. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, static image and catalogue ads, building to a few dozen new ads a month by the final month, when monthly revenue runs at about ₹6L–₹8L, with carousel watched closely since it returns less than the rest. We work in creative batches with a fixed read window each. We run every lead product in a campaign of its own, read every week. We make trust the subject of the video: creator reels, customer feedback and founder-led clips.

   Why

   The read window keeps creative spending tied to evidence. Products that do not sell show up inside a week, not after a month of shared budget. In most accounts we measured, video that carried trust held its return.

   How it works

   Only ads that convert inside the window keep running. 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. More spend buys more new ads, led by video ahead of static image.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹6L–₹8L: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We refresh tired ads by mixing old and new creatives as spend rises.

   Why

   In Month 2 to Month 3 the budget climbs in steps, 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. Frequency climbs with spend, and tired ads lose click-through first.

   How it works

   Budget follows return month to month instead of a fixed ramp. Proven ads stay while new ones are added. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Reported and store-side returns are reconciled. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹4.1L
   • ROAS 3.45x
   • Ad spend ₹1.2L
2. Month 2

   Scaling starts: products that do not sell are paused and budget moves to the winners. Spend steps up sharply, and return falls.

   • Revenue ₹5.2L
   • ROAS 2.59x
   • Ad spend ₹2L
3. Month 3

   Fresh ads are mixed in beside the proven set. Spend steps up sharply, and return dips. Monthly revenue reaches ₹6L–₹8L, the number this scenario calls for. Each order costs more by the end than it did while learning.

   • Revenue ₹7.2L
   • ROAS 2.28x
   • Ad spend ₹3.1L

1. 1

   Video carries most creative spend in this industry's measured accounts.

2. 2

   Test Static image alongside the main format: it reached the top creative tier most often in this industry's measured accounts.

3. 3

   Keep Video as the main format: it carried most creative spend and also reached the top creative tier most often in this industry's measured accounts.