Beauty & skincare case study: ₹30,000–₹35,000 to ₹29,426 monthly revenue in 1 month

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   We start with turning visits into orders, which the brand in this scenario named first, before anything else on this smaller store. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a smaller beauty and skincare brand grows monthly revenue in 1 month, from ₹30,000–₹35,000 to ₹1.2L–₹1.5L (4.2x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem in this scenario is turning visits into orders, with reaching new buyers close behind. Conversion rate caps what any ad budget can return. Higher order value lowers the share of each order that goes to ads.

   How it works

   The fix list goes to the brand's team in the opening weeks, ahead of any budget step. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Week 1 to 4

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We agree a written number for every week on the way from ₹30,000–₹35,000 to ₹1.2L–₹1.5L, and start each review with the week-to-date figure against it. We read results in three-to-four-day windows, since the case study runs week by week.

   Why

   The return on ad spend it reported is below the level where raising budget pays for a store of that size, so budget does not rise until return climbs. The brand in this scenario also asked for a return on ad spend of 2x; the case study ends just below that. The ad platform's own count runs high, so the store's count is the one that moves budget. A missed week shows up early instead of at the end of the case study.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. Written monthly numbers make each scaling decision explicit. Each window is read on purchases and cost per purchase before the next move.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We lead the testing layer with static image, video and carousel, building to a handful of new ads a month by the last four weeks, with carousel watched closely since it returns less than the rest. We run every lead product in a campaign of its own, read every week. We lead with video that carries trust: creators, customer feedback and founder-led pieces. We test static images beside video.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Trust-carrying video held return in most measured accounts. Among the beauty and skincare accounts we measured, UGC and creator video was the format most often in the top creative tier.

   How it works

   Losing products are paused within a week and budget moves to the winners. Weak UGC is swapped for founder-led video rather than scaled. Statics join once a winning video is found. The number of new ads grows with spend; static image makes up the largest part and video the next.

4. 4

   Scaling · Week 4

   What we do

   Through Week 4, we push toward ₹1.2L–₹1.5L: the budget stays close to the learning level while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. We build lookalikes from past buyers once purchases are steady, beside broad prospecting.

   Why

   In Week 4 the budget stays close to the learning level, 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. Lookalike and broad audiences run the same ads, so they can be compared. In the last four weeks, Instagram Reels takes the most spend and Instagram Feed the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   Learning starts: tracking is checked against store orders and the first ads go live on a small daily budget. Store fixes go live: reviews, trust pointers and the prepaid offer. Store orders and ad-platform revenue are checked side by side.

   • Revenue ₹7,193
   • ROAS 1.92x
   • Ad spend ₹3,747
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Spend holds, and return rises.

   • Revenue ₹7,054
   • ROAS 2.00x
   • Ad spend ₹3,520
3. Week 3

   Offers and store fixes switch on as the first orders confirm. Spend steps up, and return dips.

   • Revenue ₹7,732
   • ROAS 1.86x
   • Ad spend ₹4,150
4. Week 4

   The learning phase ends with a buyer identified and a winning ad chosen. Spend holds, and return holds. The case study finishes short of ₹1.2L–₹1.5L: budget stops rising where return starts to slip. Each order costs about what it did while learning. The final return on spend is just below the 2x this scenario calls for.

   • Revenue ₹7,447
   • ROAS 1.92x
   • Ad spend ₹3,875

1. 1

   Retargeted video viewers and unfinished form fills; built lookalikes from converted leads

2. 2

   Audit competitors and agree a three-month case study before spending.

3. 3

   Scaled into winter against written daily spend numbers