Beauty & skincare case study: ₹1L to ₹1.4L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is turning visits into orders, so the opening month on this smaller store goes there. We fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. 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 ₹1L to ₹3L (3x). 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, holds budget where return starts to slip, and does not force the number. The main problem in this scenario is turning visits into orders, with a clear strategy close behind. No budget returns more than the store converts. A larger basket spreads the cost of each order over more revenue.

   How it works

   Site fixes are handed over in the first weeks, before budget rises. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We agree a written number for every month on the way from ₹1L to ₹3L, and start each review with the month-to-date figure against it. We track ad-platform revenue beside store orders in a shared daily sheet. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured beauty and skincare stores of that size hold. A missed month shows up early instead of at the end of the case study. The ad platform's own count runs high, so the store's count is the one that moves budget.

   How it works

   The month's number is on the page at every review. Every budget call starts from the store's orders. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, static image and catalogue ads first, rising to about two dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We run every lead product in a campaign of its own, read every week. We run creator, customer-feedback and founder-led video. We run static images next to the video ads.

   Why

   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. 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

   Budget follows the products that sell. UGC that underperforms is replaced by founder-led video, not given more budget. Statics join once a winning video is found. More spend buys more new ads, led by video ahead of static image.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 as far toward ₹3L as return allows as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. As spend rises, we mix new creatives in with the proven ones before the old ones tire.

   Why

   Across Month 2 to Month 3, the budget rises gently, while return on spend holds. In two of these months budget goes up only as far as return allows, so revenue grows more slowly than the brand's number needs. 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. Proven ads stay while new ones are added. In the final month, 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. Month 1

   Learning month: 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. Cart-value offers go live at checkout.

   • Revenue ₹1L
   • ROAS 1.94x
   • Ad spend ₹54,204
2. Month 2

   The scaling phase opens: abandoned checkouts get WhatsApp follow-ups. Budget is raised only as far as return allows: return on spend holds while budget steps up.

   • Revenue ₹1.3L
   • ROAS 1.99x
   • Ad spend ₹65,324
3. Month 3

   New creator and customer-feedback videos join the account. Budget is raised only as far as return allows: return on spend holds while budget steps up. The case study finishes short of ₹3L: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹1.4L
   • ROAS 2.00x
   • Ad spend ₹72,531

1. 1

   Instagram Reels carries the largest share of spend in this industry's measured accounts.

2. 2

   Refused to raise budget on sales campaigns alone

3. 3

   Ran kitchen and home-brand interest stacks around an influencer video