Beauty & skincare case study: ₹10L to ₹23.6L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We focus the first month on turning visits into orders, the brand’s main problem, on a mid-sized base. We get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront. We use cart-value tiers so larger baskets earn a better offer.

   Why

   A mid-sized beauty and skincare brand grows monthly revenue in 3 months, from ₹10L to ₹30L (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 rather than forcing the number. The main problem in this scenario is turning visits into orders. 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 · Month 1 to 3

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We set the path from ₹10L to ₹30L as written monthly numbers, and read every review against the month so far.

   Why

   No starting return on ad spend was given; the starting return is what measured stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. A missed month 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.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, static image and catalogue ads first, rising to several 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 make trust the subject of the video: creator reels, customer feedback and founder-led clips. The learning month runs at a low daily budget and moves up only when orders come through.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Video built on trust was the format that held return in most measured accounts. Spend in the learning phase pays for information.

   How it works

   A product that does not sell is paused inside the week. Low-quality UGC is pulled and founder-led video takes its place. Only confirmed orders at the low budget unlock the next step. 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 as far toward ₹30L as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. We test cost caps, bid caps and CBO against ABO side by side before each budget step.

   Why

   In Month 2 to Month 3 the 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. 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. Controls run as parallel versions and the one that holds cost is kept. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Cart-value offers go live at checkout. Store orders and ad-platform revenue are checked side by side.

   • Revenue ₹9.8L
   • ROAS 5.04x
   • Ad spend ₹1.9L
2. Month 2

   Scaling begins: abandoned checkouts get WhatsApp follow-ups. Return dips as the budget steps up sharply.

   • Revenue ₹14.9L
   • ROAS 4.48x
   • Ad spend ₹3.3L
3. Month 3

   Cost caps and bid caps are tested against open bidding. Return dips as the budget steps up sharply. Monthly revenue passes the halfway point between ₹10L and ₹30L. Revenue ends below ₹30L, the number this scenario calls for, because the case study follows what the fastest tenth of our measured accounts reached. Each order costs more by the end than it did while learning.

   • Revenue ₹23.6L
   • ROAS 3.95x
   • Ad spend ₹6L

1. 1

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

2. 2

   Paused South India during Shravan and ran new creatives to the North

3. 3

   A low average order value limited how far spend could scale.