Beauty & skincare case study: ₹3L to ₹3.7L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   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. 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 add cart-value offers that step up at set basket sizes to lift order value.

   Why

   In this case study, a smaller beauty and skincare brand grows monthly revenue in 3 months, from ₹3L to ₹30L (10x). 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. No budget returns more than the store converts. Higher order value lowers the share of each order that goes to ads.

   How it works

   Site fixes are handed over in the first weeks, before budget rises. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We agree a written number for every month on the way from ₹3L to ₹30L, and start each review with the month-to-date figure against it. We log store orders every day beside what the ad platform claims. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   No starting return on ad spend was given; the starting return is what measured beauty and skincare stores of that size hold. A shortfall is caught in the month it happens, not at the end. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   Written monthly numbers make each scaling decision explicit. The sheet is read before each budget change. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, static image and catalogue ads, building 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 give each lead product its own campaign and read it weekly. We run creator, customer-feedback and founder-led video. The learning month runs on a small daily budget, stepping up only once orders confirm.

   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. 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. The low budget stays until orders confirm the buyer. 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 ₹30L: the budget rises gently and return dips, and Instagram Reels carries the most spend and Instagram Feed the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. We let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the budget rises gently, and return on spend dips. 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. 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. Each month's budget is set by the return the last one earned. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   Learning month: the first ads run on a small daily budget while tracking is checked against store orders. The daily sheet now matches platform revenue to store orders. Cart-value offers go live at checkout.

   • Revenue ₹3L
   • ROAS 1.98x
   • Ad spend ₹1.5L
2. Month 2

   Scaling starts: abandoned checkouts get WhatsApp follow-ups. Budget goes up only as far as return can carry it: with budget steps up, return on spend steps up.

   • Revenue ₹3.4L
   • ROAS 1.89x
   • Ad spend ₹1.8L
3. Month 3

   The weekly product read pauses the products that are not selling. Budget goes up only as far as return can carry it: with budget holds, return on spend holds. Revenue ends below ₹30L, the number this scenario calls for, because budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase.

   • Revenue ₹3.7L
   • ROAS 1.89x
   • Ad spend ₹2L

1. 1

   Instagram Reels carries the largest share of spend in measured accounts across all industries.

2. 2

   Catalogue ads tested state-wise and city-wise, 1% lookalikes, Diwali retargeting

3. 3

   Fix the store before scaling: trust pointers, reviews, return window, about page, prepaid incentive, bundle and cart-value offers.