Beauty & skincare case study: ₹15L to ₹32.3L monthly revenue in 2–3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We focus the first month on keeping return on spend while scaling, the brand’s main problem, on a mid-sized base. We layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing. We add cart-value offers that step up at set basket sizes to lift order value.

   Why

   A mid-sized beauty and skincare store grows monthly revenue in 2–3 months, from ₹15L to ₹40L (2.7x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The main problem in this scenario is keeping return on spend while scaling, with creative and content close behind. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Higher order value lowers the share of each order that goes to ads.

   How it works

   Retargeting sits next to prospecting and never replaces it. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We log store orders every day beside what the ad platform claims. We agree a written number for every month on the way from ₹15L to ₹40L, and start each review with the month-to-date figure against it. We match the return the brand in this scenario reports to what the store actually took in, before touching budget.

   Why

   Its reported return on ad spend is under what measured stores of that size hold; the case study begins from it and rebuilds return first. The brand in this scenario also asked for a return on ad spend of 3x; the case study ends below that. Return gives way as spend rises, so that return and this revenue do not come together in the time. Platform attribution over-counts, so budget decisions sit on the store-side number. A shortfall is caught in the month it happens, not at the end.

   How it works

   The sheet is read before each budget change. Each budget step is argued against the written number. The store-side return becomes the number every review uses.

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, with carousel watched closely since it returns less than the rest. We make trust the subject of the video: creator reels, customer feedback and founder-led clips. We work in creative batches with a fixed read window each. We separate the lead products into their own campaigns and review each one weekly.

   Why

   In most accounts we measured, video that carried trust held its return. The read window keeps creative spending tied to evidence. Shared campaigns hide weak products; separate ones expose them fast.

   How it works

   UGC that underperforms is replaced by founder-led video, not given more budget. Losing batches stop; winning ads take their budget. Losing products are paused within a week and budget moves to the winners. New ads rise with the budget, most of them video, then static image.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 as far toward ₹40L as return allows as the budget climbs in steps while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. We tie every budget increase to return: we step up while it holds, and step back when it drops.

   Why

   In Month 2 to Month 3 the budget climbs in steps, while return on spend holds. In one of these months the step is trimmed to the size return can hold. Each budget step here is sized so that return stays close to where it was. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   The version that keeps cost per purchase lowest stays. Budget follows return month to month instead of a fixed ramp. 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

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. The reported return is checked against the store-side return. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹15.8L
   • ROAS 1.96x
   • Ad spend ₹8.1L
2. Month 2

   The scaling phase opens: each budget move is read against the return it bought. Return on spend holds while budget steps up sharply.

   • Revenue ₹24.8L
   • ROAS 2.01x
   • Ad spend ₹12.4L
3. Month 3

   This batch's read is done: winners stay, the rest are cut. The budget step stops where return starts to slip: return on spend holds while budget steps up. Halfway from ₹15L to ₹40L is passed. The case study finishes short of ₹40L: the case study follows what the fastest tenth of our measured accounts reached. Each order costs about what it did while learning. Against the 3x this scenario calls for, the case study finishes below.

   • Revenue ₹32.3L
   • ROAS 1.96x
   • Ad spend ₹16.4L

1. 1

   Test UGC / creator video alongside the main format: it reached the top creative tier most often in this industry's measured accounts.

2. 2

   Reconciled ad-platform and Shopify figures every month after cancellations

3. 3

   Audiences built on the buyer's life and profession rather than health-condition interests