Home & kitchen case study: ₹18L to ₹34.3L monthly revenue in 2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   We give the first week to results that swing from month to month, 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 use cart-value tiers so larger baskets earn a better offer.

   Why

   A mid-sized home and kitchen brand grows monthly revenue in 2 months, from ₹18L to ₹50L (2.8x). 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 was results that swing from month to month, followed by scaling ad spend. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. A larger basket spreads the cost of each order over more revenue.

   How it works

   Each layer keeps its own budget line. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Week 1 to 9

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We write week-by-week revenue numbers with the brand's team that climb from ₹18L to ₹50L, and open every review with the week-to-date number against them.

   Why

   With no return on ad spend reported, the case study begins at the level measured stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed week shows up early instead of at the end of the case study.

   How it works

   Every budget call starts from the store's orders. Written monthly numbers make each scaling decision explicit.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We test with video, catalogue ads and static image first, rising to several dozen new ads a month by the last four weeks, with carousel watched closely since it returns less than the rest. 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. We stack kitchen and home interests around a creator video of the lead product.

   Why

   A fixed window stops spend chasing a creative before it has been read. Shared campaigns hide weak products; separate ones expose them fast. A demonstration video explains a utility product faster than a static image.

   How it works

   Batches that do not convert are cut; winners get the budget. A product that does not sell is paused inside the week. The interest stack runs beside a broad audience, read on the same video. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Week 5 to 9

   What we do

   We scale through Week 5 to Week 9 as far toward ₹50L as return allows as the budget climbs steeply and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We raise budget only while return on spend holds, and cut it when return drops. We run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up.

   Why

   In Week 5 to Week 9 the budget climbs steeply, 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. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   Budget follows return week to week instead of a fixed ramp. Controls run as parallel versions and the one that holds cost is kept. Instagram Reels carries the largest share of spend in the last four weeks, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Week 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. The cart-value tiers are live. The layered account is in place, with retargeting beside prospecting.

   • Revenue ₹4.4L
   • ROAS 4.91x
   • Ad spend ₹88,776
2. Week 2

   First creative read: ads that do not convert are cut. Return on spend rises while budget holds.

   • Revenue ₹4.2L
   • ROAS 5.22x
   • Ad spend ₹80,891
3. Week 3

   Store fixes and offers go live while orders confirm. Return on spend falls while budget steps up.

   • Revenue ₹4.2L
   • ROAS 4.55x
   • Ad spend ₹92,105
4. Week 4

   By the end of learning, the buyer is known and one ad has won. Return on spend rises while budget holds.

   • Revenue ₹4.2L
   • ROAS 4.95x
   • Ad spend ₹85,001
5. Week 5

   Scaling starts: bid-cap and cost-cap versions run beside open bidding. Return on spend holds while budget holds.

   • Revenue ₹4.4L
   • ROAS 4.94x
   • Ad spend ₹88,473
6. Week 6

   Products that do not sell are paused and budget moves to the winners. Return on spend dips while budget steps up.

   • Revenue ₹5.3L
   • ROAS 4.48x
   • Ad spend ₹1.2L
7. Week 7

   This batch's read is done: winners stay, the rest are cut. Return on spend dips while budget steps up sharply.

   • Revenue ₹7.1L
   • ROAS 3.99x
   • Ad spend ₹1.8L
8. Week 8

   Each budget move is read against the return it bought.

   • Revenue ₹10L
   • ROAS 3.56x
   • Ad spend ₹2.8L
9. Week 9

   Cost caps and bid caps are tested against open bidding. Return on spend holds while budget steps up. Revenue over the last four weeks passes the halfway point between ₹18L and ₹50L. Revenue ends below ₹50L, the number this scenario calls for, because the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹11.9L
   • ROAS 3.45x
   • Ad spend ₹3.4L

1. 1

   We stack kitchen and home interests around a creator video of the lead product.

2. 2

   Ran each product as its own campaign and read results weekly

3. 3

   Ran regional-language versions of the winning creative (6.9x)