Home & kitchen case study: ₹60,000 to ₹74,504 monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with return on ad spend, which the brand in this scenario named first, before anything else on this smaller store. We use cart-value tiers so larger baskets earn a better offer. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 6 months, from ₹60,000 to ₹30L (50x). 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 return on ad spend. Higher order value lowers the share of each order that goes to ads. Every rupee of ads is capped by how well the store turns visits into orders.

   How it works

   Tier levels are set just above the basket sizes buyers already reach. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We raise budget in a month only while return holds; where it slips too far, we hold it. We agree a written number for every month on the way from ₹60,000 to ₹30L, and start each review with the month-to-date figure against it.

   Why

   With no return on ad spend reported, the case study begins at the level measured home and kitchen stores of that size hold. The ad platform's own count runs high, so the store's count is the one that moves budget. Measured store step-ups often gave back a quarter of their return, so the case study takes a step only while return is holding.

   How it works

   Every budget call starts from the store's orders. A month whose return slips past that point keeps its budget instead. Written monthly numbers make each scaling decision explicit.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, 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 stack kitchen and home interests around a creator video of the lead product. We buy creative in batches and give each batch a fixed read window before buying more.

   Why

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

   How it works

   Budget follows the products that sell. The interest stack runs beside a broad audience, read on the same video. Losing batches stop; winning ads take their budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 toward ₹30L as the budget rises gently while return holds, 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 make regional-language versions of the winning video for the states that buy most.

   Why

   Across Month 2 to Month 4, the budget rises gently, while return on spend holds. In three of these months budget goes up only as far as return allows, so revenue grows more slowly than the agreed number needs. Each budget step here is sized so that return stays close to where it was. Buyers respond to the same idea in their own language.

   How it works

   Each month's budget is set by the return the last one earned. The regional versions run next to the original. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we protect the revenue already built and move toward ₹30L where return permits. We hold a creative bank and swap tired ads out before click-through falls. We hold budget where return peaks instead of pushing past it.

   Why

   Growth slows from Month 5, still short of ₹30L. Spend holds roughly steady from here. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional. Most of our engagements saw return fall back from its best month.

   How it works

   Refreshes are gradual: a few new ads at a time. Budget stays at the level of the best return and is trimmed when it slips.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. The cart-value tiers are live. The store fix list is live, with reviews on product pages and a prepaid offer.

   • Revenue ₹60,434
   • ROAS 1.88x
   • Ad spend ₹32,128
2. Month 2

   Scaling starts: products that do not sell are paused and budget moves to the winners. Only the part of the step that return supports is taken: spend holds, and return holds.

   • Revenue ₹66,616
   • ROAS 1.97x
   • Ad spend ₹33,771
3. Month 3

   Budget is held at the level where return peaked.

   • Revenue ₹67,839
   • ROAS 1.90x
   • Ad spend ₹35,684
4. Month 4

   Each budget move is read against the return it bought.

   • Revenue ₹71,973
   • ROAS 1.93x
   • Ad spend ₹37,350
5. Month 5

   The case study moves into its steady phase, leaning on refreshed ads and repeat buyers.

   • Revenue ₹73,980
   • ROAS 1.95x
   • Ad spend ₹37,906
6. Month 6

   The winner now also runs in regional languages. Only the part of the step that return supports is taken: spend holds, and return holds. The case study finishes short of ₹30L: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹74,504
   • ROAS 1.94x
   • Ad spend ₹38,307

1. 1

   Cut regional-language versions of the winning creative for the best-selling states.

2. 2

   Track ad-platform revenue beside store revenue (and net sales) daily or weekly, and set the numbers on the lower figure.

3. 3

   Give each product (or colour) its own campaign, read weekly, and move budget to the winner.