Home & kitchen case study: ₹50,000 to ₹65,787 monthly revenue in 12 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with reaching new buyers, which the brand in this scenario named first, before anything else on this smaller store. We set basket-size offers that reward a second and third item in the cart. 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. We split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 1 year, from ₹50,000 to ₹5L (10x). That is faster than nine in ten of our measured accounts grew over the same time, so the case study below is on what that fastest tenth reached and shows where it lands against the number this scenario calls for. The main problem in this scenario is reaching new buyers. A larger basket spreads the cost of each order over more revenue. No budget returns more than the store converts.

   How it works

   Offers are tuned to the order values that already sell. Site fixes are handed over in the first weeks, before budget rises. Warm layers run beside prospecting, not instead of it.

2. 2

   Measurement & reviews · Month 1 to 12

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We agree a written number for every month on the way from ₹50,000 to ₹5L, and start each review with the month-to-date figure against it. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   No starting return on ad spend was given; the starting return is what measured home and kitchen stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   The sheet is read before each budget change. The month's number is on the page at every review. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and carousel first, rising to about a dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We stack kitchen and home interests around a creator video of the lead product. We separate the lead products into their own campaigns and review each one weekly. We run static images next to the video ads.

   Why

   A demonstration video explains a utility product faster than a static image. Products that do not sell show up inside a week, not after a month of shared budget. In every industry we measured, static images were the format most often in the top creative tier.

   How it works

   The interest stack runs beside a broad audience, read on the same video. Budget follows the products that sell. Statics join once a winning video is found. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 8

   What we do

   Through Month 2 to Month 8, we push toward ₹5L: the budget rises gently and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We build lookalikes from past buyers once purchases are steady, beside broad prospecting. We make regional-language versions of the winning video for the states that buy most. We raise budget only while return on spend holds, and cut it when return drops.

   Why

   In Month 2 to Month 8 the budget rises gently, and return on spend falls as it does. Four of these months stop their budget step before return slips too far. Spend stays flat for one of these months, waiting on return rather than on the calendar. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Past buyers are the clearest signal of who buys next.

   How it works

   Lookalike and broad audiences run the same ads, so they can be compared. Regional cuts run beside the original winner. Budget follows return month to month instead of a fixed ramp. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

5. 5

   Steady state · Month 9 to 12

   What we do

   From Month 9, we hold the gains and push toward ₹5L only as far as return allows. We hold a creative bank and swap tired ads out before click-through falls. We build lookalikes from repeat buyers and retarget past buyers with new arrivals.

   Why

   Growth slows from Month 9, still short of ₹5L. Spend holds roughly steady from here. In one account we measured, click-through fell before revenue did; refresh is how that is avoided. Repeat buyers are the cheapest orders an account gets.

   How it works

   The bank means a tired ad is replaced the week it tires. New arrivals go to past buyers first, before broad prospecting.

1. Month 1

   Learning month: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and platform revenue are reconciled in the shared sheet. The layered account is in place, with retargeting beside prospecting.

   • Revenue ₹50,605
   • ROAS 2.24x
   • Ad spend ₹22,632
2. Month 2

   Scaling starts: past buyers get the new arrivals ahead of prospecting. With budget steps up, return on spend dips.

   • Revenue ₹54,823
   • ROAS 2.14x
   • Ad spend ₹25,675
3. Month 3

   The weekly product read pauses the products that are not selling.

   • Revenue ₹66,652
   • ROAS 1.92x
   • Ad spend ₹34,770
4. Month 4

   Each budget move is read against the return it bought. With return short of where a budget step pays, with budget holds, return on spend holds.

   • Revenue ₹68,010
   • ROAS 1.96x
   • Ad spend ₹34,611
5. Month 5

   The winner now also runs in regional languages. The budget step stops where return starts to slip: while budget holds, return on spend holds.

   • Revenue ₹66,161
   • ROAS 1.89x
   • Ad spend ₹35,025
6. Month 6

   The creative bank supplies this period's refresh.

   • Revenue ₹64,646
   • ROAS 1.86x
   • Ad spend ₹34,702
7. Month 7

   Statics go live next to the winning video.

   • Revenue ₹65,207
   • ROAS 1.95x
   • Ad spend ₹33,439
8. Month 8

   A past-buyer lookalike audience joins broad prospecting.

   • Revenue ₹64,588
   • ROAS 1.97x
   • Ad spend ₹32,799
9. Month 9

   From here the work shifts to keeping ads fresh and bringing buyers back.

   • Revenue ₹65,090
   • ROAS 1.94x
   • Ad spend ₹33,490
10. Month 10

    New arrivals go to past buyers first.

    • Revenue ₹64,839
    • ROAS 1.90x
    • Ad spend ₹34,135
11. Month 11

    The weekly product read pauses the products that are not selling. With return short of where a budget step pays, with budget holds, return on spend holds.

    • Revenue ₹65,633
    • ROAS 1.95x
    • Ad spend ₹33,719
12. Month 12

    Budget is reviewed against return before the next step. With return short of where a budget step pays, with budget holds, return on spend holds. Revenue ends below ₹5L, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

    • Revenue ₹65,787
    • ROAS 1.95x
    • Ad spend ₹33,719

1. 1

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

2. 2

   Ran each product as its own campaign and read results weekly

3. 3

   Tracked store revenue and cost per order daily, beside Meta's own number