Home & kitchen case study: ₹20,000 to ₹26,949 monthly revenue in 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 smaller base. 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. We add cart-value offers that step up at set basket sizes to lift order value.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 3 months, from ₹20,000 to ₹1L (5x). 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, holds budget where return starts to slip, and does not force the number. The main problem was keeping return on spend while scaling, followed by results that swing from month to month. Without layers, retargeting quietly eats the prospecting budget. Higher order value lowers the share of each order that goes to ads.

   How it works

   Each layer keeps its own budget line. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We write month-by-month revenue numbers with the brand's team that climb from ₹20,000 to ₹1L, and open every review with the month-to-date number against them. We keep a shared daily sheet with the ad platform's revenue next to real store orders. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured home and kitchen 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. Budget decisions are read off store numbers, not the ad platform alone. 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, catalogue ads and static image, building to about a dozen new ads a month by the final month, with no format far behind the account's return. The learning month runs at a low daily budget and moves up only when orders come through. 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

   Spend in the learning phase pays for information. A fixed window stops spend chasing a creative before it has been read. Shared campaigns hide weak products; separate ones expose them fast.

   How it works

   Spend steps up only after orders confirm at the low budget. Losing batches stop; winning ads take their budget. A product that does not sell is paused inside the week. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹1L as return allows: the budget rises gently while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We recover abandoned checkouts with WhatsApp messages as traffic grows.

   Why

   In Month 2 to Month 3 the budget rises gently, while return on spend holds. 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. Return holds through these months because each budget step stops where return starts to slip. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. WhatsApp recovery runs alongside paid retargeting, not instead of it. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   Learning month: a small daily budget carries the first ads, and store orders are matched to tracking. The daily sheet now matches platform revenue to store orders. The cart-value tiers are live.

   • Revenue ₹20,027
   • ROAS 1.99x
   • Ad spend ₹10,043
2. Month 2

   Scaling starts: a new batch goes live after the last one is read. Budget is raised only as far as return allows: return on spend dips while budget steps up.

   • Revenue ₹23,846
   • ROAS 1.89x
   • Ad spend ₹12,591
3. Month 3

   Budget shifts to the products that sold this period. Budget is raised only as far as return allows: return on spend rises while budget holds. ₹1L is not reached in the time, because budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹26,949
   • ROAS 1.98x
   • Ad spend ₹13,617

1. 1

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

2. 2

   Gave each product its own campaign

3. 3

   Ran each product as its own campaign and read results weekly