Home & kitchen case study: ₹20,000 to ₹24,364 monthly revenue in 3–4 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   Before budget rises, we get the smaller home and kitchen store ready to convert paid traffic. We start with a website audit, a creative brief and a monthly media schedule in the first week. We use cart-value tiers so larger baskets earn a better offer.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 3–4 months, from ₹20,000 to ₹5L (25x). 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 brand in this scenario named no single problem, so the case study starts from the numbers. Without a brief, each creative and budget decision starts from scratch. A larger basket spreads the cost of each order over more revenue.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 4

   What we do

   We set the path from ₹20,000 to ₹5L as written monthly numbers, and read every review against the month so far. We track ad-platform revenue beside store orders in a shared daily sheet. We set a written rule: no budget step in a month where return falls too far to pay for 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. A missed month shows up early instead of at the end of the case study. 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. Every budget call starts from the store's orders. A month whose return slips past that point keeps its budget instead.

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 every format close to the account's return. The learning month runs with a deliberately small daily budget until orders prove the buyer. We stack kitchen and home interests around a creator video of the lead product. We work in creative batches with a fixed read window each.

   Why

   Early spend buys learning, not scale. 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

   The low budget stays until orders confirm the buyer. The interest stack runs beside a broad audience, read on the same video. Batches that do not convert are cut; winners get the budget. 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 toward ₹5L: the budget rises gently and return dips, and Instagram Reels carries the most spend and Instagram Feed the next. We make regional-language versions of the winning video for the states that buy most. We let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the budget rises gently, and return on spend dips. In two of these months the step is trimmed to the size return can hold. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. The same winning idea reaches more buyers in their own language.

   How it works

   Regional cuts run beside the original winner. Each month's budget is set by the return the last one earned. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 4

   What we do

   From Month 4, we hold the gains and push toward ₹5L only as far as return allows. We recover abandoned checkouts and lift repeat orders with WhatsApp messages. We refresh tired ads by mixing old and new creatives, and keep a creative bank.

   Why

   Growth slows from Month 4, still short of ₹5L. Budget stays about level over these months. A message to someone who nearly bought costs less than an ad. A measured account showed click-through falling ahead of revenue, so tired ads are replaced early.

   How it works

   WhatsApp runs alongside paid retargeting, not instead of it. Refreshes are gradual: a few new ads at a time.

1. Month 1

   Learning month: the first ads run on a small daily budget while tracking is checked against store orders. Store orders and platform revenue are reconciled in the shared sheet. Cart-value offers go live at checkout.

   • Revenue ₹20,392
   • ROAS 2.04x
   • Ad spend ₹10,001
2. Month 2

   Scaling begins: the budget decision is taken on the return the last step earned. Only the part of the step that return supports is taken: with budget steps up, return on spend dips.

   • Revenue ₹24,055
   • ROAS 1.87x
   • Ad spend ₹12,845
3. Month 3

   The creative bank supplies this period's refresh. Only the part of the step that return supports is taken: with budget holds, return on spend holds.

   • Revenue ₹24,011
   • ROAS 1.88x
   • Ad spend ₹12,762
4. Month 4

   The case study moves into its steady phase, leaning on refreshed ads and repeat buyers. More budget does not pay at this return, so with budget holds, return on spend rises. ₹5L is not reached in the time, because budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase.

   • Revenue ₹24,364
   • ROAS 1.96x
   • Ad spend ₹12,419

1. 1

   Ran kitchen and home-brand interest stacks around an influencer video

2. 2

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

3. 3

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