Home & kitchen case study: ₹70,000–₹90,000 to ₹1.1L monthly revenue in 6 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 put the website audit, the creative brief and the monthly media schedule in place before spend moves. 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 6 months, from ₹70,000–₹90,000 to ₹5L+ (6.2x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study follows the fastest tenth's pace, holds budget where return starts to slip, and shows the gap to the brand's number honestly. No single problem was named; the case study works from the figures instead. Later budget calls need something written to be judged against. A larger basket spreads the cost of each order over more revenue.

   How it works

   The brief is agreed first; spend follows it. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We agree a written number for every month on the way from ₹70,000–₹90,000 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. The ad platform's own count runs high, so the store's count is the one that moves budget. 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 static image 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 give each lead product its own campaign and read it weekly. The learning month runs at a low daily budget and moves up only when orders come through.

   Why

   A demonstration video explains a utility product faster than a static image. Shared campaigns hide weak products; separate ones expose them fast. Early spend buys learning, not scale.

   How it works

   The interest stack runs beside a broad audience, read on the same video. Budget follows the products that sell. The low budget stays until orders confirm the buyer. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push as far toward ₹5L+ as return allows: the budget rises gently and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We recover abandoned checkouts with WhatsApp messages as traffic grows. We tie every budget increase to return: we step up while it holds, and step back when it drops.

   Why

   In Month 2 to Month 4 the budget rises gently, and return on spend falls as it does. Budget is part-stepped in two of these months, taking only what return can carry. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   WhatsApp recovery runs alongside paid retargeting, not instead of it. 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. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we protect the revenue already built and move toward ₹5L+ where return permits. We refresh tired ads by mixing old and new creatives, and keep a creative bank. We build lookalikes from repeat buyers and retarget past buyers with new arrivals.

   Why

   Growth slows from Month 5, still short of ₹5L+. Budget stays about level over these months. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional. An order from a returning buyer costs the least.

   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

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. The daily sheet now matches platform revenue to store orders. Basket-size offers switch on at checkout.

   • Revenue ₹78,782
   • ROAS 2.26x
   • Ad spend ₹34,886
2. Month 2

   Scaling begins: the budget decision is taken on the return the last step earned. Return dips as the budget steps up.

   • Revenue ₹95,926
   • ROAS 2.01x
   • Ad spend ₹47,837
3. Month 3

   Budget shifts to the products that sold this period. Only the part of the step that return supports is taken: return dips as the budget dips.

   • Revenue ₹1.1L
   • ROAS 1.92x
   • Ad spend ₹57,457
4. Month 4

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

   • Revenue ₹1.1L
   • ROAS 1.96x
   • Ad spend ₹56,434
5. Month 5

   Steady phase begins: creative refresh and repeat orders take on more of the work.

   • Revenue ₹1.1L
   • ROAS 1.87x
   • Ad spend ₹59,079
6. Month 6

   Abandoned checkouts get WhatsApp follow-ups. Only the part of the step that return supports is taken: return holds as the budget holds. Revenue ends below ₹5L+, the number this scenario calls for, because budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹1.1L
   • ROAS 1.94x
   • Ad spend ₹57,123

1. 1

   Gave each product its own campaign

2. 2

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

3. 3

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