Home & kitchen case study: ₹2L to ₹2.6L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with turning visits into orders, which the brand in this scenario named first, before anything else on this smaller store. We fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. 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 6 months, from ₹2L to ₹1Cr (50x). 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 was turning visits into orders, followed by marketing and social presence. Conversion rate caps what any ad budget can return. A larger basket spreads the cost of each order over more revenue.

   How it works

   Site fixes are handed over in the first weeks, before budget rises. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We log store orders every day beside what the ad platform claims. We agree a written number for every month on the way from ₹2L to ₹1Cr, and start each review with the month-to-date figure against it. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   No starting return on ad spend was given; the starting return is what measured home and kitchen stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. Written monthly numbers make each scaling decision explicit. 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 a few 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. The learning month runs with a deliberately small daily budget until orders prove the buyer. We make trust the subject of the video: creator reels, customer feedback and founder-led clips.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. The first rupees are for finding the buyer, not for volume. In most accounts we measured, video that carried trust held its return.

   How it works

   A product that does not sell is paused inside the week. The low budget stays until orders confirm the buyer. UGC that underperforms is replaced by founder-led video, not given more budget. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push toward ₹1Cr: 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 raise budget only while return on spend holds, and cut it when return drops.

   Why

   Across Month 2 to Month 4, the budget rises gently, and return on spend falls as it does. In three of these months the step is trimmed to the size return can hold. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. 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. Most spend reaches people who have not bought yet; past visitors 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 ₹1Cr where return permits. We recover abandoned checkouts and lift repeat orders with WhatsApp messages. We hold a creative bank and swap tired ads out before click-through falls.

   Why

   Growth slows from Month 5, still short of ₹1Cr. Budget stays about level over these months. A message to someone who nearly bought costs less than an ad. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Messages run beside retargeting ads. Refreshes are gradual: a few new ads at a time.

1. Month 1

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. The cart-value tiers are live. Reviews, trust pointers and the prepaid incentive are now on the store.

   • Revenue ₹1.9L
   • ROAS 2.16x
   • Ad spend ₹86,597
2. Month 2

   Scaling starts: new creator and customer-feedback videos join the account. The budget step stops where return starts to slip: budget steps up sharply and return on spend dips.

   • Revenue ₹2.6L
   • ROAS 1.96x
   • Ad spend ₹1.3L
3. Month 3

   Abandoned checkouts get WhatsApp follow-ups. The budget step stops where return starts to slip: budget holds and return on spend holds.

   • Revenue ₹2.7L
   • ROAS 1.94x
   • Ad spend ₹1.4L
4. Month 4

   Budget shifts to the products that sold this period.

   • Revenue ₹2.7L
   • ROAS 1.89x
   • Ad spend ₹1.4L
5. Month 5

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

   • Revenue ₹2.6L
   • ROAS 1.87x
   • Ad spend ₹1.4L
6. Month 6

   The creative bank supplies this period's refresh. The budget step stops where return starts to slip: budget holds and return on spend rises. The case study finishes short of ₹1Cr: budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹2.6L
   • ROAS 1.95x
   • Ad spend ₹1.3L

1. 1

   Gave each product its own campaign

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