Home & kitchen case study: ₹2L–₹2.5L to ₹7.4L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is creative and content, so the opening month on this smaller store goes there. We use cart-value tiers so larger baskets earn a better offer. We get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 3 months, from ₹2L–₹2.5L to ₹10L (4.4x). 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 creative and content, with return on ad spend close behind. Higher order value lowers the share of each order that goes to ads. Conversion rate caps what any ad budget can return.

   How it works

   The tiers follow real order values, not round numbers. Site fixes are handed over in the first weeks, before budget rises.

2. 2

   Measurement & reviews · Month 1 to 3

   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 ₹2L–₹2.5L to ₹10L, and start each review with the month-to-date figure against it. We match the return the brand in this scenario reports to what the store actually took in, before touching budget.

   Why

   Its reported return on ad spend is in line with what measured home and kitchen stores of that size hold, so the case study starts there. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed month shows up early instead of at the end of the case study.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. Each budget step is argued against the written number. The reported return and the store-side return are read side by side every week.

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 work in creative batches with a fixed read window each. We run creator, customer-feedback and founder-led video. We keep creators producing UGC video on a steady schedule, and cut the winners into regional languages.

   Why

   A fixed window stops spend chasing a creative before it has been read. Video built on trust was the format that held return in most measured accounts. A steady supply keeps the testing layer fed, and regional cuts reach more buyers with the same idea.

   How it works

   Batches that do not convert are cut; winners get the budget. Low-quality UGC is pulled and founder-led video takes its place. Winning UGC is cut into regional languages before new ideas are bought. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹10L as return allows: the budget climbs in steps and return dips, and Instagram Reels carries the most spend and Instagram Feed the next. We raise budget only while return on spend holds, and cut it when return drops. We cut regional-language versions of the winning video for the best-selling states.

   Why

   Across Month 2 to Month 3, the budget climbs in steps, and return on spend dips. 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

   Budget follows return month to month instead of a fixed ramp. The regional versions run next to the original. 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.

1. Month 1

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

   • Revenue ₹2.2L
   • ROAS 2.35x
   • Ad spend ₹94,837
2. Month 2

   Scaling starts: regional-language versions of the winner go live. Spend more than doubles, and return falls.

   • Revenue ₹4.5L
   • ROAS 2.06x
   • Ad spend ₹2.2L
3. Month 3

   The winning UGC runs in regional-language versions. Spend steps up sharply, and return holds. Monthly revenue passes the halfway point between ₹2L–₹2.5L and ₹10L. ₹10L is not reached in the time, because the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends close to the learning phase.

   • Revenue ₹7.4L
   • ROAS 2.13x
   • Ad spend ₹3.5L

1. 1

   Facebook Reels carries the largest share of spend in this industry's measured accounts.

2. 2

   Ran each product as its own campaign and read results weekly

3. 3

   We stack kitchen and home interests around a creator video of the lead product.