Home & kitchen case study: ₹65,000–₹75,000 to ₹4L monthly revenue in 2–3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We focus the first month on marketing and social presence, the brand’s main problem, on a smaller base. We open with three documents: a website audit, a creative brief and a media schedule by month. 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 2–3 months, from ₹65,000–₹75,000 to ₹10L (14.3x). 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 marketing and social presence. A written brief gives every later budget decision a reference point. A larger basket spreads the cost of each order over more revenue.

   How it works

   The brief is agreed first; spend follows it. Offers are tuned to the order values that already sell.

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 set the path from ₹65,000–₹75,000 to ₹10L as written monthly numbers, and read every review against the month so far. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   The return on ad spend it reported sits above what measured home and kitchen stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. Platform attribution over-counts, so budget decisions sit on the store-side number. 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 a few 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 brief creators for a steady run of UGC video, with regional-language cuts of the winners. 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. A steady supply keeps the testing layer fed, and regional cuts reach more buyers with the same idea. The first rupees are for finding the buyer, not for volume.

   How it works

   The interest stack runs beside a broad audience, read on the same video. Winning UGC is cut into regional languages before new ideas are bought. Only confirmed orders at the low budget unlock the next step. 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 toward ₹10L: the budget climbs steeply and return falls, 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 recover abandoned checkouts with WhatsApp messages as traffic grows.

   Why

   Across Month 2 to Month 3, the budget climbs steeply, and return on spend falls as it does. Budget is part-stepped in one 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

   Budget follows return month to month instead of a fixed ramp. WhatsApp recovery runs alongside paid retargeting, not instead of it. 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

   First, the set-up: tracking is checked against store orders and the first ads go live on a small daily budget. Store orders and platform revenue are reconciled in the shared sheet. The media schedule and creative brief are signed off.

   • Revenue ₹70,303
   • ROAS 3.90x
   • Ad spend ₹18,031
2. Month 2

   The scaling phase opens: regional-language cuts of the winning UGC go live. Budget more than doubles and return on spend falls.

   • Revenue ₹4L
   • ROAS 2.52x
   • Ad spend ₹1.6L
3. Month 3

   Abandoned checkouts get WhatsApp follow-ups. Budget rises to the point where return starts to give way: budget steps up and return on spend dips. The case study finishes short of ₹10L: budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹4L
   • ROAS 2.26x
   • Ad spend ₹1.8L

1. 1

   Cut regional-language versions of the winning creative for the best-selling states.

2. 2

   Tested cost caps and bid caps against open bidding

3. 3

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