Home & kitchen case study: ₹1.5L to ₹2.9L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We give the first month to creative and content, the brand’s main problem, on a smaller base. We set basket-size offers that reward a second and third item in the cart. We start with a website audit, a creative brief and a monthly media schedule in the first week.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 3 months, from ₹1.5L to ₹25L (16.7x). 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 marketing and social presence close behind. A larger basket spreads the cost of each order over more revenue. Without a brief, each creative and budget decision starts from scratch.

   How it works

   The tiers follow real order values, not round numbers. All three are shared with the brand's team before any budget step.

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 ₹1.5L to ₹25L as written monthly numbers, and read every review against the month so far. We match the return the brand in this scenario reports to what the store actually took in, before touching budget.

   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. Ad platforms claim more orders than stores record, so the store number decides budget. A missed month shows up early instead of at the end of the case study.

   How it works

   Every budget call starts from the store's orders. Written monthly numbers make each scaling decision explicit. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building 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 make trust the subject of the video: creator reels, customer feedback and founder-led clips. We brief creators for a steady run of UGC video, with regional-language cuts of the winners.

   Why

   A fixed window stops spend chasing a creative before it has been read. Trust-carrying video held return in most measured accounts. Regular UGC keeps testing going, and a regional cut stretches a winning idea further.

   How it works

   Batches that do not convert are cut; winners get the budget. Weak UGC is swapped for founder-led video rather than scaled. Regional cuts of a winner come before new ideas. 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 3

   What we do

   We scale through Month 2 to Month 3 toward ₹25L as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of 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

   Across Month 2 to Month 3, the budget climbs in steps, and return on spend falls as it does. In two of these months budget goes up only as far as return allows, so revenue grows more slowly than the brand's number needs. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Buyers respond to the same idea in their own language.

   How it works

   Regional cuts run beside the original winner. Budget follows return month to month instead of a fixed ramp. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. The store-side return is now the one budget follows. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹1.5L
   • ROAS 3.50x
   • Ad spend ₹41,536
2. Month 2

   The scaling phase opens: the winning UGC runs in regional-language versions. Only the part of the step that return supports is taken: return falls as the budget falls.

   • Revenue ₹2.6L
   • ROAS 2.51x
   • Ad spend ₹1L
3. Month 3

   Budget is reviewed against return before the next step. Only the part of the step that return supports is taken: return dips as the budget dips. ₹25L is not reached in the time, because budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹2.9L
   • ROAS 2.22x
   • Ad spend ₹1.3L

1. 1

   Tested cost caps and bid caps against open bidding

2. 2

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

3. 3

   Track ad-platform revenue beside store revenue (and net sales) daily or weekly, and set the numbers on the lower figure.