Home & kitchen case study: ₹15L–₹20L to ₹37.7L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We set up the base for a mid-sized home and kitchen store before any budget rises. We put the website audit, the creative brief and the monthly media schedule in place before spend moves. We set basket-size offers that reward a second and third item in the cart.

   Why

   A mid-sized home and kitchen brand grows monthly revenue in 6 months, from ₹15L–₹20L to ₹1Cr (5.7x). 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. No single problem was named; the case study works from the figures instead. 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. 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 set the path from ₹15L–₹20L to ₹1Cr 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

   No starting return on ad spend was given; the starting return is what measured stores of that size hold. 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

   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 lead the testing layer with video, catalogue ads and static image, building to several dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We stack kitchen and home interests around a creator video of the lead product. We work in creative batches with a fixed read window each. We separate the lead products into their own campaigns and review each one weekly.

   Why

   A demonstration video explains a utility product faster than a static image. The read window keeps creative spending tied to evidence. Shared campaigns hide weak products; separate ones expose them fast.

   How it works

   The interest stack runs beside a broad audience, read on the same video. Losing batches stop; winning ads take their budget. Losing products are paused within a week and budget moves to the winners. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 as far toward ₹1Cr as return allows as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We test cost caps, bid caps and CBO against ABO side by side before each budget step. We raise budget only while return on spend holds, and cut it when return drops.

   Why

   In Month 2 to Month 4 the budget climbs in steps, and return on spend falls as it does. In one of these months the step is trimmed to the size return can hold. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. Each month's budget is set by the return the last one earned. Most of the final month's spend sits on Instagram Reels, then Instagram Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we hold the gains and push toward ₹1Cr only as far as return allows. We refresh tired ads by mixing old and new creatives, and keep a creative bank. We show new arrivals to past buyers first, and build lookalikes from the ones who came back.

   Why

   From Month 5 growth slows while revenue is still under ₹1Cr. Spend still grows, at a slower pace than during scaling. In one account we measured, click-through fell before revenue did; refresh is how that is avoided. 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: tracking is checked against store orders and the first ads go live on a small daily budget. The daily sheet now matches platform revenue to store orders. The cart-value tiers are live.

   • Revenue ₹17.3L
   • ROAS 5.12x
   • Ad spend ₹3.4L
2. Month 2

   The scaling phase opens: budget is reviewed against return before the next step. With budget steps up, return on spend dips.

   • Revenue ₹19.6L
   • ROAS 4.75x
   • Ad spend ₹4.1L
3. Month 3

   The weekly product read pauses the products that are not selling. With budget steps up sharply, return on spend falls.

   • Revenue ₹26.6L
   • ROAS 4.01x
   • Ad spend ₹6.6L
4. Month 4

   Bid-cap and cost-cap versions run beside open bidding. The step is sized by what return can bear: with budget steps up sharply, return on spend dips.

   • Revenue ₹37.8L
   • ROAS 3.74x
   • Ad spend ₹10.1L
5. Month 5

   Steady phase begins: creative refresh and repeat orders take on more of the work. The step is sized by what return can bear: as budget holds, return on spend holds.

   • Revenue ₹37.6L
   • ROAS 3.62x
   • Ad spend ₹10.4L
6. Month 6

   New arrivals go to past buyers first. The step is sized by what return can bear: as budget holds, return on spend holds. ₹1Cr 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 ₹37.7L
   • ROAS 3.49x
   • Ad spend ₹10.8L

1. 1

   Tested cost caps and bid caps against open bidding

2. 2

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

3. 3

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