Home & kitchen case study: ₹5L to ₹12.7L monthly revenue in 7 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is a clear strategy, so the opening month on this mid-sized store goes there. We put the website audit, the creative brief and the monthly media schedule in place before spend moves. We split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a mid-sized home and kitchen brand grows monthly revenue in 7 months, from ₹5L to ₹1.5Cr (30x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The main problem in this scenario is a clear strategy. Without a brief, each creative and budget decision starts from scratch. Without layers, retargeting quietly eats the prospecting budget.

   How it works

   The brief is agreed first; spend follows it. Warm layers run beside prospecting, not instead of it. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Month 1 to 7

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We raise budget in a month only while return holds; where it slips too far, we hold it. We agree a written number for every month on the way from ₹5L to ₹1.5Cr, and start each review with the month-to-date figure against it.

   Why

   With no return on ad spend reported, the case study begins at the level measured stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. In the store accounts we measured, one budget step-up in four lost about a quarter of its return or more, so a step is taken only while return holds.

   How it works

   Every budget call starts from the store's orders. A month whose return slips past that point keeps its budget instead. The month's number is on the page at every review.

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 separate the lead products into their own campaigns and review each one weekly. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   A demonstration video explains a utility product faster than a static image. Products that do not sell show up inside a week, not after a month of shared budget. Spend in the learning phase pays for information.

   How it works

   The interest stack runs beside a broad audience, read on the same video. Losing products are paused within a week and budget moves to the winners. Spend steps up only after orders confirm at the low budget. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 5

   What we do

   We scale through Month 2 to Month 5 toward ₹1.5Cr as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. 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 5, the budget climbs in steps, and return on spend falls as it does. Two of these months stop their budget step where return slips too far. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. 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. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

5. 5

   Steady state · Month 6 to 7

   What we do

   From Month 6, we hold the gains and push toward ₹1.5Cr only as far as return allows. We keep a bank of ready creatives and rotate them in as ads tire. We recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   Growth slows from Month 6, still short of ₹1.5Cr. Budget stays about level over these months. A measured account showed click-through falling ahead of revenue, so tired ads are replaced early. A message to someone who nearly bought costs less than an ad.

   How it works

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

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Testing, scaling and retargeting now run as separate layers. The daily sheet now matches platform revenue to store orders.

   • Revenue ₹5.1L
   • ROAS 2.77x
   • Ad spend ₹1.8L
2. Month 2

   The scaling phase opens: cost caps and bid caps are tested against open bidding. Return dips as the budget steps up.

   • Revenue ₹6.2L
   • ROAS 2.52x
   • Ad spend ₹2.5L
3. Month 3

   The creative bank supplies this period's refresh. Return falls as the budget steps up sharply.

   • Revenue ₹10.3L
   • ROAS 2.13x
   • Ad spend ₹4.8L
4. Month 4

   The budget decision is taken on the return the last step earned. Budget goes up only as far as return can carry it: return dips as the budget dips.

   • Revenue ₹11.9L
   • ROAS 1.96x
   • Ad spend ₹6L
5. Month 5

   Repeat-order messages go to past buyers. Budget goes up only as far as return can carry it: return rises as the budget holds.

   • Revenue ₹12.6L
   • ROAS 2.06x
   • Ad spend ₹6.1L
6. Month 6

   The case study moves into its steady phase, leaning on refreshed ads and repeat buyers.

   • Revenue ₹12.7L
   • ROAS 1.97x
   • Ad spend ₹6.5L
7. Month 7

   Products that do not sell are paused and budget moves to the winners. The budget waits on return rather than on the calendar, so return holds as the budget holds. Revenue ends below ₹1.5Cr, the number this scenario calls for, because budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹12.7L
   • ROAS 2.02x
   • Ad spend ₹6.3L

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

   Find the buyer on a small daily budget first, then step spend up in stages.