Home & kitchen case study: ₹35,000 to ₹46,656 monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at growth that has stalled, the brand’s main problem, on a smaller base. We build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. 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 6 months, from ₹35,000 to ₹35L (100x). 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 growth that has stalled, with scaling ad spend close behind. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. No budget returns more than the store converts.

   How it works

   Retargeting sits next to prospecting and never replaces it. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We agree a written number for every month on the way from ₹35,000 to ₹35L, and start each review with the month-to-date figure against it. We log store orders every day beside what the ad platform claims. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   With no return on ad spend reported, the case study begins at the level measured home and kitchen stores of that size hold. A shortfall is caught in the month it happens, not at the end. Platform attribution over-counts, so budget decisions sit on the store-side number.

   How it works

   Written monthly numbers make each scaling decision explicit. Every budget call starts from the store's orders. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to about a dozen new ads a month by the final month, with no format far behind the account's return. We give each lead product its own campaign and read it weekly. We stack kitchen and home interests around a creator video of the lead product. We run creator, customer-feedback and founder-led video.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. A demonstration video explains a utility product faster than a static image. Video built on trust was the format that held return in most measured accounts.

   How it works

   A product that does not sell is paused inside the week. The interest stack runs beside a broad audience, read on the same video. UGC that underperforms is replaced by founder-led video, not given more budget. 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 4

   What we do

   We scale through Month 2 to Month 4 toward ₹35L as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We recover abandoned checkouts with WhatsApp messages as traffic grows.

   Why

   In Month 2 to Month 4 the budget rises gently, while return on spend holds. In three of these months the step is trimmed to the size return can hold. Each budget step here is sized so that return stays close to where it was. 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. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. 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 ₹35L only as far as return allows. We refresh tired ads by mixing old and new creatives, and keep a creative bank. We use WhatsApp for abandoned checkouts and for past buyers' next order.

   Why

   From Month 5 growth slows while revenue is still under ₹35L. Spend holds roughly steady from here. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional. A message to someone who nearly bought costs less than an ad.

   How it works

   The bank means a tired ad is replaced the week it tires. Messages run beside retargeting ads.

1. Month 1

   Learning month: 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 layered account is in place, with retargeting beside prospecting.

   • Revenue ₹36,021
   • ROAS 1.94x
   • Ad spend ₹18,551
2. Month 2

   Scaling begins: products that do not sell are paused and budget moves to the winners. Budget rises to the point where return starts to give way: budget steps up and return on spend dips.

   • Revenue ₹39,685
   • ROAS 1.86x
   • Ad spend ₹21,290
3. Month 3

   Tired ads are refreshed from the creative bank. Budget rises to the point where return starts to give way: budget holds and return on spend holds.

   • Revenue ₹42,961
   • ROAS 1.89x
   • Ad spend ₹22,775
4. Month 4

   Past buyers get a WhatsApp nudge for their next order.

   • Revenue ₹44,140
   • ROAS 1.92x
   • Ad spend ₹22,983
5. Month 5

   Steady phase begins: creative refresh and repeat orders take on more of the work.

   • Revenue ₹45,032
   • ROAS 1.93x
   • Ad spend ₹23,283
6. Month 6

   Budget is reviewed against return before the next step. Budget rises to the point where return starts to give way: budget holds and return on spend holds. Revenue ends below ₹35L, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹46,656
   • ROAS 1.96x
   • Ad spend ₹23,862

1. 1

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

2. 2

   Gave each product its own campaign

3. 3

   Tracked store revenue and cost per order daily, beside Meta's own number