Home & kitchen case study: ₹16L–₹20L to ₹35.4L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is reaching new buyers, so the opening month on this mid-sized store goes there. We add cart-value offers that step up at set basket sizes to lift order value. 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

   A mid-sized home and kitchen brand grows monthly revenue in 6 months, from ₹16L–₹20L to ₹50L (2.8x). That pace is within what the fastest tenth of our measured accounts reached over the same time. The main problem was reaching new buyers. A larger basket spreads the cost of each order over more revenue. No budget returns more than the store converts.

   How it works

   The tiers follow real order values, not round numbers. Site fixes are handed over in the first weeks, before budget rises.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We write month-by-month revenue numbers with the brand's team that climb from ₹16L–₹20L to ₹50L, and open every review with the month-to-date number against them. We keep a shared daily sheet with the ad platform's revenue next to real store orders. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured stores of that size hold. A missed month shows up early instead of at the end of the case study. Platform attribution over-counts, so budget decisions sit on the store-side number.

   How it works

   The month's number is on the page at every review. 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 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 separate the lead products into their own campaigns and review each one weekly. 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. 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. A product that does not sell is paused inside the week. 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 4

   What we do

   Through Month 2 to Month 4, we push toward ₹50L: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We make regional-language versions of the winning video for the states that buy most. We build lookalikes from past buyers once purchases are steady, beside broad prospecting.

   Why

   Across Month 2 to Month 4, the budget climbs in steps, and return on spend falls as it does. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. Buyers respond to the same idea in their own language.

   How it works

   The regional versions run next to the original. Lookalikes are read against broad on the same creative. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we protect the revenue already built and move toward ₹50L where return permits. We build lookalikes from repeat buyers and retarget past buyers with new arrivals. We refresh tired ads by mixing old and new creatives, and keep a creative bank.

   Why

   Growth slows from Month 5, still short of ₹50L. Spend holds roughly steady from here. An order from a returning buyer costs the least. A measured account showed click-through falling ahead of revenue, so tired ads are replaced early.

   How it works

   New arrivals go to past buyers first, before broad prospecting. New creatives mix with proven ones rather than replacing them all at once.

1. Month 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. Store orders and ad-platform revenue are checked side by side. The store fix list is live, with reviews on product pages and a prepaid offer.

   • Revenue ₹18.9L
   • ROAS 3.75x
   • Ad spend ₹5L
2. Month 2

   The scaling phase opens: a past-buyer lookalike audience joins broad prospecting. Return on spend holds while budget holds.

   • Revenue ₹20.3L
   • ROAS 3.70x
   • Ad spend ₹5.5L
3. Month 3

   Past buyers get the new arrivals ahead of prospecting. Return on spend falls while budget steps up sharply.

   • Revenue ₹26.8L
   • ROAS 2.96x
   • Ad spend ₹9.1L
4. Month 4

   The creative bank supplies this period's refresh. Return on spend falls while budget steps up. Monthly revenue passes the halfway point between ₹16L–₹20L and ₹50L.

   • Revenue ₹35L
   • ROAS 2.58x
   • Ad spend ₹13.5L
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back. Because a larger budget does not earn its keep at this return, return on spend rises while budget holds.

   • Revenue ₹36.5L
   • ROAS 2.74x
   • Ad spend ₹13.3L
6. Month 6

   The weekly product read pauses the products that are not selling. The step is sized by what return can bear: return on spend dips while budget holds. ₹50L 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 ₹35.4L
   • ROAS 2.53x
   • Ad spend ₹14L

1. 1

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

2. 2

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

3. 3

   Ran regional-language versions of the winning creative (6.9x)