Home & kitchen case study: ₹8L to ₹14.3L monthly revenue in 6–12 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We focus the first month on keeping return on spend while scaling, the brand’s main problem, on a mid-sized base. 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 use cart-value tiers so larger baskets earn a better offer. We fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive.

   Why

   A mid-sized home and kitchen brand grows monthly revenue in 6–12 months, from ₹8L to ₹1Cr (12.5x). 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 keeping return on spend while scaling, with results that swing from month to month close behind. Without layers, retargeting quietly eats the prospecting budget. A larger basket spreads the cost of each order over more revenue.

   How it works

   Retargeting sits next to prospecting and never replaces it. Offers are tuned to the order values that already sell. The fix list goes to the brand's team in the opening weeks, ahead of any budget step.

2. 2

   Measurement & reviews · Month 1 to 12

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We agree a written number for every month on the way from ₹8L to ₹1Cr, and start each review with the month-to-date figure against it. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   The return on ad spend it reported sits above what measured 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 shortfall is caught in the month it happens, not at the end.

   How it works

   Every budget call starts from the store's orders. Each budget step is argued against the written number. 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 several dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We buy creative in batches and give each batch a fixed read window before buying more. We stack kitchen and home interests around a creator video of the lead product. The learning month runs on a small daily budget, stepping up only once orders confirm.

   Why

   The read window keeps creative spending tied to evidence. A demonstration video explains a utility product faster than a static image. Early spend buys learning, not scale.

   How it works

   Losing batches stop; winning ads take their budget. The interest stack runs beside a broad audience, read on the same video. Spend steps up only after orders confirm at the low budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 8

   What we do

   Through Month 2 to Month 8, we push toward ₹1Cr: the budget climbs in steps and return falls, and Instagram Reels carries the most 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 test cost caps, bid caps and CBO against ABO side by side before each budget step. We cut regional-language versions of the winning video for the best-selling states.

   Why

   In Month 2 to Month 8 the budget climbs in steps, and return on spend falls as it does. In three of these months budget goes up only as far as return allows, so revenue grows more slowly than the agreed number needs. Budget does not rise in one of these months: at that return, more spend does not pay. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Parallel controls reveal which setup keeps cost per purchase down as spend grows.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Controls run as parallel versions and the one that holds cost is kept. The regional versions run next to the original. 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 9 to 12

   What we do

   From Month 9, we protect the revenue already built and move toward ₹1Cr where return permits. We stop pushing budget once return has peaked. We keep a bank of ready creatives and rotate them in as ads tire.

   Why

   Growth slows from Month 9, still short of ₹1Cr. Spend holds roughly steady from here. Return on spend fell from its peak month in most of our engagements; peaks do not hold. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Budget is held while return is at its best and cut back when it slips. New creatives mix with proven ones rather than replacing them all at once.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. The cart-value tiers are live. The layered account is in place, with retargeting beside prospecting.

   • Revenue ₹7.9L
   • ROAS 4.08x
   • Ad spend ₹1.9L
2. Month 2

   Scaling begins: the budget decision is taken on the return the last step earned. Return on spend dips while budget steps up.

   • Revenue ₹8.6L
   • ROAS 3.72x
   • Ad spend ₹2.3L
3. Month 3

   Tired ads are refreshed from the creative bank.

   • Revenue ₹9.5L
   • ROAS 3.31x
   • Ad spend ₹2.9L
4. Month 4

   Regional-language versions of the winner go live.

   • Revenue ₹12.2L
   • ROAS 2.97x
   • Ad spend ₹4.1L
5. Month 5

   Bid-cap and cost-cap versions run beside open bidding. The budget step stops where return starts to slip: return on spend falls while budget steps up.

   • Revenue ₹13.8L
   • ROAS 2.52x
   • Ad spend ₹5.5L
6. Month 6

   Bid-cap and cost-cap versions run beside open bidding. Return has not reached the level that justifies more budget, so return on spend rises while budget holds.

   • Revenue ₹14.4L
   • ROAS 2.69x
   • Ad spend ₹5.4L
7. Month 7

   This batch's read is done: winners stay, the rest are cut. The budget step stops where return starts to slip: return on spend holds while budget holds.

   • Revenue ₹14.6L
   • ROAS 2.63x
   • Ad spend ₹5.5L
8. Month 8

   The budget decision is taken on the return the last step earned.

   • Revenue ₹14.6L
   • ROAS 2.60x
   • Ad spend ₹5.6L
9. Month 9

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

   • Revenue ₹14.6L
   • ROAS 2.59x
   • Ad spend ₹5.7L
10. Month 10

    Ads with falling click-through are swapped from the bank. Return has not reached the level that justifies more budget, so return on spend holds while budget holds.

    • Revenue ₹14.6L
    • ROAS 2.65x
    • Ad spend ₹5.5L
11. Month 11

    Budget is held at the level where return peaked. The budget step stops where return starts to slip: return on spend holds while budget holds.

    • Revenue ₹14.6L
    • ROAS 2.62x
    • Ad spend ₹5.6L
12. Month 12

    Regional-language versions of the winner go live. The budget step stops where return starts to slip: return on spend dips while budget holds. ₹1Cr is not reached in the time, because budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

    • Revenue ₹14.3L
    • ROAS 2.46x
    • Ad spend ₹5.8L

1. 1

   Test bidding controls (cost caps, bid caps, CBO against ABO) side by side before scaling.

2. 2

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

3. 3

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