Home & kitchen case study: ₹50,000 to ₹69,361 monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with creative and content, which the brand in this scenario named first, before anything else on this smaller store. We add cart-value offers that step up at set basket sizes to lift order value. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 6 months, from ₹50,000 to ₹15L (30x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem in this scenario is creative and content. Each extra item in a basket is revenue the ad has already paid for. Conversion rate caps what any ad budget can return.

   How it works

   Tier levels are set just above the basket sizes buyers already reach. 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 track ad-platform revenue beside store orders in a shared daily sheet. We set the path from ₹50,000 to ₹15L as written monthly numbers, and read every review against the month so far. We match the return the brand in this scenario reports to what the store actually took in, before touching budget.

   Why

   At the return on ad spend it reported, extra budget costs more than it brings, so return has to climb first. The ad platform's own count runs high, so the store's count is the one that moves budget. A shortfall is caught in the month it happens, not at the end.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The month's number is on the page at every review. The store-side return becomes the number every review uses.

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, keeping carousel on a short leash because its return trails the account. We buy creative in batches and give each batch a fixed read window before buying more. We lead with video that carries trust: creators, customer feedback and founder-led pieces. We keep creators producing UGC video on a steady schedule, and cut the winners into regional languages.

   Why

   The read window keeps creative spending tied to evidence. Trust-carrying video held return in most measured accounts. Regular UGC keeps testing going, and a regional cut stretches a winning idea further.

   How it works

   Losing batches stop; winning ads take their budget. Weak UGC is swapped for founder-led video rather than scaled. Regional cuts of a winner come before new ideas. 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 toward ₹15L as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We make regional-language versions of the winning video for the states that buy most. We tie every budget increase to return: we step up while it holds, and step back when it drops.

   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. The same winning idea reaches more buyers in their own language.

   How it works

   Regional cuts run beside the original winner. Each month's budget is set by the return the last one earned. 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 ₹15L 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

   From Month 5 growth slows while revenue is still under ₹15L. 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. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   New creatives mix with proven ones rather than replacing them all at once. WhatsApp runs alongside paid retargeting, not instead of it.

1. Month 1

   Learning month: the first ads run on a small daily budget while tracking is checked against store orders. The daily sheet now matches platform revenue to store orders. The reported return is checked against the store-side return.

   • Revenue ₹53,530
   • ROAS 1.91x
   • Ad spend ₹28,066
2. Month 2

   Scaling begins: budget is reviewed against return before the next step. Only the part of the step that return supports is taken: return on spend rises while budget holds.

   • Revenue ₹59,053
   • ROAS 2.00x
   • Ad spend ₹29,481
3. Month 3

   Past buyers get a WhatsApp nudge for their next order.

   • Revenue ₹63,467
   • ROAS 2.01x
   • Ad spend ₹31,644
4. Month 4

   Tired ads are refreshed from the creative bank.

   • Revenue ₹63,600
   • ROAS 1.87x
   • Ad spend ₹34,059
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back.

   • Revenue ₹68,414
   • ROAS 1.92x
   • Ad spend ₹35,617
6. Month 6

   The winner now also runs in regional languages. Only the part of the step that return supports is taken: return on spend holds while budget holds. Revenue ends below ₹15L, 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 ₹69,361
   • ROAS 1.90x
   • Ad spend ₹36,450

1. 1

   Ran each product as its own campaign and read results weekly

2. 2

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

3. 3

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