Home & kitchen case study: ₹30L to ₹1.24Cr monthly revenue in 1–2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   We set up the base for an established home and kitchen store before any budget rises. We open with three documents: a website audit, a creative brief and a media schedule by month. 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

   An established home and kitchen store grows monthly revenue in 1–2 months, from ₹30L to ₹3Cr (10x). 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 brand in this scenario named no single problem, so the case study starts from the numbers. A written brief gives every later budget decision a reference point. Conversion rate caps what any ad budget can return.

   How it works

   All three are shared with the brand's team before any budget step. Site fixes are handed over in the first weeks, before budget rises.

2. 2

   Measurement & reviews · Week 1 to 9

   What we do

   We track ad-platform revenue beside store revenue and net sales after returns, cancellations and tax. We write week-by-week revenue numbers with the brand's team that climb from ₹30L to ₹3Cr, and open every review with the week-to-date number against them. We set a written rule: no budget step in a week where return falls too far to pay for it.

   Why

   With no return on ad spend reported, the case study begins at the level measured stores of that size hold. In measured accounts net sales ran below logged store revenue, so the numbers sit on the lower figure. A missed week shows up early instead of at the end of the case study.

   How it works

   Budget decisions are read off net sales, not the ad platform alone. The week's number is on the page at every review. A week whose return starts to slip past that point keeps its budget instead.

3. 3

   Creative testing · Week 1 to 4

   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 last four weeks, 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 test static images beside video. The four learning weeks run with a deliberately small daily budget until orders prove the buyer.

   Why

   A demonstration video explains a utility product faster than a static image. In every industry we measured, static images were the format most often in the top creative tier. The first rupees are for finding the buyer, not for volume.

   How it works

   The interest stack runs beside a broad audience, read on the same video. Statics join once a winning video is found. The low budget stays until orders confirm the buyer. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Week 5 to 9

   What we do

   Through Week 5 to Week 9, we push toward ₹3Cr: the budget climbs steeply and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. We test cost caps, bid caps and CBO against ABO side by side before each budget step. We raise budget only while return on spend holds, and cut it when return drops.

   Why

   In Week 5 to Week 9 the budget climbs steeply, and return on spend falls as it does. Two of these weeks stop their budget step where return starts to slip too far. Spend stays flat for one of these weeks, waiting on return rather than on the calendar. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Controls show which setup holds cost per purchase as spend rises.

   How it works

   The version that keeps cost per purchase lowest stays. Budget follows return week to week instead of a fixed ramp. Most of the last four weeks's spend sits on Instagram Reels, then Instagram Feed. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Week 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Net sales after returns are matched to logged revenue. The store fix list is live, with reviews on product pages and a prepaid offer.

   • Revenue ₹6.4L
   • ROAS 5.17x
   • Ad spend ₹1.2L
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Return on spend holds while budget holds.

   • Revenue ₹6.6L
   • ROAS 5.08x
   • Ad spend ₹1.3L
3. Week 3

   Offers and store fixes switch on as the first orders confirm.

   • Revenue ₹6.6L
   • ROAS 4.93x
   • Ad spend ₹1.3L
4. Week 4

   The learning phase ends with a buyer identified and a winning ad chosen.

   • Revenue ₹6.7L
   • ROAS 4.97x
   • Ad spend ₹1.3L
5. Week 5

   Scaling begins: cost caps and bid caps are tested against open bidding. Return on spend holds while budget steps up.

   • Revenue ₹8.5L
   • ROAS 4.88x
   • Ad spend ₹1.7L
6. Week 6

   The budget decision is taken on the return the last step earned. Return on spend falls while budget more than doubles.

   • Revenue ₹17.2L
   • ROAS 4.29x
   • Ad spend ₹4L
7. Week 7

   Static images are tested beside the winning video. Budget is raised only as far as return allows: return on spend dips while budget steps up sharply.

   • Revenue ₹32.9L
   • ROAS 3.83x
   • Ad spend ₹8.6L
8. Week 8

   Cost caps and bid caps are tested against open bidding. Budget is raised only as far as return allows: return on spend dips while budget steps up.

   • Revenue ₹36.1L
   • ROAS 3.54x
   • Ad spend ₹10.2L
9. Week 9

   Each budget move is read against the return it bought. Return is still below the level where more budget pays, so return on spend rises while budget holds. Revenue ends below ₹3Cr, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹38L
   • ROAS 3.74x
   • Ad spend ₹10.2L

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

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