Home & kitchen case study: ₹15L to ₹30L monthly revenue in 3–6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is a clear strategy, so the opening month on this mid-sized store goes there. We open with three documents: a website audit, a creative brief and a media schedule by month. We build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting.

   Why

   A mid-sized home and kitchen brand grows monthly revenue in 3–6 months, from ₹15L to ₹30L (2x). Only the faster-growing accounts we measured reached that pace in the same time, but they did reach it. The main problem was a clear strategy. A written brief gives every later budget decision a reference point. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one.

   How it works

   All three are shared with the brand's team before any budget step. Each layer keeps its own budget line.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We write month-by-month revenue numbers with the brand's team that climb from ₹15L to ₹30L, and open every review with the month-to-date number against them. We check the reported return on spend against store revenue before any budget moves.

   Why

   The return on ad spend it reported sits below what measured stores of that size hold, so the case study starts there and rebuilds return before budget rises. The ad platform's own count runs high, so the store's count is the one that moves budget. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   The sheet is read before each budget change. Written monthly numbers make each scaling decision explicit. Both returns are reviewed together each week.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and static image first, rising to a few dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We run every lead product in a campaign of its own, read every week. 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

   Shared campaigns hide weak products; separate ones expose them fast. A demonstration video explains a utility product faster than a static image. The first rupees are for finding the buyer, not for volume.

   How it works

   Losing products are paused within a week and budget moves to the winners. The interest stack runs beside a broad audience, read on the same video. 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 · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 toward ₹30L as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. We raise budget only while return on spend holds, and cut it when return drops.

   Why

   In Month 2 to Month 4 the budget rises gently, while return on spend holds. Each budget step here is sized so that return stays close to where it was. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. 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 close the last stretch to ₹30L without losing return. We refresh tired ads by mixing old and new creatives, and keep a creative bank. We recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   At Month 5 revenue is close to ₹30L. Budget keeps rising, more slowly than in the scaling months. 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

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Store orders and platform revenue are reconciled in the shared sheet. The account runs in layers: testing, scaling and retargeting.

   • Revenue ₹14.8L
   • ROAS 3.53x
   • Ad spend ₹4.2L
2. Month 2

   The scaling phase opens: repeat-order messages go to past buyers. Return on spend rises while budget holds.

   • Revenue ₹16.3L
   • ROAS 3.84x
   • Ad spend ₹4.2L
3. Month 3

   The creative bank supplies this period's refresh. Return on spend holds while budget steps up.

   • Revenue ₹21.2L
   • ROAS 3.78x
   • Ad spend ₹5.6L
4. Month 4

   Cost caps and bid caps are tested against open bidding. Monthly revenue passes the halfway point between ₹15L and ₹30L.

   • Revenue ₹25.1L
   • ROAS 3.54x
   • Ad spend ₹7.1L
5. Month 5

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

   • Revenue ₹28.7L
   • ROAS 3.68x
   • Ad spend ₹7.8L
6. Month 6

   Each budget move is read against the return it bought. Return on spend holds while budget holds. Revenue gets to ₹30L, the level this scenario calls for. Each order costs about what it did while learning.

   • Revenue ₹30.6L
   • ROAS 3.60x
   • Ad spend ₹8.5L

1. 1

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

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

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