Home & kitchen case study: ₹30,000 to ₹72,730 monthly revenue in 3 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 smaller store goes there. We use cart-value tiers so larger baskets earn a better offer. We start with a website audit, a creative brief and a monthly media schedule in the first week.

   Why

   In this case study, a smaller home and kitchen brand grows monthly revenue in 3 months, from ₹30,000 to ₹2.5L (8.3x). That is faster than nine in ten of our measured accounts grew over the same time, so the case study below is on what that fastest tenth reached and shows where it lands against the number this scenario calls for. The main problem in this scenario is reaching new buyers, with marketing and social presence close behind. Higher order value lowers the share of each order that goes to ads. A written brief gives every later budget decision a reference point.

   How it works

   Offers are tuned to the order values that already sell. All three are shared with the brand's team before any budget step.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We set the path from ₹30,000 to ₹2.5L as written monthly numbers, and read every review against the month so far. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured home and kitchen stores of that size hold. The ad platform's own count runs high, so the store's count is the one that moves budget. A missed month shows up early instead of at the end of the case study.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. Each budget step is argued against the written number. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and carousel, building to about a 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 give each lead product its own campaign and read it weekly. We test static images beside video.

   Why

   A demonstration video explains a utility product faster than a static image. A product nobody buys is visible within a week when it has its own campaign. In every industry we measured, static images were the format most often in the top creative tier.

   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. Statics are added after a video wins. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 toward ₹2.5L as the budget climbs in steps and return dips, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We build lookalikes from past buyers once purchases are steady, beside broad prospecting. We cut regional-language versions of the winning video for the best-selling states.

   Why

   In Month 2 to Month 3 the budget climbs in steps, and return on spend dips. Two of these months stop their budget step where return slips too far. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. The people who already bought describe the next buyer best.

   How it works

   Lookalike and broad audiences run the same ads, so they can be compared. The regional versions run next to the original. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Basket-size offers switch on at checkout. The media schedule and creative brief are signed off.

   • Revenue ₹30,409
   • ROAS 2.07x
   • Ad spend ₹14,676
2. Month 2

   Scaling begins: the winner now also runs in regional languages. Budget goes up only as far as return can carry it: spend more than doubles, and return more than doubles.

   • Revenue ₹75,232
   • ROAS 1.92x
   • Ad spend ₹39,168
3. Month 3

   Budget shifts to the products that sold this period. Budget goes up only as far as return can carry it: spend holds, and return holds. Revenue ends below ₹2.5L, 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 ₹72,730
   • ROAS 1.88x
   • Ad spend ₹38,691

1. 1

   Gave each product its own campaign

2. 2

   Ran each product as its own campaign and read results weekly

3. 3

   Run Google Search and Shopping beside Meta, and use Google's demand data to shape Meta.