Home & kitchen case study: plan for ₹10L to ₹20L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Start with keeping return on spend while scaling, which the booking named first, before anything else on this mid-sized store. Layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart retargeting. Set basket-size offers that reward a second and third item in the cart.

   Why

   The enquiry came from a mid-sized home and kitchen brand that wants to grow monthly revenue in 3 months, from ₹10L to ₹20L (2x). That pace is common enough: one in four of our measured accounts matched it in the same time, or did better. The main problem named at booking was keeping return on spend while scaling, followed by scaling ad spend. Separate layers stop prospecting and retargeting competing for the same budget. A larger basket spreads the cost of each order over more revenue.

   How it works

   Warm layers run beside prospecting, not instead of it. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & targets · Month 1 to 3

   What we'd do

   Log store orders every day beside what the ad platform claims. Write month-by-month targets with the brand's team that climb from ₹10L to ₹20L, and open every review with the month-to-date number against them. Match the return the brand reports to what the store actually took in, before touching budget.

   Why

   The return on ad spend it reported sits below what measured stores of that size hold, so the plan 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. A missed month shows up early instead of at the end of the plan.

   How it works

   The sheet is read before each budget change. Each budget step is argued against the written target. Both returns are reviewed together each week.

3. 3

   Creative testing · Month 1

   What we'd do

   Lead the testing layer with video, catalogue ads and static image, building to a few dozen new ads a month by the final month, when monthly revenue runs at about ₹20L, keeping carousel on a short leash because its return trails the account. Run every lead product in a campaign of its own, read every week. The learning month runs at a low daily budget and moves up only when orders come through. Stack kitchen and home interests around a creator video of the lead product.

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Spend in the learning phase pays for information. A demonstration video explains a utility product faster than a static image.

   How it works

   Losing products are paused within a week and budget moves to the winners. The low budget stays until orders confirm the buyer. The interest stack runs beside a broad audience, read on the same video. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Month 2 to 3

   What we'd do

   Scale through Month 2 to Month 3 toward ₹20L as the budget climbs in steps and return dips, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Run cost caps, bid caps, CBO and ABO as parallel versions before budget steps up. Let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the modelled budget climbs in steps, and return on spend dips. 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

   The version that keeps cost per purchase lowest stays. Each month's budget is set by the return the last one earned. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Reported and store-side returns are reconciled. Store orders and ad-platform revenue are checked side by side.

   • Projected revenue ₹10.5L
   • Projected ROAS 2.74x
   • Planned ad spend ₹3.8L
2. Month 2

   The scaling phase opens: budget shifts to the products that sold this period. With budget steps up, return on spend holds. Halfway from ₹10L to ₹20L is passed.

   • Projected revenue ₹15.2L
   • Projected ROAS 2.72x
   • Planned ad spend ₹5.6L
3. Month 3

   Budget is reviewed against return before the next step. With budget steps up sharply, return on spend dips. Revenue gets to ₹20L, the level the brand asked for. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹21.1L
   • Projected ROAS 2.46x
   • Planned ad spend ₹8.6L

1. 01

   Expect Facebook Reels to carry the largest share of spend in this industry's measured accounts.

2. 02

   Give each product (or colour) its own campaign, read weekly, and move budget to the winner.

3. 03

   Find the buyer on a small daily budget first, then step spend up in stages.