Electronics case study: plan for ₹40L–₹50L to ₹83.2L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   The booking named reaching new buyers first, so the opening month on this established store goes there. Ask for a product-level stock forecast so spend follows what can ship. Layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart retargeting.

   Why

   The enquiry came from an established electronics brand that wants to grow monthly revenue in 6 months, from ₹40L–₹50L to ₹1.5Cr (3.3x). That is faster than nine in ten of our measured accounts grew over the same time, so the plan below is modelled on what that fastest tenth reached and shows where it lands against the target. The problem named at booking was reaching new buyers. Scaling ads on a product that sells out wastes the peak. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one.

   How it works

   Spend is planned against stock by product. Each layer keeps its own budget line.

2. 2

   Measurement & targets · Month 1 to 6

   What we'd do

   Agree a written target for every month on the way from ₹40L–₹50L to ₹1.5Cr, and start each review with the month-to-date figure against it. Report net sales after returns, cancellations and tax next to store revenue and platform revenue.

   Why

   With no return on ad spend reported, the plan begins at the level measured stores of that size hold. A shortfall is caught in the month it happens, not at the end. In measured accounts net sales ran below logged store revenue, so targets sit on the lower number.

   How it works

   Written targets make each scaling decision explicit. Net sales, not platform revenue, decide each budget step.

3. 3

   Creative testing · Month 1

   What we'd do

   Test with video, static image and catalogue ads first, rising to several dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. Test several interest clusters against a broad audience, with video and catalogue ads. The learning month runs on a small daily budget, stepping up only once orders confirm. Separate the lead products into their own campaigns and review each one weekly.

   Why

   Buyers split by use case, so clusters show which one buys before budget is committed. Early spend buys learning, not scale. Products that do not sell show up inside a week, not after a month of shared budget.

   How it works

   Clusters are read against broad before the scaling plan is set. The low budget stays until orders confirm the buyer. Budget follows the products that sell. New ads rise with the budget, most of them video, then static image.

4. 4

   Scaling · Month 2 to 4

   What we'd do

   Through Month 2 to Month 4, push as far toward ₹1.5Cr as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. Build lookalikes from past buyers once purchases are steady, beside broad prospecting. Test cost caps, bid caps and CBO against ABO side by side before each budget step.

   Why

   In Month 2 to Month 4 the modelled budget climbs in steps, and return on spend falls as it does. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. The people who already bought describe the next buyer best.

   How it works

   Lookalikes are read against broad on the same creative. The version that keeps cost per purchase lowest stays. 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.

5. 5

   Steady state · Month 5 to 6

   What we'd do

   From Month 5, hold the gains and push toward ₹1.5Cr only as far as return allows. Show new arrivals to past buyers first, and build lookalikes from the ones who came back. Keep a bank of ready creatives and rotate them in as ads tire.

   Why

   From Month 5 growth slows while revenue is still under ₹1.5Cr. Budget keeps rising, more slowly than in the scaling months. An order from a returning buyer costs the least. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Past buyers see new arrivals before anyone else. The bank means a tired ad is replaced the week it tires.

1. Month 1

   Learning month: tracking is checked against store orders and the first ads go live on a small daily budget. Net sales are reconciled against logged revenue. The account runs in layers: testing, scaling and retargeting.

   • Projected revenue ₹44.8L
   • Projected ROAS 3.60x
   • Planned ad spend ₹12.4L
2. Month 2

   Scaling begins: budget shifts to the products that sold this period. Spend holds, and return holds.

   • Projected revenue ₹47.4L
   • Projected ROAS 3.50x
   • Planned ad spend ₹13.6L
3. Month 3

   New arrivals go to past buyers first. Spend steps up, and return holds.

   • Projected revenue ₹56.1L
   • Projected ROAS 3.57x
   • Planned ad spend ₹15.7L
4. Month 4

   Cost caps and bid caps are tested against open bidding.

   • Projected revenue ₹69.9L
   • Projected ROAS 2.99x
   • Planned ad spend ₹23.4L
5. Month 5

   The plan moves into its steady phase, leaning on refreshed ads and repeat buyers.

   • Projected revenue ₹82.6L
   • Projected ROAS 3.02x
   • Planned ad spend ₹27.3L
6. Month 6

   Ads with falling click-through are swapped from the bank. Spend holds, and return rises. The plan finishes short of ₹1.5Cr: the plan follows what the fastest tenth of our measured accounts reached. Each order costs more by the end than it did while learning.

   • Projected revenue ₹83.2L
   • Projected ROAS 3.20x
   • Planned ad spend ₹26L

1. 01

   Reconciled ad-platform and Shopify figures every month after cancellations

2. 02

   Scaling plan built on video creative, bundle offers and a cost-control campaign

3. 03

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