Electronics case study: plan for ₹7L to ₹55.3L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at scaling ad spend, the problem named at booking, on a mid-sized base. Ask for a product-level stock forecast so spend follows what can ship. Split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts.

   Why

   A mid-sized electronics brand came to us to grow monthly revenue in 6 months, from ₹7L to ₹1Cr (14.3x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the plan is built on what the fastest tenth reached, and shows the gap to the target honestly. The problem named at booking was scaling ad spend. 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

   Log store orders every day beside what the ad platform claims. Set the path from ₹7L to ₹1Cr as written monthly targets, and read every review against the month so far. Reconcile the return on spend the brand reports with store revenue before the first budget change.

   Why

   The return on ad spend it reported sits above what measured electronics stores of that size hold; the plan starts from it and expects some of it to give way as spend rises. The ad platform's own count runs high, so the store's count is the one that moves budget. Written targets expose a slow month while there is still time to act.

   How it works

   Every budget call starts from the store's orders. Written targets make each scaling decision explicit. Both returns are reviewed together each week.

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. Give each lead product its own campaign and read it weekly. Test several interest clusters against a broad audience, with video and catalogue ads. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Buyers split by use case, so clusters show which one buys before budget is committed. Spend in the learning phase pays for information.

   How it works

   Losing products are paused within a week and budget moves to the winners. Clusters are read against broad before the scaling plan is set. The low budget stays until orders confirm the buyer. 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 toward ₹1Cr: the budget climbs steeply and return falls, and Instagram Reels carries the most spend and Facebook Feed the next. Test cost caps, bid caps and CBO against ABO side by side before each budget step. Raise budget only while return on spend holds, and cut it when return drops.

   Why

   Across Month 2 to Month 4, the modelled budget climbs steeply, and return on spend falls as it does. 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

   Controls run as parallel versions and the one that holds cost is kept. There is no fixed ramp; each month's budget follows the return of the last. Instagram Reels carries the largest share of spend in the final month, with Facebook Feed next. 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 ₹1Cr only as far as return allows. Use WhatsApp for abandoned checkouts and for past buyers' next order. Keep a bank of ready creatives and rotate them in as ads tire.

   Why

   From Month 5 growth slows while revenue is still under ₹1Cr. Budget keeps rising, more slowly than in the scaling months. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout. A measured account showed click-through falling ahead of revenue, so tired ads are replaced early.

   How it works

   WhatsApp runs alongside paid retargeting, not instead of it. Refreshes are gradual: a few new ads at a time.

1. Month 1

   First, the set-up: tracking is checked against store orders and the first ads go live on a small daily budget. Testing, scaling and retargeting now run as separate layers. Reported and store-side returns are reconciled.

   • Projected revenue ₹7.2L
   • Projected ROAS 3.53x
   • Planned ad spend ₹2L
2. Month 2

   Scaling begins: tired ads are refreshed from the creative bank. Spend steps up sharply, and return falls.

   • Projected revenue ₹10.1L
   • Projected ROAS 3.03x
   • Planned ad spend ₹3.3L
3. Month 3

   Products that do not sell are paused and budget moves to the winners. Spend more than doubles, and return dips.

   • Projected revenue ₹23.6L
   • Projected ROAS 2.82x
   • Planned ad spend ₹8.4L
4. Month 4

   Bid-cap and cost-cap versions run beside open bidding. Revenue is now past halfway from ₹7L to ₹1Cr.

   • Projected revenue ₹54.4L
   • Projected ROAS 2.37x
   • Planned ad spend ₹23L
5. Month 5

   The plan moves into its steady phase, leaning on refreshed ads and repeat buyers. The budget step stops where return would slip: spend holds, and return dips.

   • Projected revenue ₹52.5L
   • Projected ROAS 2.14x
   • Planned ad spend ₹24.6L
6. Month 6

   Budget is reviewed against return before the next step. More budget would not pay at this return, so spend holds, and return rises. Revenue ends below ₹1Cr, the target set at enquiry, because budget stops rising where return would slip. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹55.3L
   • Projected ROAS 2.29x
   • Planned ad spend ₹24.1L

1. 01

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

2. 02

   Reconciled ad-platform and Shopify figures every month after cancellations

3. 03

   Net sales ran below logged store revenue after returns, cancellations and tax.