Electronics case study: plan for ₹10L to ₹43.9L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at return on ad spend, the problem named at booking, on a mid-sized base. Use cart-value tiers so larger baskets earn a better offer. Get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront.

   Why

   A mid-sized electronics store asked us how to grow monthly revenue in 3 months, from ₹10L to ₹1Cr (10x). 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. At booking, the brand named return on ad spend as its main problem. Each extra item in a basket is revenue the ad has already paid for. No budget returns more than the store converts.

   How it works

   Offers are tuned to the order values that already sell. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & targets · Month 1 to 3

   What we'd do

   Write month-by-month targets with the brand's team that climb from ₹10L to ₹1Cr, and open every review with the month-to-date number against them. Track ad-platform revenue beside store orders in a shared daily sheet. Check the reported return on spend against store revenue before any budget moves.

   Why

   Its reported return on ad spend is too low for more budget to pay, so the first months lift return before any budget step. A missed month shows up early instead of at the end of the plan. The ad platform's own count runs high, so the store's count is the one that moves budget.

   How it works

   Each budget step is argued against the written target. Every budget call starts from the store's orders. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Month 1

   What we'd do

   Lead the testing layer with video, static image and catalogue ads, building to several dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. Give each lead product its own campaign and read it weekly. Test several interest clusters against a broad audience, with video and catalogue ads. Lead with video that carries trust: creators, customer feedback and founder-led pieces.

   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. Trust-carrying video held return in most measured accounts.

   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. Weak UGC is swapped for founder-led video rather than scaled. The number of new ads grows with spend; video makes up the largest part and static image the next.

4. 4

   Scaling · Month 2 to 3

   What we'd do

   Scale through Month 2 to Month 3 toward ₹1Cr as the budget climbs steeply while return rises, with Instagram Reels taking the largest share of spend and Facebook Feed the next. Raise budget only while return on spend holds, and cut it when return drops. Test cost caps, bid caps and CBO against ABO side by side before each budget step.

   Why

   In Month 2 to Month 3 the modelled budget climbs steeply, while return on spend rises. In two of these months budget goes up only as far as return allows, so revenue grows more slowly than the target needs. Return rises through these months as it climbs from where the account started toward what measured stores of its size hold. Controls show which setup holds cost per purchase as spend rises.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Controls run as parallel versions and the one that holds cost is kept. In the final month, Instagram Reels takes the most spend and Facebook Feed the next most. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   Learning month: the first ads run on a small daily budget while tracking is checked against store orders. Store fixes go live: reviews, trust pointers and the prepaid offer. Store orders and ad-platform revenue are checked side by side.

   • Projected revenue ₹10.2L
   • Projected ROAS 1.86x
   • Planned ad spend ₹5.5L
2. Month 2

   Scaling starts: cost caps and bid caps are tested against open bidding. Budget is raised only as far as return allows: return on spend rises while budget more than doubles.

   • Projected revenue ₹23.6L
   • Projected ROAS 1.95x
   • Planned ad spend ₹12.1L
3. Month 3

   The budget decision is taken on the return the last step earned. Budget is raised only as far as return allows: return on spend holds while budget steps up sharply. ₹1Cr is not reached in the time, because budget stops rising where return would slip. Each order costs about what it did while learning.

   • Projected revenue ₹43.9L
   • Projected ROAS 1.95x
   • Planned ad spend ₹22.5L

1. 01

   Reconciled ad-platform and Shopify figures every month after cancellations

2. 02

   Website fixes: shop-by-phone-brand menu, reviews and trust pointers on product pages

3. 03

   Kept a creatives bank and 4–8 social posts a month