Electronics case study: ₹7L–₹10L to ₹20.7L 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 results that swing from month to month, so the opening month on this mid-sized store goes there. We layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing. We ask for a product-level stock count so spend follows what can ship.

   Why

   A mid-sized electronics brand grows monthly revenue in 3 months, from ₹7L–₹10L to ₹30L (3.5x). 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 results that swing from month to month. Separate layers stop prospecting and retargeting competing for the same budget. Scaling ads on a product that sells out wastes the peak.

   How it works

   Retargeting sits next to prospecting and never replaces it. Spend is set against stock by product.

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 write month-by-month revenue numbers with the brand's team that climb from ₹7L–₹10L to ₹30L, and open every review with the month-to-date number against them.

   Why

   It did not report a return on ad spend, so the case study starts from what measured electronics stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   Every budget call starts from the store's orders. Each budget step is argued against the written number.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, static image and catalogue ads, building to a few dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We buy creative in batches and give each batch a fixed read window before buying more. We test several interest clusters against a broad audience, with video and catalogue ads. We separate the lead products into their own campaigns and review each one weekly.

   Why

   A fixed window stops spend chasing a creative before it has been read. Buyers split by use case, so clusters show which one buys before budget is committed. Products that do not sell show up inside a week, not after a month of shared budget.

   How it works

   Losing batches stop; winning ads take their budget. Clusters are read against broad before the scaling budget is set. A product that does not sell is paused inside the week. More spend buys more new ads, led by video ahead of static image.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 as far toward ₹30L as return allows as the budget climbs in steps and return falls, with Instagram Reels taking the largest share of spend and Facebook Reels the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. As spend rises, we mix new creatives in with the proven ones before the old ones tire.

   Why

   Across Month 2 to Month 3, the budget climbs in steps, 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. Frequency climbs with spend, and tired ads lose click-through first.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. New creatives join proven ones rather than replacing them all at once. Most of the final month's spend sits on Instagram Reels, then Facebook Reels. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. Store orders and ad-platform revenue are checked side by side. The account runs in layers: testing, scaling and retargeting.

   • Revenue ₹8.6L
   • ROAS 3.36x
   • Ad spend ₹2.5L
2. Month 2

   Scaling begins: the budget decision is taken on the return the last step earned. Spend steps up sharply, and return dips.

   • Revenue ₹13.8L
   • ROAS 3.22x
   • Ad spend ₹4.3L
3. Month 3

   Fresh ads are mixed in beside the proven set. Spend steps up sharply, and return falls. Revenue is now past halfway from ₹7L–₹10L to ₹30L. The case study finishes short of ₹30L: the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹20.7L
   • ROAS 2.77x
   • Ad spend ₹7.5L

1. 1

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

2. 2

   Ask for a product-level festive stock count so spend follows stock.

3. 3

   Run several interest clusters plus a broad audience with video and catalogue ads, then a bundle offer and a cost-control campaign to scale.