Electronics case study: ₹1L to ₹1.4L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with turning visits into orders, which the brand in this scenario named first, before anything else on this smaller store. We 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. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a smaller electronics brand grows monthly revenue in 3 months, from ₹1L to ₹10L–₹15L (12.5x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem was turning visits into orders, followed by marketing and social presence. Conversion rate caps what any ad budget can return. Higher order value lowers the share of each order that goes to ads.

   How it works

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

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We log store orders every day beside what the ad platform claims. We set the path from ₹1L to ₹10L–₹15L as written monthly numbers, and read every review against the month so far. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured electronics stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. Written monthly numbers expose a slow month while there is still time to act.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The month's number is on the page at every review. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

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

   Why

   Shared campaigns hide weak products; separate ones expose them fast. Video built on trust was the format that held return in most measured accounts. Buyers split by use case, so clusters show which one buys before budget is committed.

   How it works

   Budget follows the products that sell. Low-quality UGC is pulled and founder-led video takes its place. Clusters are read against broad before the scaling budget is set. New ads rise with the budget, most of them video, then static image.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 toward ₹10L–₹15L as the budget rises gently while return rises, with Instagram Reels taking the largest share of spend and Facebook Feed the next. We set spend against stock with a product-level stock count, and scale with bundle offers and a cost-control campaign. We let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the budget rises gently, 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 brand's number needs. Return rises through these months as it climbs from where the account started toward what measured stores of its size hold. Bundles lift order value and a cost-control campaign holds cost per purchase as spend rises.

   How it works

   Spend is set against stock by product. There is no fixed ramp; each month's budget follows the return of the last. 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

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Cart-value offers go live at checkout. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹1L
   • ROAS 1.87x
   • Ad spend ₹55,385
2. Month 2

   Scaling begins: new creator and customer-feedback videos join the account. Budget rises to the point where return starts to give way: budget steps up and return on spend holds.

   • Revenue ₹1.2L
   • ROAS 1.90x
   • Ad spend ₹64,818
3. Month 3

   The budget decision is taken on the return the last step earned. Budget rises to the point where return starts to give way: budget holds and return on spend holds. Revenue ends below ₹10L–₹15L, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹1.4L
   • ROAS 1.95x
   • Ad spend ₹70,363

1. 1

   Fix the store before scaling: trust pointers, reviews, return window, about page, prepaid incentive, bundle and cart-value offers.

2. 2

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

3. 3

   Six interest clusters, from tech and gaming to fashion, plus a broad audience, with video and catalogue ads