Other case study: ₹5L–₹10L to ₹77.7L monthly revenue in 3–6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is return on ad spend, so the opening month on this mid-sized store goes there. 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 layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing.

   Why

   A mid-sized brand grows monthly revenue in 3–6 months, from ₹5L–₹10L to ₹1Cr (13.3x). 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 in this scenario is return on ad spend, with turning visits into orders close behind. Conversion rate caps what any ad budget can return. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one.

   How it works

   The fix list goes to the brand's team in the opening weeks, ahead of any budget step. Warm layers run beside prospecting, not instead of it.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We log store orders every day beside what the ad platform claims. We agree a written number for every month on the way from ₹5L–₹10L to ₹1Cr, and start each review with the month-to-date figure against it. We check the reported return on spend against store revenue before any budget moves.

   Why

   Its reported return on ad spend is higher than measured stores of that size hold, so the case study starts there and lets part of it ease as budget grows. The ad platform's own count runs high, so the store's count is the one that moves 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. Both returns are reviewed together each week.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and static image first, rising to several dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We lead with video that carries trust: creators, customer feedback and founder-led pieces. The learning month runs on a small daily budget, stepping up only once orders confirm. We buy creative in batches and give each batch a fixed read window before buying more.

   Why

   Trust-carrying video held return in most measured accounts. Early spend buys learning, not scale. The read window keeps creative spending tied to evidence.

   How it works

   Low-quality UGC is pulled and founder-led video takes its place. Spend steps up only after orders confirm at the low budget. Losing batches stop; winning ads take their budget. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 toward ₹1Cr as the budget climbs steeply and return falls, with Instagram Reels taking the largest share of spend and Instagram Feed the next. We let return decide budget: more while it holds, less when it slips. Once purchases are steady, we add lookalikes of past buyers next to broad prospecting.

   Why

   In Month 2 to Month 4 the 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. The people who already bought describe the next buyer best.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Lookalikes are read against broad on the same creative. 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 do

   From Month 5, we hold the gains and push toward ₹1Cr only as far as return allows. We hold budget where return peaks instead of pushing past it. We use WhatsApp for abandoned checkouts and for past buyers' next order.

   Why

   From Month 5 growth slows while revenue is still under ₹1Cr. Spend still grows, at a slower pace than during scaling. Return on spend fell from its peak month in most of our engagements; peaks do not hold. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   Budget stays at the level of the best return and is trimmed when it slips. Messages run beside retargeting ads.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. The store fix list is live, with reviews on product pages and a prepaid offer. Reported and store-side returns are reconciled.

   • Revenue ₹7.2L
   • ROAS 5.01x
   • Ad spend ₹1.4L
2. Month 2

   Scaling begins: a past-buyer lookalike audience joins broad prospecting. Budget steps up and return on spend dips.

   • Revenue ₹9.6L
   • ROAS 4.67x
   • Ad spend ₹2L
3. Month 3

   The budget decision is taken on the return the last step earned. Budget steps up sharply and return on spend dips.

   • Revenue ₹16.5L
   • ROAS 4.32x
   • Ad spend ₹3.8L
4. Month 4

   Past buyers get a WhatsApp nudge for their next order. Budget more than doubles and return on spend dips.

   • Revenue ₹36.7L
   • ROAS 3.85x
   • Ad spend ₹9.5L
5. Month 5

   Steady phase begins: creative refresh and repeat orders take on more of the work. Budget steps up sharply and return on spend dips. Monthly revenue passes the halfway point between ₹5L–₹10L and ₹1Cr.

   • Revenue ₹71.7L
   • ROAS 3.51x
   • Ad spend ₹20.4L
6. Month 6

   Budget is held at the level where return peaked. Budget goes up only as far as return can carry it: budget holds and return on spend holds. ₹1Cr is not reached in the time, because budget stops rising where return starts to slip. Each order costs more by the end than it did while learning.

   • Revenue ₹77.7L
   • ROAS 3.49x
   • Ad spend ₹22.3L

1. 1

   Return on spend fell from its peak month in most engagements; peaks do not hold.

2. 2

   After the spring fall: hero categories, a pixel fix and festive influencer content

3. 3

   Moved budget in three-to-four-day windows at campaign level