Pets case study: ₹50,000 to ₹3.2L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We give the first month to creative and content, the brand’s main problem, on a smaller base. We add cart-value offers that step up at set basket sizes to lift order value. We build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting.

   Why

   In this case study, a smaller pet brand grows monthly revenue in 6 months, from ₹50,000 to ₹5L (10x). 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 was creative and content, followed by marketing and social presence. A larger basket spreads the cost of each order over more revenue. Without layers, retargeting quietly eats the prospecting budget.

   How it works

   Tier levels are set just above the basket sizes buyers already reach. Warm layers run beside prospecting, not instead of it.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We agree a written number for every month on the way from ₹50,000 to ₹5L, 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

   The return on ad spend it reported sits above what measured stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. 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. Written monthly numbers make each scaling decision explicit. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to a few dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We work in creative batches with a fixed read window each. We make trust the subject of the video: creator reels, customer feedback and founder-led clips. We keep creators producing UGC video on a steady schedule, and cut the winners into regional languages.

   Why

   The read window keeps creative spending tied to evidence. Video built on trust was the format that held return in most measured accounts. A steady supply keeps the testing layer fed, and regional cuts reach more buyers with the same idea.

   How it works

   Batches that do not convert are cut; winners get the budget. UGC that underperforms is replaced by founder-led video, not given more budget. Regional cuts of a winner come before new ideas. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push toward ₹5L: the budget climbs steeply and return falls, and Instagram Reels carries the most spend and Instagram Feed the next. As spend rises, we mix new creatives in with the proven ones before the old ones tire. We tie every budget increase to return: we step up while it holds, and step back when it drops.

   Why

   In Month 2 to Month 4 the budget climbs steeply, and return on spend falls as it does. Spend scaled in our measured accounts pushed cost per order up and return down, so no step is taken on hope. More spend means the same people see an ad more often, and click-through fades.

   How it works

   Proven ads stay while new ones are added. 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 Instagram Feed next. Cold audiences take most of the budget; warm audiences 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 ₹5L only as far as return allows. We refresh tired ads by mixing old and new creatives, and keep a creative bank. We use WhatsApp for abandoned checkouts and for past buyers' next order.

   Why

   Growth slows from Month 5, still short of ₹5L. Budget keeps rising, more slowly than in the scaling months. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional. A message to someone who nearly bought costs less than an ad.

   How it works

   New creatives mix with proven ones rather than replacing them all at once. Messages run beside retargeting ads.

1. Month 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The reported return is checked against the store-side return. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹48,361
   • ROAS 3.59x
   • Ad spend ₹13,488
2. Month 2

   Scaling starts: past buyers get a WhatsApp nudge for their next order. Return falls as the budget steps up sharply.

   • Revenue ₹68,119
   • ROAS 2.95x
   • Ad spend ₹23,116
3. Month 3

   The winning UGC runs in regional-language versions.

   • Revenue ₹1.2L
   • ROAS 2.36x
   • Ad spend ₹48,776
4. Month 4

   Customer-feedback and creator videos are refreshed. Return dips as the budget more than doubles.

   • Revenue ₹2.3L
   • ROAS 2.10x
   • Ad spend ₹1.1L
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back. Budget rises to the point where return starts to give way: return holds as the budget steps up. Revenue is now past halfway from ₹50,000 to ₹5L.

   • Revenue ₹3.1L
   • ROAS 2.09x
   • Ad spend ₹1.5L
6. Month 6

   Ads with falling click-through are swapped from the bank. Budget rises to the point where return starts to give way: return dips as the budget dips. ₹5L is not reached in the time, because budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹3.2L
   • ROAS 1.89x
   • Ad spend ₹1.7L

1. 1

   Scaled into winter against written daily spend numbers

2. 2

   Reconciled ad-platform and Shopify figures every month after cancellations

3. 3

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