Health & wellness case study: ₹28,000 to ₹47,617 monthly revenue in 2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   We start with return on ad spend, which the brand in this scenario named first, before anything else on this smaller store. We add cart-value offers that step up at set basket sizes to lift order value. We build audiences on the buyer's life and profession, not on health-condition interests.

   Why

   In this case study, a smaller health and wellness brand grows monthly revenue in 2 months, from ₹28,000 to ₹2L (7.1x). 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. A larger basket spreads the cost of each order over more revenue. Condition-based audience setting is restricted and reaches the wrong buyer.

   How it works

   Offers are tuned to the order values that already sell. Lifestyle audiences replace condition interests from the first campaign.

2. 2

   Measurement & reviews · Week 1 to 9

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We set the path from ₹28,000 to ₹2L as written weekly numbers, and read every review against the week so far. We read results in three-to-four-day windows, since the case study runs week by week.

   Why

   No starting return on ad spend was given; the starting return is what measured health and wellness stores of that size hold. Ad platforms claim more orders than stores record, so the store number decides budget. A shortfall is caught in the week it happens, not at the end.

   How it works

   The sheet is read before each budget change. Each budget step is argued against the written number. Each window is read on purchases and cost per purchase before the next move.

3. 3

   Creative testing · Week 1 to 4

   What we do

   We lead the testing layer with video, static image and catalogue ads, building to about a dozen new ads a month by the last four weeks, with no format far behind the account's return. We separate the lead products into their own campaigns and review each one weekly. The four learning weeks run on a small daily budget, stepping up only once orders confirm. We build creative around the buyer's everyday routine rather than a condition.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Early spend buys learning, not scale. Condition claims are restricted and reach the wrong buyer.

   How it works

   Budget follows the products that sell. The low budget stays until orders confirm the buyer. Everyday-routine creative is read against the lifestyle audiences. New ads rise with the budget, most of them video, then static image.

4. 4

   Scaling · Week 5 to 9

   What we do

   Through Week 5 to Week 9, we push toward ₹2L: the budget climbs in steps and return dips, and Instagram Reels carries the most spend and Facebook Feed the next. We let return decide budget: more while it holds, less when it slips. As spend rises, we mix new creatives in with the proven ones before the old ones tire.

   Why

   Across Week 5 to Week 9, the budget climbs in steps, and return on spend dips. Budget is part-stepped in four of these weeks, taking only what return can carry. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. Frequency climbs with spend, and tired ads lose click-through first.

   How it works

   Budget follows return week to week instead of a fixed ramp. Proven ads stay while new ones are added. Most of the last four weeks's spend sits on Instagram Reels, then Facebook Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   Learning starts: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and platform revenue are reconciled in the shared sheet. Basket-size offers switch on at checkout.

   • Revenue ₹6,626
   • ROAS 2.01x
   • Ad spend ₹3,292
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Spend holds, and return holds.

   • Revenue ₹6,766
   • ROAS 2.05x
   • Ad spend ₹3,305
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Revenue ₹6,541
   • ROAS 2.01x
   • Ad spend ₹3,249
4. Week 4

   The learning phase ends with a buyer identified and a winning ad chosen.

   • Revenue ₹7,042
   • ROAS 2.02x
   • Ad spend ₹3,485
5. Week 5

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

   • Revenue ₹8,548
   • ROAS 1.98x
   • Ad spend ₹4,310
6. Week 6

   The weekly product read pauses the products that are not selling. Budget is raised only as far as return allows: spend steps up, and return holds.

   • Revenue ₹10,800
   • ROAS 1.94x
   • Ad spend ₹5,559
7. Week 7

   New creatives join the proven ones.

   • Revenue ₹12,286
   • ROAS 1.88x
   • Ad spend ₹6,549
8. Week 8

   The budget decision is taken on the return the last step earned. Budget is raised only as far as return allows: spend holds, and return holds.

   • Revenue ₹12,398
   • ROAS 1.91x
   • Ad spend ₹6,493
9. Week 9

   The weekly product read pauses the products that are not selling. Budget is raised only as far as return allows: spend holds, and return holds. The case study finishes short of ₹2L: budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹12,133
   • ROAS 1.91x
   • Ad spend ₹6,354

1. 1

   Opened a month-to-date table against the cap on every weekly call

2. 2

   Relaunched campaigns on a new pixel the moment hostile comments appeared

3. 3

   Rebuilt creative around the two services that convert, plus a founder-led ad