Health & wellness case study: ₹65,000 to ₹76,895 monthly revenue in 4 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 layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart remarketing. We keep a clean campaign setup ready to relaunch if hostile comments start hurting delivery.

   Why

   In this case study, a smaller health and wellness brand grows monthly revenue in 4 months, from ₹65,000 to ₹10L (15.4x). 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 creative and content, followed by results that swing from month to month. Layers keep each budget line readable, so a weak campaign cannot hide inside a strong one. Hostile comments under running ads depress their delivery and conversion.

   How it works

   Warm layers run beside prospecting, not instead of it. Campaigns are relaunched on a clean setup rather than left to degrade.

2. 2

   Measurement & reviews · Month 1 to 4

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We match the return the brand in this scenario reports to what the store actually took in, before touching budget. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   The return on ad spend it reported sits above what measured health and wellness stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. The brand in this scenario also asked for a return on ad spend of 4x; the case study ends below that. Return gives way as spend rises, so that return and this revenue do not come together in the time. Ad platforms claim more orders than stores record, so the store number decides budget. In the store accounts we measured, one budget step-up in four lost about a quarter of its return or more, so a step is taken only while return holds.

   How it works

   The sheet is read before each budget change. The reported return and the store-side return are read side by side every week. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building 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 buy creative in batches and give each batch a fixed read window before buying more. We run creator, customer-feedback and founder-led video. We keep creators producing UGC video on a steady schedule, and cut the winners into regional languages.

   Why

   Buying more before a batch is read means paying for guesses. 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

   Losing batches stop; winning ads take their budget. UGC that underperforms is replaced by founder-led video, not given more budget. Regional cuts of a winner come before new ideas. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 toward ₹10L as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Facebook Reels the next. We refresh tired ads by mixing old and new creatives as spend rises. 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 3 the budget rises gently, while return on spend holds. In two of these months the step is trimmed to the size return can hold. Return holds through these months because each budget step stops where return starts to slip. 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. Most of the final month's spend sits on Instagram Reels, then Facebook Reels. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

5. 5

   Steady state · Month 4

   What we do

   From Month 4, we protect the revenue already built and move toward ₹10L where return permits. We hold budget where return peaks instead of pushing past it. We keep a bank of ready creatives and rotate them in as ads tire.

   Why

   From Month 4 growth slows while revenue is still under ₹10L. Budget keeps rising, more slowly than in the scaling months. Most of our engagements saw return fall back from its best month. In one account we measured, click-through fell before revenue did; refresh is how that is avoided.

   How it works

   Budget stays at the level of the best return and is trimmed when it slips. The bank means a tired ad is replaced the week it tires.

1. Month 1

   The learning phase opens: the first ads run on a small daily budget while tracking is checked against store orders. The daily sheet now matches platform revenue to store orders. The account runs in layers: testing, scaling and retargeting.

   • Revenue ₹68,304
   • ROAS 2.02x
   • Ad spend ₹33,865
2. Month 2

   The scaling phase opens: the winning UGC runs in regional-language versions. The step is sized by what return can bear: return on spend dips while budget steps up.

   • Revenue ₹78,824
   • ROAS 1.87x
   • Ad spend ₹42,253
3. Month 3

   A new batch goes live after the last one is read. The step is sized by what return can bear: return on spend rises while budget holds.

   • Revenue ₹78,591
   • ROAS 1.96x
   • Ad spend ₹39,999
4. Month 4

   Steady phase begins: creative refresh and repeat orders take on more of the work. The step is sized by what return can bear: return on spend dips while budget holds. ₹10L 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. Against the 4x this scenario calls for, the case study finishes below.

   • Revenue ₹76,895
   • ROAS 1.87x
   • Ad spend ₹41,140

1. 1

   Relaunch on a clean setup when negative comments start hurting delivery.

2. 2

   Relaunched campaigns on a new pixel the moment hostile comments appeared

3. 3

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