Health & wellness case study: ₹10L to ₹23.4L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with cash-on-delivery returns and cancellations, which the brand named first, before anything else on this mid-sized store. We tilt orders to prepaid with a prepaid discount and a cash-on-delivery charge that covers its cost. We add cart-value offers that step up at set basket sizes to lift order value.

   Why

   A mid-sized health and wellness brand grows monthly revenue in 3 months, from ₹10L to ₹50L (5x). The main problem is cash-on-delivery returns and cancellations. Returned cash-on-delivery orders turn attributed revenue into a loss. Each extra item in a basket is revenue the ad has already paid for.

   How it works

   The prepaid offer shows on product pages and at checkout. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We set the numbers on net sales after returns and cancellations, not on logged revenue. We reconcile the return on spend the brand reports with store revenue before the first budget change. We write month-by-month revenue numbers with the brand's team that climb from ₹10L to ₹50L, and open every review with the month-to-date number against them.

   Why

   The return on ad spend it reported sits close to what stores of that size hold, and the case study starts from it. Returns and cancellations cut into logged revenue before it reaches the bank. A shortfall is caught in the month it happens, not at the end.

   How it works

   Each review opens with net sales against the month's number. Both returns are reviewed together each week. Each budget step is argued against the written number.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, static image and UGC and creator video, 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 run every lead product in a campaign of its own, read every week. The learning month runs at a low daily budget and moves up only when orders come through.

   Why

   Buying more before a batch is read means paying for guesses. Shared campaigns hide weak products; separate ones expose them fast. Spend in the learning phase pays for information.

   How it works

   Losing batches stop; winning ads take their budget. A product that does not sell is paused inside the week. The low budget stays until orders confirm the buyer. The number of new ads grows with spend; video makes up the largest part and static image the next.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push as far toward ₹50L as return allows: the budget climbs in steps and return falls, and Instagram Reels carries the most spend and Facebook Reels the next. We test cost caps, bid caps and CBO against ABO side by side before each budget step. We let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the budget climbs in steps, and return on spend falls as it does. One of these months stops its budget step where return starts to slip too far. Our accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Controls show which setup holds cost per purchase as spend rises.

   How it works

   Controls run as parallel versions and the one that holds cost is kept. Budget follows return month to month instead of a fixed ramp. In the final month, Instagram Reels takes the most spend and Facebook Reels the next most. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. The prepaid offer and the cash-on-delivery charge go live. Cart-value offers go live at checkout.

   • Revenue ₹10.4L
   • ROAS 3.43x
   • Ad spend ₹3L
2. Month 2

   The scaling phase opens: bid-cap and cost-cap versions run beside open bidding. With budget steps up sharply, return on spend dips.

   • Revenue ₹16.8L
   • ROAS 3.07x
   • Ad spend ₹5.5L
3. Month 3

   The budget decision is taken on the return the last step earned. Only the part of the step that return supports is taken: with budget steps up sharply, return on spend falls. The case study finishes short of ₹50L. Cost per purchase ends higher than in the learning phase.

   • Revenue ₹23.4L
   • ROAS 2.64x
   • Ad spend ₹8.9L

1. 1

   Product pages rebuilt for a trust-led buyer: offer price, COD, reviews, 30-day money-back guarantee, video reviews

2. 2

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

3. 3

   Add static images beside video once a winning video is found.