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

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

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

   Why

   The enquiry came from a mid-sized health and wellness brand that wants to grow monthly revenue in 3 months, from ₹10L to ₹50L (5x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the plan is built on what the fastest tenth reached, and shows the gap to the target honestly. At booking, the brand named cash-on-delivery returns and cancellations as its main problem. 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 & targets · Month 1 to 3

   What we'd do

   Set targets on net sales after returns and cancellations, not on logged revenue. Reconcile the return on spend the brand reports with store revenue before the first budget change. Write month-by-month targets 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 measured stores of that size hold, and the plan 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 target. Both returns are reviewed together each week. Each budget step is argued against the written target.

3. 3

   Creative testing · Month 1

   What we'd do

   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. Work in creative batches with a fixed read window each. 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'd do

   Through Month 2 to Month 3, 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. Test cost caps, bid caps and CBO against ABO side by side before each budget step. Let return decide budget: more while it holds, less when it slips.

   Why

   In Month 2 to Month 3 the modelled budget climbs in steps, and return on spend falls as it does. One of these months stop their budget step where return would slip too far. Our measured 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.

   • Projected revenue ₹10.4L
   • Projected ROAS 3.43x
   • Planned 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.

   • Projected revenue ₹16.8L
   • Projected ROAS 3.07x
   • Planned 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 plan finishes short of ₹50L: the plan follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹23.4L
   • Projected ROAS 2.64x
   • Planned ad spend ₹8.9L

1. 01

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

2. 02

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

3. 03

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