Kids case study: plan for ₹60L to ₹81.5L monthly revenue in 2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we'd do

   Start with results that swing from month to month, which the booking named first, before anything else on this established store. Build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. Mark the festive and wedding dates first and have seasonal collections ready ahead of them.

   Why

   The enquiry came from an established kids brand that wants to grow monthly revenue in 2 months, from ₹60L to ₹1Cr (+67%). 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 results that swing from month to month as its main problem. Separate layers stop prospecting and retargeting competing for the same budget. Buyers in this category shop around festivals and weddings.

   How it works

   Retargeting sits next to prospecting and never replaces it. Each peak has its campaign ready before it starts.

2. 2

   Measurement & targets · Week 1 to 9

   What we'd do

   Track ad-platform revenue beside store revenue and net sales after returns, cancellations and tax. Agree a written target for every week on the way from ₹60L to ₹1Cr, and start each review with the week-to-date figure against it.

   Why

   It did not report a return on ad spend, so the plan starts from what measured stores of that size hold. Returns, cancellations and tax cut logged revenue, so targets are set on net sales. A shortfall is caught in the week it happens, not at the end.

   How it works

   Budget decisions are read off net sales, not the ad platform alone. Written targets make each scaling decision explicit.

3. 3

   Creative testing · Week 1 to 4

   What we'd do

   Test with video, catalogue ads and static image first, rising to several dozen new ads a month by the last four weeks, keeping carousel on a short leash because its return trails the account. Work in creative batches with a fixed read window each. Test catalogue ads state by state and city by city, with close lookalikes of past buyers. The four learning weeks run on a small daily budget, stepping up only once orders confirm.

   Why

   Buying more before a batch is read means paying for guesses. Kids' demand varies by region and season. The first rupees are for finding the buyer, not for volume.

   How it works

   Only ads that convert inside the window keep running. Regions that convert keep their catalogue sets; the rest are cut. The low budget stays until orders confirm the buyer. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Week 5 to 9

   What we'd do

   Through Week 5 to Week 9, push as far toward ₹1Cr as return allows: the budget climbs in steps and return dips, and Instagram Reels carries the most spend and Facebook Feed the next. Move budget in three-to-four-day windows at campaign level. Let return decide budget: more while it holds, less when it slips.

   Why

   In Week 5 to Week 9 the modelled budget climbs in steps, and return on spend dips. Our measured accounts saw cost per order rise and return fall as spend scaled; that is why each step here waits on return. Short windows react to category demand without resetting learning every day.

   How it works

   Each window is read on purchases and cost per purchase before the next move. Budget follows return week to week instead of a fixed ramp. Instagram Reels carries the largest share of spend in the last four weeks, with Facebook Feed next. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The next festive collection is briefed and ready. Net sales after returns are matched to logged revenue.

   • Projected revenue ₹13.8L
   • Projected ROAS 3.62x
   • Planned ad spend ₹3.8L
2. Week 2

   First creative read: ads that do not convert are cut. Budget holds and return on spend holds.

   • Projected revenue ₹13.8L
   • Projected ROAS 3.61x
   • Planned ad spend ₹3.8L
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Projected revenue ₹13.4L
   • Projected ROAS 3.84x
   • Planned ad spend ₹3.5L
4. Week 4

   The learning phase ends with a buyer identified and a winning ad chosen. Budget steps up and return on spend dips.

   • Projected revenue ₹14.1L
   • Projected ROAS 3.54x
   • Planned ad spend ₹4L
5. Week 5

   Scaling starts: the creative batch is read and the winners keep the budget. Budget holds and return on spend holds.

   • Projected revenue ₹14.2L
   • Projected ROAS 3.47x
   • Planned ad spend ₹4.1L
6. Week 6

   Catalogue sets are kept in regions that convert and cut elsewhere. Budget steps up and return on spend dips.

   • Projected revenue ₹16.2L
   • Projected ROAS 3.20x
   • Planned ad spend ₹5.1L
7. Week 7

   Budget is reviewed against return before the next step. Budget holds and return on spend rises.

   • Projected revenue ₹19.1L
   • Projected ROAS 3.48x
   • Planned ad spend ₹5.5L
8. Week 8

   Budget moves in short windows at campaign level. Budget steps up and return on spend holds.

   • Projected revenue ₹21.9L
   • Projected ROAS 3.42x
   • Planned ad spend ₹6.4L
9. Week 9

   This batch's read is done: winners stay, the rest are cut. Budget steps up and return on spend dips. Revenue over the last four weeks passes the halfway point between ₹60L and ₹1Cr. Revenue ends below ₹1Cr, the target set at enquiry, because the plan follows what the fastest tenth of our measured accounts reached. Cost per purchase ends higher than in the learning phase.

   • Projected revenue ₹24.2L
   • Projected ROAS 3.15x
   • Planned ad spend ₹7.7L

1. 01

   A low average order value limited how far spend could scale.

2. 02

   Catalogue ads tested state-wise and city-wise, 1% lookalikes, Diwali retargeting

3. 03

   Scaled into winter against written daily spend targets