Food & beverage case study: plan for ₹20L to ₹37.3L monthly revenue in 9 months

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   The booking named keeping return on spend while scaling first, so the opening month on this established store goes there. Layer the account: a creative-testing campaign, a scaling campaign, cold audiences that exclude past buyers, and cart retargeting. Fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. Mark the festive and wedding dates first and have seasonal collections ready ahead of them.

   Why

   An established food and beverage store asked us how to grow monthly revenue in 9 months, from ₹20L to ₹1Cr (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. The main problem named at booking was keeping return on spend while scaling, followed by growth that has stalled. Separate layers stop prospecting and retargeting competing for the same budget. No budget returns more than the store converts.

   How it works

   Warm layers run beside prospecting, not instead of it. Fixes are listed and handed over early, so later budget lands on a store that converts. Seasonal campaigns are prepared ahead of each peak.

2. 2

   Measurement & targets · Month 1 to 9

   What we'd do

   Report net sales after returns, cancellations and tax next to store revenue and platform revenue. Agree a written target for every month on the way from ₹20L to ₹1Cr, and start each review with the month-to-date figure against it. Check the reported return on spend against store revenue before any budget moves.

   Why

   Its reported return on ad spend is under what measured food and beverage stores of that size hold; the plan begins from it and rebuilds return first. Returns, cancellations and tax cut logged revenue, so targets are set on net sales. A missed month shows up early instead of at the end of the plan.

   How it works

   Budget decisions are read off net sales, not the ad platform alone. Each budget step is argued against the written target. The store-side return becomes the number every review uses.

3. 3

   Creative testing · Month 1

   What we'd do

   Test with video, catalogue ads and UGC and creator video first, rising to several dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. Run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. The learning month runs on a small daily budget, stepping up only once orders confirm. Give each lead product its own campaign and read it weekly.

   Why

   Broad delivery lets the algorithm find buyers for an everyday product. The first rupees are for finding the buyer, not for volume. Shared campaigns hide weak products; separate ones expose them fast.

   How it works

   A video that takes off gets the budget; tests that do not convert are cut. Spend steps up only after orders confirm at the low budget. Losing products are paused within a week and budget moves to the winners. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 6

   What we'd do

   Scale through Month 2 to Month 6 as far toward ₹1Cr as return allows as the budget rises gently while return rises, with Instagram Reels taking the largest share of spend and Instagram Feed the next. Let return decide budget: more while it holds, less when it slips. Put seasonal collection campaigns in front of each festive and wedding peak. Front-load about a third of each month's budget into the first week.

   Why

   Across Month 2 to Month 6, the modelled budget rises gently, while return on spend rises. Return rises through these months as it climbs from where the account started toward what measured stores of its size hold. Demand is seasonal and regional, so spend moves with the calendar.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. Spend shifts into each season and between regions with the calendar. The monthly budget curve is weighted to the opening days. Instagram Reels carries the largest share of spend in the final month, with Instagram Feed next. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 7 to 9

   What we'd do

   From Month 7, protect the revenue already built and move toward ₹1Cr where return permits. Hold budget where return peaks instead of pushing past it. Refresh tired ads by mixing old and new creatives, and keep a creative bank.

   Why

   From Month 7 growth slows while revenue is still under ₹1Cr. Budget keeps rising, more slowly than in the scaling months. Return on spend fell from its peak month in most of our engagements; peaks do not hold. 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. New creatives mix with proven ones rather than replacing them all at once.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. The store fix list is live, with reviews on product pages and a prepaid offer. Net sales are reconciled against logged revenue.

   • Projected revenue ₹19.8L
   • Projected ROAS 2.86x
   • Planned ad spend ₹6.9L
2. Month 2

   The scaling phase opens: budget is held at the level where return peaked. Return on spend rises while budget holds.

   • Projected revenue ₹20.7L
   • Projected ROAS 3.10x
   • Planned ad spend ₹6.7L
3. Month 3

   Budget shifts to the products that sold this period.

   • Projected revenue ₹22.6L
   • Projected ROAS 3.42x
   • Planned ad spend ₹6.6L
4. Month 4

   The seasonal collection campaign leads this period's spend mix. Return on spend dips while budget steps up.

   • Projected revenue ₹25.6L
   • Projected ROAS 3.04x
   • Planned ad spend ₹8.4L
5. Month 5

   A seasonal collection campaign takes a larger share of budget. Return on spend rises while budget holds.

   • Projected revenue ₹27.7L
   • Projected ROAS 3.23x
   • Planned ad spend ₹8.6L
6. Month 6

   The weekly product read pauses the products that are not selling. Return on spend dips while budget steps up.

   • Projected revenue ₹31.6L
   • Projected ROAS 3.00x
   • Planned ad spend ₹10.5L
7. Month 7

   Steady phase begins: creative refresh and repeat orders take on more of the work. Return on spend holds while budget holds.

   • Projected revenue ₹32.8L
   • Projected ROAS 3.05x
   • Planned ad spend ₹10.8L
8. Month 8

   Budget is held at the level where return peaked.

   • Projected revenue ₹34.3L
   • Projected ROAS 2.95x
   • Planned ad spend ₹11.6L
9. Month 9

   The weekly product read pauses the products that are not selling. Return on spend holds while budget holds. ₹1Cr is not reached in the time, because the plan follows what the fastest tenth of our measured accounts reached. Cost per purchase ends close to the learning phase.

   • Projected revenue ₹37.3L
   • Projected ROAS 2.93x
   • Planned ad spend ₹12.7L

1. 01

   Layer the account: creative testing, a scaling campaign, new audiences that exclude past buyers, and catalogue or cart retargeting.

2. 02

   Kept remarketing on add-to-cart and checkout audiences (11.2x)

3. 03

   Refreshed tired ads by mixing old and new creatives; one product ad returned to 24.8x