Food & beverage case study: plan for ₹10,000 to ₹23,279 monthly revenue in 1 year

New ads per month

1. 1

   Research & offer · Month 1

   What we'd do

   Aim the first month at return on ad spend, the problem named at booking, on a smaller base. Use cart-value tiers so larger baskets earn a better offer. Get the store ready for paid traffic first: reviews and trust pointers on product pages, a visible return window, an about page and a reason to pay upfront. Build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting.

   Why

   The enquiry came from a smaller food and beverage brand that wants to grow monthly revenue in 1 year, from ₹10,000 to ₹10L (100x). That is faster than nine in ten of our measured accounts grew over the same time, so the plan below is modelled on what that fastest tenth reached and shows where it lands against the target. At booking, the brand named return on ad spend as its main problem. Each extra item in a basket is revenue the ad has already paid for. Conversion rate caps what any ad budget can return.

   How it works

   Offers are tuned to the order values that already sell. Site fixes are handed over in the first weeks, before budget rises. Each layer keeps its own budget line.

2. 2

   Measurement & targets · Month 1 to 12

   What we'd do

   Agree a written target for every month on the way from ₹10,000 to ₹10L, and start each review with the month-to-date figure against it. Keep a shared daily sheet with the ad platform's revenue next to real store orders. Set targets on net sales after returns and cancellations, not on logged revenue.

   Why

   It did not report a return on ad spend, so the plan starts from what measured food and beverage stores of that size hold. A missed month shows up early instead of at the end of the plan. Ad platforms claim more orders than stores record, so the store number decides budget.

   How it works

   Each budget step is argued against the written target. Budget decisions are read off store numbers, not the ad platform alone. Each review opens with net sales against the target.

3. 3

   Creative testing · Month 1

   What we'd do

   Lead the testing layer with video, catalogue ads and UGC and creator video, building to about a dozen new ads a month by the final month, with no format far behind the account's return. Run every lead product in a campaign of its own, read every week. Run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. The learning month runs with a deliberately small daily budget until orders prove the buyer.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. An everyday product sells best when the algorithm is free to find its buyers. The first rupees are for finding the buyer, not for volume.

   How it works

   Losing products are paused within a week and budget moves to the winners. A video that takes off gets the budget; tests that do not convert are cut. The low budget stays until orders confirm the buyer. The number of new ads grows with spend; video makes up the largest part and catalogue ads the next.

4. 4

   Scaling · Month 2 to 8

   What we'd do

   Through Month 2 to Month 8, push toward ₹10L: the budget climbs in steps while return holds, and Instagram Reels carries the most spend and Facebook Reels the next. Tie every budget increase to return: step up while it holds, step back when it drops. Front-load about a third of each month's budget into the first week. Cut regional-language versions of the winning video for the best-selling states.

   Why

   In Month 2 to Month 8 the modelled budget climbs in steps, while return on spend holds. In several of these months budget goes up only as far as return allows, so revenue grows more slowly than the target needs. Each budget step here is sized so that return stays close to where it was. Early-month demand is stronger for repeat consumables.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. The monthly budget curve is weighted to the opening days. The regional versions run next to the original. Instagram Reels carries the largest share of spend in the final month, with Facebook Reels next. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 9 to 12

   What we'd do

   From Month 9, protect the revenue already built and move toward ₹10L 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 9 growth slows while revenue is still under ₹10L. Budget stays about level over these months. Return on spend fell from its peak month in most of our engagements; peaks do not hold. Falling click-through preceded a revenue drop in a measured account, so creative refresh is not optional.

   How it works

   Budget stays at the level of the best return and is trimmed when it slips. Refreshes are gradual: a few new ads at a time.

1. Month 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. Store orders and ad-platform revenue are checked side by side. The cart-value tiers are live.

   • Projected revenue ₹10,537
   • Projected ROAS 1.90x
   • Planned ad spend ₹5,550
2. Month 2

   The scaling phase opens: the creative bank supplies this period's refresh. Return on spend holds while budget steps up.

   • Projected revenue ₹13,429
   • Projected ROAS 1.92x
   • Planned ad spend ₹7,012
3. Month 3

   Regional-language versions of the winner go live.

   • Projected revenue ₹17,468
   • Projected ROAS 1.88x
   • Planned ad spend ₹9,303
4. Month 4

   Budget is reviewed against return before the next step. Budget goes up only as far as return can carry it: return on spend holds while budget steps up.

   • Projected revenue ₹19,432
   • Projected ROAS 1.87x
   • Planned ad spend ₹10,375
5. Month 5

   Each budget move is read against the return it bought. Budget goes up only as far as return can carry it: return on spend holds while budget holds.

   • Projected revenue ₹20,878
   • Projected ROAS 1.87x
   • Planned ad spend ₹11,160
6. Month 6

   Products that do not sell are paused and budget moves to the winners.

   • Projected revenue ₹21,690
   • Projected ROAS 1.87x
   • Planned ad spend ₹11,571
7. Month 7

   The creative bank supplies this period's refresh.

   • Projected revenue ₹22,754
   • Projected ROAS 1.93x
   • Planned ad spend ₹11,796
8. Month 8

   Regional-language versions of the winner go live.

   • Projected revenue ₹22,901
   • Projected ROAS 1.92x
   • Planned ad spend ₹11,914
9. Month 9

   The plan moves into its steady phase, leaning on refreshed ads and repeat buyers.

   • Projected revenue ₹23,000
   • Projected ROAS 1.94x
   • Planned ad spend ₹11,865
10. Month 10

    Budget is held at the level where return peaked.

    • Projected revenue ₹23,163
    • Projected ROAS 1.97x
    • Planned ad spend ₹11,763
11. Month 11

    Budget is reviewed against return before the next step.

    • Projected revenue ₹22,855
    • Projected ROAS 1.90x
    • Planned ad spend ₹12,043
12. Month 12

    The weekly product read pauses the products that are not selling. Because a larger budget would not earn its keep at this return, return on spend holds while budget holds. The plan finishes short of ₹10L: budget stops rising where return would slip. Each order costs about what it did while learning.

    • Projected revenue ₹23,279
    • Projected ROAS 1.96x
    • Planned ad spend ₹11,891

1. 01

   Front-loaded 30–35% of each month's budget into the first eight days

2. 02

   Put 95% of the Meta budget behind videos of the core products, including a customer testimonial

3. 03

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