Food & beverage case study: plan for ₹30L to ₹42.2L monthly revenue in 6 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. Split the account into layers: one campaign to test creative, one to scale winners, prospecting that leaves out past buyers, and retargeting for carts. 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.

   Why

   An established food and beverage brand came to us to grow monthly revenue in 6 months, from ₹30L to ₹60L (2x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the plan aims at the pace of that top tenth, holds budget where return would slip, and does not force the target. At booking, the brand named keeping return on spend while scaling as its main problem, with return on ad spend close behind. Separate layers stop prospecting and retargeting competing for the same budget. Every rupee of ads is capped by how well the store turns visits into orders.

   How it works

   Warm layers run beside prospecting, not instead of it. The fix list goes to the brand's team in the opening weeks, ahead of any budget step.

2. 2

   Measurement & targets · Month 1 to 6

   What we'd do

   Track ad-platform revenue beside store revenue and net sales after returns, cancellations and tax. Set the path from ₹30L to ₹60L as written monthly targets, and read every review against the month so far. Check the reported return on spend against store revenue before any budget moves.

   Why

   The return on ad spend it reported is below the level where raising budget pays for a store of that size, so budget does not rise until return climbs. Returns, cancellations and tax cut logged revenue, so targets are set on net sales. Written targets expose a slow month while there is still time to act.

   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

   Lead the testing layer with video, catalogue ads and UGC and creator video, building to several dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. Give each lead product its own campaign and read it weekly. The learning month runs with a deliberately small daily budget until orders prove the buyer. Run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget.

   Why

   Products that do not sell show up inside a week, not after a month of shared budget. Early spend buys learning, not scale. Broad delivery lets the algorithm find buyers for an everyday product.

   How it works

   A product that does not sell is paused inside the week. Spend steps up only after orders confirm at the low budget. The video that takes off is scaled; the tests around it are cut. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we'd do

   Through Month 2 to Month 4, push as far toward ₹60L as return allows: the budget rises gently while return rises, and Instagram Reels carries the most spend and Instagram Feed 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.

   Why

   Across Month 2 to Month 4, the modelled budget rises gently, while return on spend rises. Two of these months stop their budget step where return would slip too far. Return improves over these months, moving from the starting level toward the level of measured stores of that size. Early-month demand is stronger for repeat consumables.

   How it works

   Each month's budget is set by the return the last one earned. 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. Most spend reaches people who have not bought yet; past visitors return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we'd do

   From Month 5, hold the gains and push toward ₹60L only as far as return allows. Stop pushing budget once return has peaked. Hold a creative bank and swap tired ads out before click-through falls.

   Why

   Growth slows from Month 5, still short of ₹60L. Spend still grows, at a slower pace than during scaling. Most of our engagements saw return fall back from its best month. In one account we measured, click-through fell before revenue did; refresh is how that is avoided.

   How it works

   Budget is held while return is at its best and cut back when it slips. Refreshes are gradual: a few new ads at a time.

1. Month 1

   Learning month: a small daily budget carries the first ads, and store orders are matched to tracking. The store-side return is now the one budget follows. The layered account is in place, with retargeting beside prospecting.

   • Projected revenue ₹30.2L
   • Projected ROAS 1.87x
   • Planned ad spend ₹16.1L
2. Month 2

   Scaling starts: the creative bank supplies this period's refresh. Spend holds, and return rises.

   • Projected revenue ₹32.4L
   • Projected ROAS 1.95x
   • Planned ad spend ₹16.6L
3. Month 3

   Each budget move is read against the return it bought. Budget is raised only as far as return allows: spend steps up, and return holds.

   • Projected revenue ₹35.5L
   • Projected ROAS 1.93x
   • Planned ad spend ₹18.4L
4. Month 4

   Budget is held at the level where return peaked. Budget is raised only as far as return allows: spend holds, and return rises.

   • Projected revenue ₹39.1L
   • Projected ROAS 2.02x
   • Planned ad spend ₹19.3L
5. Month 5

   From here the work shifts to keeping ads fresh and bringing buyers back.

   • Projected revenue ₹41.3L
   • Projected ROAS 1.97x
   • Planned ad spend ₹20.9L
6. Month 6

   Products that do not sell are paused and budget moves to the winners. More budget would not pay at this return, so spend holds, and return rises. ₹60L is not reached in the time, because budget stops rising where return would slip. Cost per purchase ends close to the learning phase.

   • Projected revenue ₹42.2L
   • Projected ROAS 2.06x
   • Planned ad spend ₹20.5L

1. 01

   Expect Instagram Reels to carry the largest share of spend in measured accounts across all industries.

2. 02

   Test Static image alongside the main format: it reached the top creative tier most often in measured accounts across all industries.

3. 03

   Moved a third of spend into one broad video campaign at 3.6x