Food & beverage case study: ₹8.33L to ₹30.3L monthly revenue in 12 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at marketing and social presence, the brand’s main problem, on a mid-sized base. We put the website audit, the creative brief and the monthly media schedule in place before spend moves. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a mid-sized food and beverage brand grows monthly revenue in 1 year, from ₹8.33L to ₹1Cr (12x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The main problem was marketing and social presence. Later budget calls need something written to be judged against. Every rupee of ads is capped by how well the store turns visits into orders.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. Fixes are listed and handed over early, so later budget lands on a store that converts. Tier levels are set just above the basket sizes buyers already reach.

2. 2

   Measurement & reviews · Month 1 to 12

   What we do

   We set the path from ₹8.33L to ₹1Cr as written monthly numbers, and read every review against the month so far. We track ad-platform revenue beside store orders in a shared daily sheet. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   No starting return on ad spend was given; the starting return is what measured stores of that size hold. A shortfall is caught in the month it happens, not at the end. Platform attribution over-counts, so budget decisions sit on the store-side number.

   How it works

   Each budget step is argued against the written number. The sheet is read before each budget change. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We 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. We put most of the budget behind one broad video campaign of the core products, including a customer testimonial. We separate the lead products into their own campaigns and review each one weekly. We work in creative batches with a fixed read window each.

   Why

   Broad delivery lets the algorithm find buyers for an everyday product. A product nobody buys is visible within a week when it has its own campaign. Buying more before a batch is read means paying for guesses.

   How it works

   The video that takes off is scaled; the tests around it are cut. A product that does not sell is paused inside the week. Losing batches stop; winning ads take their budget. 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 9

   What we do

   Through Month 2 to Month 9, we push toward ₹1Cr: the budget climbs steeply and return falls, and Instagram Reels carries the most spend and Facebook Feed the next. We let return decide budget: more while it holds, less when it slips. We front-load about a third of each month's budget into the first week. We cut regional-language versions of the winning video for the best-selling states.

   Why

   Across Month 2 to Month 9, the budget climbs steeply, and return on spend falls as it does. In two of these months the step is trimmed to the size return can hold. One of these months hold the budget where it is until return improves. In measured accounts, scaling spend raised cost per order and lowered return on spend, so each step waits for return to hold. 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. Regional cuts run beside the original winner. Most of the final month's spend sits on Instagram Reels, then Facebook Feed. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 10 to 12

   What we do

   From Month 10, we hold the gains and push toward ₹1Cr only as far as return allows. We keep a bank of ready creatives and rotate them in as ads tire. We recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   Growth slows from Month 10, still short of ₹1Cr. Spend holds roughly steady from here. A measured account showed click-through falling ahead of revenue, so tired ads are replaced early. A message to someone who nearly bought costs less than an ad.

   How it works

   Refreshes are gradual: a few new ads at a time. WhatsApp runs alongside paid retargeting, not instead of it.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. Basket-size offers switch on at checkout. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹8.6L
   • ROAS 3.20x
   • Ad spend ₹2.7L
2. Month 2

   Scaling begins: the creative bank supplies this period's refresh. With budget steps up, return on spend holds.

   • Revenue ₹9.9L
   • ROAS 3.08x
   • Ad spend ₹3.2L
3. Month 3

   The weekly product read pauses the products that are not selling.

   • Revenue ₹11.1L
   • ROAS 2.97x
   • Ad spend ₹3.7L
4. Month 4

   The creative batch is read and the winners keep the budget.

   • Revenue ₹14.9L
   • ROAS 2.88x
   • Ad spend ₹5.2L
5. Month 5

   Repeat-order messages go to past buyers.

   • Revenue ₹18.8L
   • ROAS 2.64x
   • Ad spend ₹7.1L
6. Month 6

   The winner now also runs in regional languages. With budget steps up sharply, return on spend dips.

   • Revenue ₹27.4L
   • ROAS 2.46x
   • Ad spend ₹11.1L
7. Month 7

   The budget decision is taken on the return the last step earned. Budget is raised only as far as return allows: with budget steps up, return on spend dips.

   • Revenue ₹29.5L
   • ROAS 2.32x
   • Ad spend ₹12.7L
8. Month 8

   Tired ads are refreshed from the creative bank. The budget waits on return rather than on the calendar, so with budget holds, return on spend rises.

   • Revenue ₹30.6L
   • ROAS 2.48x
   • Ad spend ₹12.3L
9. Month 9

   Budget shifts to the products that sold this period. Budget is raised only as far as return allows: with budget holds, return on spend dips.

   • Revenue ₹29.3L
   • ROAS 2.32x
   • Ad spend ₹12.7L
10. Month 10

    Steady phase begins: creative refresh and repeat orders take on more of the work. The budget waits on return rather than on the calendar, so with budget holds, return on spend holds.

    • Revenue ₹30L
    • ROAS 2.39x
    • Ad spend ₹12.5L
11. Month 11

    This batch's read is done: winners stay, the rest are cut. Budget is raised only as far as return allows: with budget holds, return on spend holds.

    • Revenue ₹29.6L
    • ROAS 2.35x
    • Ad spend ₹12.6L
12. Month 12

    Past buyers get a WhatsApp nudge for their next order. The budget waits on return rather than on the calendar, so with budget holds, return on spend rises. The case study finishes short of ₹1Cr: budget stops rising where return starts to slip. Cost per purchase ends higher than in the learning phase.

    • Revenue ₹30.3L
    • ROAS 2.45x
    • Ad spend ₹12.3L

1. 1

   Cost per order stayed well above what a low-price consumable can carry.

2. 2

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

3. 3

   Tracked Meta and Shopify side by side, day by day, in a shared sheet