Food & beverage case study: ₹50L–₹60L to ₹95.1L monthly revenue in 6 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is turning visits into orders, so the opening month on this established store goes there. We fix the store before scaling: trust pointers, reviews, a clear return window, an about page and a prepaid incentive. We put the website audit, the creative brief and the monthly media schedule in place before spend moves.

   Why

   An established food and beverage store grows monthly revenue in 6 months, from ₹50L–₹60L to ₹1.5Cr (2.7x). 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 turning visits into orders, followed by marketing and social presence. Conversion rate caps what any ad budget can return. A written brief gives every later budget decision a reference point.

   How it works

   The fix list goes to the brand's team in the opening weeks, ahead of any budget step. The brief is agreed first; spend follows it.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We report net sales after returns, cancellations and tax next to store revenue and platform revenue. We write month-by-month revenue numbers with the brand's team that climb from ₹50L–₹60L to ₹1.5Cr, and open every review with the month-to-date number against them. We match the return the brand in this scenario reports to what the store actually took in, before touching budget.

   Why

   Its reported return on ad spend is under what measured food and beverage stores of that size hold; the case study begins from it and rebuilds return first. In measured accounts net sales ran below logged store revenue, so the numbers sit on the lower figure. Written monthly numbers 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. The month's number is on the page at every review. The reported return and the store-side return are read side by side every week.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, catalogue ads and UGC and creator video first, rising to several dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We run every lead product in a campaign of its own, read every week. We run creator, customer-feedback and founder-led video. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget.

   Why

   A product nobody buys is visible within a week when it has its own campaign. In most accounts we measured, video that carried trust held its return. Broad delivery lets the algorithm find buyers for an everyday product.

   How it works

   Budget follows the products that sell. Weak UGC is swapped for founder-led video rather than scaled. A video that takes off gets the budget; tests that do not convert are cut. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   Through Month 2 to Month 4, we push as far toward ₹1.5Cr as return allows: the budget rises gently while return rises, and Instagram Reels carries the most spend and Facebook Reels the next. We tie every budget increase to return: we step up while it holds, and step back when it drops. We front-load about a third of each month's budget into the first week.

   Why

   In Month 2 to Month 4 the budget rises gently, while return on spend rises. 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. In the final month, Instagram Reels takes the most spend and Facebook Reels the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 5 to 6

   What we do

   From Month 5, we protect the revenue already built and move toward ₹1.5Cr where return permits. We recover abandoned checkouts and lift repeat orders with WhatsApp messages. We hold the numbers on net sales as volume grows, since returns rise with it.

   Why

   From Month 5 growth slows while revenue is still under ₹1.5Cr. Spend continues to grow. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout. Net sales ran below logged store revenue in our measured accounts.

   How it works

   WhatsApp runs alongside paid retargeting, not instead of it. Each review opens with net sales against the month's number.

1. Month 1

   Learning month: tracking is checked against store orders and the first ads go live on a small daily budget. The store fix list is live, with reviews on product pages and a prepaid offer. The audit, brief and media schedule are agreed with the brand's team.

   • Revenue ₹52.1L
   • ROAS 3.46x
   • Ad spend ₹15L
2. Month 2

   Scaling begins: products that do not sell are paused and budget moves to the winners. Spend holds, and return holds.

   • Revenue ₹55.5L
   • ROAS 3.57x
   • Ad spend ₹15.5L
3. Month 3

   New creator and customer-feedback videos join the account. Spend steps up, and return holds.

   • Revenue ₹62.3L
   • ROAS 3.61x
   • Ad spend ₹17.3L
4. Month 4

   Past buyers get a WhatsApp nudge for their next order.

   • Revenue ₹74.5L
   • ROAS 3.71x
   • Ad spend ₹20.1L
5. Month 5

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

   • Revenue ₹87.6L
   • ROAS 3.18x
   • Ad spend ₹27.6L
6. Month 6

   The budget decision is taken on the return the last step earned. Spend holds, and return rises. Revenue ends below ₹1.5Cr, the number this scenario calls for, because the case study follows what the fastest tenth of our measured accounts reached. Cost per purchase ends close to the learning phase.

   • Revenue ₹95.1L
   • ROAS 3.37x
   • Ad spend ₹28.2L

1. 1

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

2. 2

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

3. 3

   Facebook Reels carries the largest share of spend in this industry's measured accounts.