Food & beverage case study: ₹1.3L to ₹1.5L 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 reaching new buyers, so the opening month on this smaller store goes there. We mark the festive and wedding dates first and have seasonal collections ready ahead of them. We use cart-value tiers so larger baskets earn a better offer.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 6 months, from ₹1.3L to ₹10Cr (769.2x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth rather than forcing the number. The main problem was reaching new buyers. Demand in this category rises and falls with the festive calendar. Each extra item in a basket is revenue the ad has already paid for.

   How it works

   Seasonal campaigns are prepared ahead of each peak. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 6

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We set the path from ₹1.3L to ₹10Cr as written monthly numbers, and read every review against the month so far. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

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

   How it works

   Every budget call starts from the store's orders. Each budget step is argued against the written number. Where return slips too far, the month keeps last month's budget.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to a few dozen new ads a month by the final month, with carousel watched closely since it returns less than the rest. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. We commission ads in batches, and read each batch for a set window before ordering the next. We make trust the subject of the video: creator reels, customer feedback and founder-led clips.

   Why

   An everyday product sells best when the algorithm is free to find its buyers. Buying more before a batch is read means paying for guesses. Video built on trust was the format that held return in most measured accounts.

   How it works

   A video that takes off gets the budget; tests that do not convert are cut. Losing batches stop; winning ads take their budget. UGC that underperforms is replaced by founder-led video, not given more budget. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 4

   What we do

   We scale through Month 2 to Month 4 toward ₹10Cr as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Facebook Reels the next. We scale into the festive and wedding calendar with seasonal collection campaigns. We cut regional-language versions of the winning video for the best-selling states.

   Why

   Across Month 2 to Month 4, the budget rises gently, while return on spend holds. Three of these months stop their budget step where return slips too far. Each budget step here is sized so that return stays close to where it was. Seasonal demand moves by region, so the spend does too.

   How it works

   Spend shifts into each season and between regions with the calendar. The regional versions run next to the original. In the final month, Instagram Reels takes the most spend and Facebook Reels the next most. 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 do

   From Month 5, we protect the revenue already built and move toward ₹10Cr where return permits. We recover abandoned checkouts and lift repeat orders with WhatsApp messages. We refresh tired ads by mixing old and new creatives, and keep a creative bank.

   Why

   From Month 5 growth slows while revenue is still under ₹10Cr. Budget stays about level over these months. A message to someone who nearly bought costs less than an ad. In one account we measured, click-through fell before revenue did; refresh is how that is avoided.

   How it works

   Messages run beside retargeting ads. The bank means a tired ad is replaced the week it tires.

1. Month 1

   First, the set-up: the first ads run on a small daily budget while tracking is checked against store orders. The cart-value tiers are live. Seasonal collections are prepared for the next peak.

   • Revenue ₹1.2L
   • ROAS 1.90x
   • Ad spend ₹63,686
2. Month 2

   Scaling starts: tired ads are refreshed from the creative bank. Budget rises to the point where return starts to give way: with budget holds, return on spend rises.

   • Revenue ₹1.3L
   • ROAS 2.00x
   • Ad spend ₹67,147
3. Month 3

   The winner now also runs in regional languages.

   • Revenue ₹1.4L
   • ROAS 1.88x
   • Ad spend ₹72,143
4. Month 4

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

   • Revenue ₹1.4L
   • ROAS 1.89x
   • Ad spend ₹76,308
5. Month 5

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

   • Revenue ₹1.5L
   • ROAS 1.89x
   • Ad spend ₹77,517
6. Month 6

   Past buyers get a WhatsApp nudge for their next order. Budget rises to the point where return starts to give way: as budget holds, return on spend holds. The case study finishes short of ₹10Cr: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹1.5L
   • ROAS 1.90x
   • Ad spend ₹77,007

1. 1

   Run by region for regional festivals, and pan-India for Dasara and Diwali

2. 2

   15 UGC videos, including regional-language cuts for the two best-selling states

3. 3

   Buy creative in batches, give each batch a fixed read window, and refresh tired ads by mixing old and new.