Food & beverage case study: ₹2L to ₹3L monthly revenue in 2 months

New ads per month

1. 1

   Research & offer · Week 1 to 2

   What we do

   We give the first week to scaling ad spend, the brand’s main problem, on a smaller base. We build the account in layers so each can be read on its own: testing, scaling, new-buyer prospecting and cart retargeting. We 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

   A smaller food and beverage brand grows monthly revenue in 2 months, from ₹2L to ₹10L (5x). The main problem is scaling ad spend, with reaching new buyers close behind. Separate layers stop prospecting and retargeting competing for the same budget. Conversion rate caps what any ad budget can return.

   How it works

   Retargeting sits next to prospecting and never replaces it. Fixes are listed and handed over early, so later budget lands on a store that converts.

2. 2

   Measurement & reviews · Week 1 to 9

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We write week-by-week revenue numbers with the brand's team that climb from ₹2L to ₹10L, and open every review with the week-to-date number against them. We set a written rule: no budget step in a week where return falls too far to pay for it.

   Why

   The return on ad spend it reported sits above what food and beverage stores of that size hold; the case study starts from it and allows for some of it to give way as spend rises. The ad platform's own count runs high, so the store's count is the one that moves budget. A missed week shows up early instead of at the end of the case study.

   How it works

   Budget decisions are read off store numbers, not the ad platform alone. The week's number is on the page at every review. Where return starts to slip too far, the week keeps last week's budget.

3. 3

   Creative testing · Week 1 to 4

   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 last four weeks, with carousel watched closely since it returns less than the rest. We run every lead product in a campaign of its own, read every week. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. The four learning weeks run with a deliberately small daily budget until orders prove the buyer.

   Why

   A product nobody buys is visible within a week when it has its own campaign. An everyday product sells best when the algorithm is free to find its buyers. Spend in the learning phase pays for information.

   How it works

   A product that does not sell is paused inside the week. A video that takes off gets the budget; tests that do not convert are cut. Only confirmed orders at the low budget unlock the next step. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Week 5 to 9

   What we do

   Through Week 5 to Week 9, we push as far toward ₹10L as return allows: the budget rises gently while return holds, and Instagram Reels carries the most spend and Facebook Reels the next. We make regional-language versions of the winning video for the states that buy most. We put seasonal collection campaigns in front of each festive and wedding peak.

   Why

   In Week 5 to Week 9 the budget rises gently, while return on spend holds. Budget is part-stepped in two of these weeks, taking only what return can carry. Return holds through these weeks because each budget step stops where return starts to slip. The same winning idea reaches more buyers in their own language.

   How it works

   The regional versions run next to the original. Spend shifts into each season and between regions with the calendar. Most of the last four weeks's spend sits on Instagram Reels, then Facebook Reels. Cold audiences take most of the budget; warm audiences return more per rupee.

1. Week 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. Testing, scaling and retargeting now run as separate layers. Store fixes go live: reviews, trust pointers and the prepaid offer.

   • Revenue ₹45,897
   • ROAS 1.99x
   • Ad spend ₹23,118
2. Week 2

   The first read of the ads is in, and the ones that do not convert stop. Return holds as the budget holds.

   • Revenue ₹48,719
   • ROAS 2.03x
   • Ad spend ₹23,961
3. Week 3

   Store fixes and offers go live while orders confirm.

   • Revenue ₹47,755
   • ROAS 1.99x
   • Ad spend ₹24,026
4. Week 4

   By the end of learning, the buyer is known and one ad has won.

   • Revenue ₹50,149
   • ROAS 1.97x
   • Ad spend ₹25,475
5. Week 5

   Scaling begins: the seasonal collection campaign leads this period's spend mix. Return holds as the budget holds.

   • Revenue ₹49,949
   • ROAS 2.01x
   • Ad spend ₹24,878
6. Week 6

   The winner now also runs in regional languages. Return holds as the budget steps up.

   • Revenue ₹58,343
   • ROAS 2.03x
   • Ad spend ₹28,764
7. Week 7

   Budget shifts to the products that sold this period.

   • Revenue ₹75,044
   • ROAS 1.89x
   • Ad spend ₹39,733
8. Week 8

   A seasonal collection campaign takes a larger share of budget. The step is sized by what return can bear: return holds as the budget holds.

   • Revenue ₹79,940
   • ROAS 1.88x
   • Ad spend ₹42,456
9. Week 9

   The winner now also runs in regional languages. The step is sized by what return can bear: return holds as the budget holds. Revenue ends below ₹10L, the number the brand set, because budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase.

   • Revenue ₹83,030
   • ROAS 1.92x
   • Ad spend ₹43,272

1. 1

   Put most of the budget behind one broad video campaign of the core products, and scale a video that takes off.

2. 2

   Facebook Reels carries the largest share of spend in our accounts in this industry.

3. 3

   Monthly review calls that start with what has not worked