Revenue Architecture
in Action

The Challenge

The brand was scaling fast but bleeding cash. Every unit sold was generating negative contribution margin once Amazon fees, FBA costs, returns, and ad spend were accounted for. Leadership was confused — the P&L showed revenue growth but cash was disappearing.

Our Approach

• Full unit economics deep-dive: mapped CM1, CM2, and CM3 for every SKU across both platforms
• Identified top 12 SKUs with negative net margin — restructured pricing with fee-aware models
• Redesigned FBA vs FBM decision logic based on actual landed cost per SKU
• Built a contribution margin dashboard for daily visibility
• Restructured Sponsored Products architecture to optimize for CM, not just ROAS

The Challenge

The founder was spending ₹15L/month on Meta ads and generating ₹3Cr revenue — but couldn't explain where the profit was going. No unit economics model existed. Every quarter felt like a reset.

Our Approach

• Built a complete unit economics model from scratch — COGS, CAC, returns, logistics per order
• Identified that 60% of revenue came from first-time customers with negative CM2
• Designed a retention architecture: WhatsApp sequences, email lifecycle, loyalty structure
• Restructured Meta campaigns around repeat purchase audiences vs cold acquisition
• Introduced AOV-boosting offer structures (bundles, upsells) to improve blended economics

The Challenge

A well-funded brand with strong reviews and good sell-through rates was somehow losing ₹25 lakhs every month. The problem: no one had ever mapped the full cost stack per order. Marketing was optimized for ROAS. Logistics was treated as fixed. Returns were ignored.

Our Approach

• Emergency unit economics audit: mapped every rupee of cost per order across all channels
• Found that return rate of 22% was costing ₹8L/month that no one was tracking
• Redesigned packaging and product descriptions to systematically reduce returns
• Rebuilt pricing architecture with full fee stack visibility
• Created blended CAC model and restructured media mix accordingly

The Challenge

The brand was over-indexed on Amazon and knew it — but every attempt to expand to Flipkart, D2C Shopify, or quick commerce had either failed or been abandoned after burning cash. They needed a systematic channel economics framework, not another agency promising growth.

Our Approach

• Built a channel profitability scorecard — net margin, CAC, payback period, and risk profile per channel
• Prioritized Flipkart (lowest CAC, strong category demand) and D2C (highest LTV potential)
• Designed Shopify store with conversion-first architecture and retention infrastructure from day one
• Introduced Google Shopping as the primary D2C acquisition engine (lower CPCs, high intent)
• Structured inventory and working capital allocation by channel margin profile

The Challenge

The brand had excellent product-market fit — NPS of 72, strong reviews — but customers weren't coming back. Without a retention system, every month meant starting from scratch on acquisition. Rising Meta CPMs were eating into already thin margins.

Our Approach

• Mapped customer lifecycle and identified that 68% of LTV came from orders 2-4
• Built a 21-day post-purchase WhatsApp sequence driving second purchase
• Designed email lifecycle: welcome, education, replenishment, loyalty, and win-back flows
• Created a referral program tied to replenishment cycle timing
• Restructured Meta campaigns to exclude recent purchasers and focus budget on lookalike of repeat buyers