Enterprise Portfolio GTM & Channel Economics
Formula-linked 9-sheet financial model and channel sensitivity analysis across two software products
Context & Core Problem
An early-stage enterprise technology group evaluating capital allocation across B2B marketplace and financial software distribution channels required an objective go-to-market model testing whether unit economics cleared standard investment hurdles.
Architectural Approach & Decisions
Constructed a bottom-up market sizing structure using published government enterprise counts (Udyam, DPIIT) and financial intermediary registers. Built a 9-sheet formula-linked workbook where every input recalculates end-to-end. Ran a comparative channel acquisition test between Direct-to-Consumer and B2B2C Financial Advisor distribution.
Verifiable Evidence & Observable Outcomes
Built a bottom-up TAM-to-SAM compression using published government enterprise registries. Compared two distribution channels: direct-to-consumer proved economically unviable at modeled acquisition costs, while the intermediary route was significantly more capital-efficient but still failed the client's internal investment hurdle on LTV/CAC ratio and payback period.
Empirical Result
Delivered an honest failing-hurdle analysis rather than smoothing projections. Identified the exact acquisition cost threshold needed to clear investment hurdles via sensitivity analysis. Structured a staged pilot strategy contingent on verified metrics before committing larger capital.
System Architecture & Data Flow
Bottom-Up Sizing
Segment counts from official Udyam and DPIIT registries
Unit Economics
Retention-decayed LTV, CAC payback, and contribution margin
Sensitivity Stress
Identified exact ₹4,000 CAC threshold to clear hurdle
Gated Allocation
Phased capital release protecting founder cash reserves
Technical Architecture Specifications
Transparent Limitations & Governance Boundaries
Sanitized advisory case study. Client identities, proprietary metrics, and brand names are generalized or synthetic to strictly comply with confidentiality standards. All mathematical models operate on generalized industry benchmark parameters.