Model LTV, CAC, payback, and cohort dynamics for SaaS unit economics.
npx skills add https://github.com/tmcga/alpha-stack --skill fpa
Please help me install this Agent Skill. Skill: fpa Source: https://github.com/tmcga/alpha-stack/tree/main/skills/fpa Command: npx skills add https://github.com/tmcga/alpha-stack --skill fpa
This unit-economics framework provides a repeatable, bottom-up method to model and optimize profitability at the per-unit level for SaaS, ecommerce, marketplaces, and services.
Provide a bottom-up unit-economics model and generate a cohort-based LTV/CAC analysis for your SaaS business.
High-intent search queries and answers about installing and using this skill.
To calculate LTV and CAC for SaaS segments, you need a bottom-up unit-economics framework that models per-unit profitability across cohorts. This approach computes segment-level LTV, CAC, and payback periods by analyzing churn, expansion, and gross margin trajectories for groups like SMB, Mid-Market, and Enterprise.
Running cohort analysis for SaaS unit economics involves building cohort-based projections that track ARR, NRR, and gross margin over time. You group customers by acquisition period, then model their churn and expansion dynamics to project revenue trajectories and test sensitivity to pricing changes.
Yes, you can model payback period and NRR for ecommerce and marketplace businesses using per-unit economics frameworks. These models apply to any business with per-unit profitability metrics, supporting cohort dynamics, segment-level LTV/CAC calculations, and scenario testing across different business models.
Sensitivity analyses test how variations in churn, expansion, price changes, and discount rates impact unit economics outcomes. By adjusting these base assumptions in your LTV and CAC models, you can project multiple ARR and gross margin scenarios to guide pricing, GTM, and product decisions.
You need base assumptions for churn, expansion, pricing, and discount rates to build a bottom-up unit economics model. Historical cohort data on ARR and NRR improves projection accuracy, but the framework supports scenario testing with assumed inputs to generate clear outputs for decision-making.
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