Cohort-Based Revenue Intelligence

SaaS revenue isn't linear. It decays, bends, accelerates, plateaus, and collapses in patterns most FP&A teams never quantify. Traditional forecasting treats churn as a single number. Reality behaves like a survival function.
Cohort-Based Revenue Intelligence introduces a new class of FP&A modeling built around survival curves, hazard rates, cohort decay mechanics, and predictive churn analytics, giving SaaS finance teams the ability to understand and forecast revenue at a biological, mathematical, and behavioral level.
James Preston breaks down these advanced statistical systems into practical, CFO-ready tools for SaaS planning, pricing, forecasting, and board reporting. This book shows how to replace outdated retention tables with dynamic, probabilistic revenue engines that evolve with customer behavior.
Inside, you'll learn:
* How survival curves reveal hidden patterns in retention, expansion, and cohort decay
* Hazard-model techniques to predict when customers are most likely to churn
* Cohort-based revenue models that outperform traditional top-down and ARR-based forecasting
* Python & Excel workflows for building retention curves, hazard functions, and renewal probability models
* How to quantify the financial value of onboarding, sales cycle length, contract structure, price changes, and product usage
* Techniques for modeling expansion revenue, net retention, and multi-year cohort shape evolution
* Frameworks for translating survival-driven analytics into board decks, investor reporting, and FP&A strategy
SaaS companies that master cohort dynamics dominate their markets. Those that don't eventually drown in invisible churn. This book gives FP&A teams the systems and analytical depth to see what others miss, and act on it.
In SaaS, revenue survival isn't an outcome. It's a model. Build it, understand it, and your forecasts become unbreakable.

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