Understanding the business context behind data is essential for analytics professionals to deliver impactful insights. By exploring the interconnected factors shaping the organization's operations and objectives, analysts can uncover meaningful patterns and make informed recommendations that drive strategic decision-making. This holistic approach empowers product, marketing, and revenue teams to optimize their strategies and achieve tangible business results.
Product Analytics Techniques
3 resources in this subcategory
Topics in Product Analytics Techniques:
Funnel & Drop-Off Analysis Activation & Time-to-Value Analysis Retention & Cohort Analysis User Journey & Path Analysis Feature Adoption & Depth Metrics Segmentation & Persona Overlays A/B & Multivariate Experimentation In-Product Messaging & Nudge Analytics Churn & Resurrection Drivers Power-User & Habit Loops North-Star Metric Validation Product-Market Fit Signals Predictive & ML User Scoring Instrumentation Governance & Debt Cleanup
Ergest Xheblati May 28, 2025
Techniques for measuring and forecasting the lifetime value of customers, including retention curves, discount rates, cohort LTV, and probabilistic vs. deterministic LTV models. Linking LTV to other key metrics like CAC and LTV:CAC ratio to assess the efficiency of the business model.
Tangi Gouez May 29, 2025
An in-depth look at how to calculate, analyze, and leverage customer lifetime value (LTV) - a critical SaaS metric for understanding the long-term value of customers and optimizing acquisition and retention strategies.
Lukas Oldenburg June 2, 2025