Product Analytics and Growth Diagnostics

1

Introduction to product analytics

Fundamentals of product analytics: metrics, frameworks, and the product analyst mindset.

2

Retention and cohort analysis in product

Measure and improve retention with cohort analysis, return metrics, and decay curves.

Prerequisites: 1

3

Customer development for product analytics

Integrating quantitative data and qualitative research: interviews, behavioral signals, and decision synthesis.

Prerequisites: 1

4

Cohort analysis and behavioral cohorts

Segment users by behavior, not demographics, with behavioral cohort analysis. From classic retention to transition matrices: how to map the user lifecycle.

Prerequisites: 1

6

UX research and behavioral data

Integrating UX research, analytics, and observation to understand friction, motivation, and perceived value.

Prerequisites: 1

7

NPS and satisfaction: Net Promoter Score without superstitions

Using NPS, CSAT, and CES as diagnostic signals, not as company religion.

Prerequisites: 1

8

A/B testing for product

How to design, read, and govern product experiments without falling into false positives.

Prerequisites: 1

9

Feature prioritization with data

Quantitative frameworks to prioritize which features to build: RICE, Kano, Cost of Delay. How to turn a political backlog into a portfolio of measurable bets.

Prerequisites: 1

10

Product-market fit: signals, metrics, and false positives

How to recognize product-market fit with retention, segmentation, surveys, and economic signals.

Prerequisites: 1

11

Case Study: Complete Product Analysis

Practical Project: Analyze the product end-to-end and present recommendations. From the health dashboard to behavioral segmentation, up to the prioritized roadmap.

Prerequisites: 1

12

Cheat Sheet — Product Analysis

Quick reference for product analytics metrics, frameworks, and patterns. An operational summary to diagnose product health, retention, activation, and roadmap priorities.

Prerequisites: 1