Analyze Users & Optimize Product Retention

Overview

Transform your product analytics capability with advanced user segmentation and retention optimization techniques. This course empowers data analysts to move beyond surface-level metrics to uncover deep behavioral patterns that drive product success. By completing this course, you'll master the application of k-means clustering to identify distinct user segments and gain the analytical sophistication to evaluate rolling-cohort versus N-day retention methods for strategic decision-making. You'll learn to profile power users through RFM analysis, create compelling data narratives for stakeholders, and publish technical guidance that elevates your team's analytical capabilities. This course is unique because it bridges the gap between technical implementation and business impact, teaching you to transform raw user data into actionable product insights that directly influence retention and growth strategies. To be successful in this course, you should have experience with data analytics, basic understanding of machine learning concepts, and familiarity with Python or similar analytical tools.

Syllabus

Module 1: User Clustering Analysis - Foundation

Learners will master k-means clustering implementation using scikit-learn to segment users based on RFM variables, enabling them to create data-driven user profiles that inform product strategy and targeted interventions.

Module 2: Retention Method Evaluation - Core Application

Learners will analyze different retention calculation methodologies, understand their strategic implications, and create technical recommendations that guide data-driven retention strategy decisions in product analytics contexts.