Analyze Users & Optimize Product Retention
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Analyze Users & Optimize Product Retention
This course is part of multiple programs.
Instructor: Hurix Digital
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What you'll learn
Clustering-based user segmentation uncovers behavior patterns for better personalization and targeting.
Retention methods shape insights—choosing the right one ensures accurate product health assessment.
Identifying power users enables better retention, feature design, and lifetime value growth.
Clear communication and documentation turn technical analysis into actionable, team-wide impact.
Skills you'll gain
- Technical Documentation
- Knowledge Transfer
- Performance Measurement
- Product Strategy
- Customer Retention
- Data Presentation
- Data-Driven Decision-Making
- Customer Insights
- Advanced Analytics
- Strategic Decision-Making
- Data Storytelling
- Unsupervised Learning
- Data Analysis
- Customer Analysis
- Machine Learning Algorithms
- Applied Machine Learning
Details to know
January 2026
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There are 2 modules in this course
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.
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.
What's included
1 video2 readings2 assignments
1 video•Total 4 minutes
- Why Customer Segmentation Drives Product Success•4 minutes
2 readings•Total 20 minutes
- K-Means Clustering Fundamentals for Customer Analytics•10 minutes
- RFM Analysis Framework: Strategic Customer Segmentation for Product Analytics•10 minutes
2 assignments•Total 33 minutes
- Build Customer Segments Using K-Means Clustering•18 minutes
- User Clustering and RFM Analysis Knowledge Check•15 minutes
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.
What's included
2 videos1 reading3 assignments
2 videos•Total 13 minutes
- Why Retention Methodology Choice Impacts Business Strategy•5 minutes
- Calculating and Interpreting Different Retention Metrics•8 minutes
1 reading•Total 10 minutes
- Rolling-Cohort vs N-Day Retention: Core Concepts•10 minutes
3 assignments•Total 38 minutes
- User Segmentation and Retention Analysis Mastery•15 minutes
- Compare Retention Methods and Create Technical Recommendations •20 minutes
- Retention Analysis Methodology Knowledge Check•3 minutes
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