Analyze Users & Optimize Product Retention

This course is part of multiple programs.

Instructor: Hurix Digital

Access provided by IT Education Association

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

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

Details to know

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Assessments

5 assignments¹

AI Graded see disclaimer

Taught in English

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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.