Analyze Users & Optimize Product Retention

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Analyze Users & Optimize Product Retention

This course is part of multiple programs.

Instructor: Professionals in the Industry

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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

Shareable certificate

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Assessments

5 assignments¹

AI Graded see disclaimer

Taught in English

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Earn a shareable career certificate

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 videoTotal 4 minutes

Why Customer Segmentation Drives Product Success4 minutes

2 readingsTotal 20 minutes

K-Means Clustering Fundamentals for Customer Analytics10 minutes
RFM Analysis Framework: Strategic Customer Segmentation for Product Analytics10 minutes

2 assignmentsTotal 33 minutes

Build Customer Segments Using K-Means Clustering18 minutes
User Clustering and RFM Analysis Knowledge Check15 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 videosTotal 13 minutes

Why Retention Methodology Choice Impacts Business Strategy5 minutes
Calculating and Interpreting Different Retention Metrics8 minutes

1 readingTotal 10 minutes

Rolling-Cohort vs N-Day Retention: Core Concepts10 minutes

3 assignmentsTotal 38 minutes

User Segmentation and Retention Analysis Mastery15 minutes
Compare Retention Methods and Create Technical Recommendations 20 minutes
Retention Analysis Methodology Knowledge Check3 minutes

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Instructor

Professionals in the Industry

454 Courses78,248 learners

Offered by

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