User retention is the difference between thriving products and those that fade into obscurity. Yet 73% of product teams struggle to choose the right retention metrics, leading to misguided strategies and missed opportunities.
Evaluate and Explain Retention Curves
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Evaluate and Explain Retention Curves
This course is part of User Retention Analytics Specialization
Instructor: Hurix Digital
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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
Retention metric choice, such as n-day or rolling, shapes how user behavior patterns are interpreted for strategy.
Survival analysis offers a robust statistical method to compare retention across groups and guide optimization.
Significance testing in retention analysis avoids misleading results from random variation and supports sound decisions.
Survival curve visuals simplify complex retention data into insights stakeholders can quickly understand and use.
Skills you'll gain
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Recently updated!
January 2026
Taught in English
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This course is part of the User Retention Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
In this module, learners will explore why choosing the right retention formula is vital for data analysis and product strategy. They’ll learn both formulas through examples, apply them in real-world scenarios, and see a hands-on demo using actual product analytics data.
What's included
2 videos2 readings2 assignments
In this Module, learners will apply Kaplan-Meier survival analysis to evaluate user retention patterns over time, create survival plots in R with statistical testing to compare groups, and integrate analytical findings into experiment readouts that mirror real-world data analyst deliverables for stakeholder communication.
What's included
2 videos2 readings3 assignments
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