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
您将学到什么
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.
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 User Retention Analytics 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有2个模块
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.
涵盖的内容
2个视频2篇阅读材料2个作业
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.
涵盖的内容
2个视频2篇阅读材料3个作业
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