Data-Driven Product Management

2h 1mIntermediate2024-04-05

Authors

Drew Falkman

Architecting sites for large companies using Java, PHP, and WordPress

Course details

In this course with product strategist Drew Falkman, learn the difference between product and marketing analytics, and why product analytics and the insight they provide are critical to making important product decisions. Drew reviews popular product analytics software tools—including AI solutions—and introduces you to the main tools and processes product managers use to analyze product data. Learn how to maintain a data-driven strategy, recognize key product analytics to measure, how to make data-driven business cases, and explore some advanced analytics considerations for product managers and their organizations.

Learning objectives
Discover best practices for using metrics to drive key product decisions.
Learn how to establish and maintain a data-driven product strategy.
Recognize key product analytics to measure, and the insight they provide.
Learn how to make data-driven business cases.
Learn how to experiment with the data insights you're gathering to improve product/market fit and performance.
Explore advanced analytics considerations for product managers and their organizations.

Skills covered

Product ManagementDecision-MakingBusiness AnalyticsPersonaData ScienceProfessional DevelopmentBusiness Analysis and StrategyLeadership and Management

Concepts

0. Introduction

01 - Data-driven decisions for product success

1. What Is Data-Driven Product Management

02 - The importance of product analytics
03 - How analytics drive product decisions
04 - Introduction to product analytics software

2. Data-Driven Product Strategy

05 - Defining your product analytics strategy
06 - Early product - Finding product-market fit
07 - Product growth and acquisition strategy
08 - Product retention strategy
09 - Feature management and experimentation strategy
10 - KPIs and metrics that matter
11 - Finding your North Star

3. Product Analytics

12 - Introduction to Amplitude and FullStory
13 - Configuration and data collection
14 - Introduction to users and cohort analysis
15 - Segmenting users
16 - Tracking behaviors and metrics
17 - Funnel analysis
18 - Introduction to conversions
19 - Which behaviors drive conversion

4. Experimentation

20 - Experiment planning
21 - Creating an experiment
22 - Analyzing experiment results
23 - Testing new features using feature flagging

5. Making a Business Case

24 - How product data drives business decisions
25 - Choosing your data
26 - Constructing your case

6. Advanced Analytics Concepts

27 - Creating dashboards
28 - AI and predictive analytics
29 - The future - Self-improving products

Conclusion

30 - Next steps