Freemium Economics
Leveraging Analytics and User Segmentation to Drive Revenue
Description
Key Features
- Learn how to apply data science and big data principles in freemium product design and development to maximize conversion, boost retention, and deliver revenue
- Gain a broad introduction to the conceptual economic pillars of freemium and a complete understanding of the unique approaches needed to acquire users and convert them from free to paying customers
- Get practical tips and analytical guidance to successfully implement the freemium model
- Understand the metrics and infrastructure required to measure the success of a freemium product and improve it post-launch
- Includes a detailed explanation of the lifetime customer value (LCV) calculation and step-by-step instructions for implementing key performance indicators in a simple, universally-accessible tool like Excel
Readership
Analysts, user acquisition and product managers, mid- and senior-level managers in Freemium businesses
Table of Contents
Chapter One: What is the Freemium Model?
1.1: The fundamentals of Freemium
1.2: What freemium isn't
1.3: Freemium Case Study: Skype
1.4: Freemium Case Study: Clash of Clans
1.5: Freemium Case Study: Spotify
Chapter Two: Analytics as the Heart of Freemium
2.1: Analytics is the foundation of the freemium model
2.1.1: Scale and the 5%
2.1.2: What is Analytics
2.1.3: What is Big Data
2.6: Designing an analytics platform for Freemium
2.6.1: Collecting Data in freemium
2.6.2: Storing data in freemium
2.6.3: Reporting data in freemium
2.2: Iterative product design
2.2.1: Data-driven development
2.2.2: The minimum viable product
2.2.3: Data-driven design vs. Data-prejudiced design
Chapter Three: Quantitative methods for product management
3.1: Data Analysis
3.1.1: Descriptive Statistics
3.1.2: Exploratory Data Analysis
3.1.3: Probability Distributions
3.2: A/B Testing
3.2.1: What is an A/B test
3.2.2: Designing an A/B test
3.2.3: Interpreting A/B test results
3.3: Regression Analysis
3.3.1: What is regression?
3.3.4 Regression in Product Development
3.3.2: Linear regression
3.3.3:Logistic regression
3.4: User Segmentation
3.4.1: Behavioral Data
3.4.2: Demographic Data
3.4.3: Predictions
Chapter Four: Freemium Metrics
4.1: Minimum Viable Metrics
4.1.1: Minimum Viable Metrics
4.1.2: Who works with data?
4.2: Retention
4.2.1: The retention profile
4.2.2: Retention Metrics
4.2.3: Tracking Retention
4.3: Monetization
4.3.1: Conversion
4.3.2: Revenue
4.4: Engagement
4.4.1: Session metrics
4.4.2: Net Promoter Score
4.5: Virality
4.5.1: Viral Hooks
4.5.2: Virality Timeline
4.5.3: K-Factor
4.6: Reporting
4.6.1: Reporting centralization
4.6.2: Dashboard Design
4.6.3: Ad-hoc analysis
4.7: Growth
4.7.1: Paid Users
4.7.2: Organic Users
4.7.3: Churn
4.7: Analytics as a source of revenue
Chapter Five: Lifetime Customer Value
Chapter Six: Monetization and Downstream Marketing
Chapter Seven: Virality
Chapter Eight: Optimized User Acquisition
Product details
- No. of pages: 254
- Language: English
- Copyright: © Morgan Kaufmann 2014
- Published: December 27, 2013
- Imprint: Morgan Kaufmann
- eBook ISBN: 9780124166981
- Paperback ISBN: 9780124166905
About the Author
Eric Seufert
Eric received an undergraduate degree in Finance from the University of Texas at Austin and an MA in Economics from University College London, where he was an Erasmus Mundus scholar. Eric joined Skype immediately out of graduate school and subsequently held marketing and strategy roles at Digital Chocolate and Wooga, where he is now the Head of Marketing. Prior to graduate school, Eric worked at uShip, the Austin-based marketplace for shipping services.
Originally from Texas, Eric currently lives in Berlin. In his spare time, Eric enjoys traveling and writing.