Freemium Economics

Leveraging Analytics and User Segmentation to Drive Revenue


  • Eric Seufert, Editor, Mobile Dev Memo

Freemium Economics presents a practical, instructive approach to successfully implementing the freemium model into your software products by building analytics into product design from the earliest stages of development.

Your freemium product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don't fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the freemium model. Freemium Economics de-mystifies the freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow product launch.

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Analysts, user acquisition and product managers, mid- and senior-level managers in Freemium businesses.


Book information

  • Published: January 2014
  • ISBN: 978-0-12-416690-5

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