Freemium economics : leveraging analytics and user segmentation to drive revenue / Eric Benjamin Seufert.

Enhanced descriptions from Syndetics:

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.

Table of contents provided by Syndetics

• Acknowledgments (p. xiii)
• Author Biography (p. xv)
• Introduction (p. xvii)
• Chapter 1 The Freemium Business Model (p. 1)
• Commerce at a price point of $0 (p. 1)
• Components of the freemium business model (p. 2)
• Scale (p. 3)
• Insight (p. 5)
• Monetization (p. 7)
• Optimization (p. 9)
• Freemium economics (p. 10)
• Price elasticity of demand (p. 11)
• Price discrimination (p. 14)
• Pareto efficiency (p. 17)
• Freemium product case studies (p. 20)
• Skype (p. 20)
• Spotify (p. 22)
• Candy Crush Saga (p. 25)
• Chapter 2 Analytics and Freemium Products (p. 29)
• Insight as the foundation of freemium product development (p. 29)
• Analytics (p. 29)
• What is analytics? (p. 30)
• What is big data? (p. 33)
• Designing an analytics platform for freemium product development (p. 34)
• Storing data for a freemium product (p. 37)
• Reporting data for a freemium product (p. 39)
• Data-driven design (p. 41)
• The minimum viable product (p. 43)
• Data-driven design versus data-prejudiced design (p. 44)
• Chapter 3 Quantitative Methods for Product Management (p. 47)
• Data analysis (p. 47)
• Descriptive statistics (p. 47)
• Exploratory data analysis (p. 51)
• Probability distributions (p. 52)
• Basic data visuals (p. 55)
• Confidence intervals (p. 59)
• A/B testing (p. 63)
• What is an A/B test? (p. 63)
• Designing an A/B test (p. 65)
• Interpreting A/B test results (p. 66)
• Regression analysis (p. 69)
• What is regression? (p. 69)
• The regression model in product development (p. 70)
• Linear regression (p. 72)
• Logistic regression (p. 75)
• User segmentation (p. 76)
• Behavioral data (p. 77)
• Demographic data (p. 79)
• Predicting user segments (p. 80)
• Chapter 4 Freemium Metrics (p. 83)
• Instrumenting freemium products (p. 83)
• Minimum viable metrics (p. 83)
• Working with metrics in the freemium model (p. 84)
• Retention (p. 86)
• The retention profile (p. 86)
• Retention metrics (p. 88)
• Tracking retention (p. 90)
• Monetization (p. 91)
• Conversion (p. 92)
• Revenue metrics (p. 94)
• Engagement (p. 97)
• The onboarding funnel (p. 98)
• Session metrics (p. 100)
• Net promoter score (p. 101)
• Virality (p. 102)
• Virality hooks (p. 103)
• The k-factor (p. 104)
• Using metrics in the freemium model (p. 106)
• Metrics and the organization (p. 107)
• Dashboard design (p. 108)
• Ad-hoc analysis (p. 110)
• Minimum viable metrics as a source of revenue (p. 111)
• Chapter 5 Lifetime Customer Value (p. 115)
• Lifetime customer value (p. 115)
• Lifetime customer value and the freemium model (p. 115)
• Making use of LTV (p. 117)
• LTV in, LTV out (p. 118)
• Retention versus acquisition (p. 121)
• Discounting LTV (p. 122)
• Calculating lifetime customer value (p. 123)
• The spreadsheet approach (p. 124)
• Constructing the retention profile in a spreadsheet (p. 125)
• Calculating user lifetime from the retention profile curve (p. 129)
• Calculating revenue with trailing ARPDAU (p. 130)
• Structuring the LTV worksheet and deriving LTV (p. 133)
• ARPDAU versus projected individual revenue (p. 134)
• The analytics method (p. 135)
• The Pareto/NBD method (p. 137)
• The regression method (p. 138)
• Implementing an analytics model (p. 140)
• Auditing an analytics model (p. 141)
• Making decisions with LTV (p. 142)
• LTV and marketing (p. 143)
• LTV and product development (p. 145)
• LTV and organizational priority (p. 146)
• The politics of LTV (p. 147)
• Chapter 6 Freemium Monetization (p. 149)
• The continuous monetization curve (p. 149)
• Choice, preference, and spending (p. 149)
• What is the continuous monetization curve? (p. 150)
• Engineering a freemium product catalogue (p. 152)
• Freemium and non-paying users (p. 154)
• Revenue-based user segments (p. 156)
• Data products in the freemium model (p. 157)
• Recommendation engines (p. 158)
• The dynamic product catalogue (p. 160)
• Productizing analytics (p. 161)
• Downstream marketing (p. 162)
• Reengagement marketing (p. 163)
• Promotional targeting (p. 164)
• Measuring downstream marketing (p. 165)
• Chapter 7 Virality (p. 169)
• The viral product (p. 169)
• What is virality? (p. 169)
• Calculating virality (p. 170)
• The effects of compounding virality (p. 172)
• Virality and retention (p. 175)
• Signal versus noise (p. 177)
• Quantified virality (p. 178)
• Viral periods (p. 179)
• Saturation (p. 185)
• Building the viral model (p. 189)
• Engineering virality (p. 192)
• The viral product (p. 192)
• Viral networks (p. 194)
• Increasing viral invitations (p. 195)
• Increasing viral conversions (p. 197)
• Chapter 8 Growth (p. 199)
• Facilitating a large user base (p. 199)
• Strategic growth (p. 199)
• Demographic targeting and saturation (p. 200)
• Optimizing the onboarding process (p. 201)
• Optimizing product copy (p. 203)
• Paid user acquisition (p. 204)
• Misconceptions about paid user acquisition (p. 205)
• Advertising exchanges (p. 207)
• Demand-side platforms (p. 209)
• Supply-side platforms (p. 210)
• Paid search (p. 211)
• Virality and user acquisition (p. 215)
• Mobile user acquisition (p. 216)
• Mobile user acquisition and the law of large numbers (p. 216)
• Mobile user acquisition and adverse selection (p. 218)
• Alternative user acquisition (p. 219)
• Cross-promotion, virality, and discovery (p. 219)
• Search engine optimization (p. 221)
• Traditional media (p. 223)
• References (p. 227)
• Index (p. 229)

Author notes provided by Syndetics

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.