Leveraging Analytics and. User Segmentation

Transcription

1 Freemium Economics Leveraging Analytics and User Segmentation to Drive Revenue Eric Benjamin Seufert ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Morgan Kaufmann is an imprint of Elsevier

2 Acknowledgments Author Biography Introduction xiii xv xvii CHARTER i The Freemium Business Model 1 Commerce at a price point of $0 1 Components of the freemium business model 2 Scale 3 Insight 5 Monetization 7 Optimization 9 Freemium economic«10 Price elasticity of demand 11 Price discrimination 14 Pareto efficiency 17 Freemium product case studies 20 Skype 20 Spotify 22 Candy Crush Saga 25 CHARTER 2 Analytics and Freemium Products 29 Insight as the foundation of freemium product development 29 Analytics 29 What is analytics? 30 What is big data? 33 Designing an analytics platform for freemium product development 34 Storing data for a freemium product 37 Reporting data for a freemium product 39 Data-driven design 41 The minimum viable product 43 Data-driven design versus data-prejudiced design 44 CHARTER 3 Quantitative Methods for Product Management 47 Data analysis 47 Descriptive statistics 47 Exploratory data analysis 51 ix

3 x Contents Probability distributions 52 Basic data visuals 55 Confidence intervals 59 A/B testing 63 What is an A/B test? 63 Designing an A/B test 65 Interpreting A/B test results 66 Regression analysis 69 What is regression? 69 The regression model in product development 70 Linear regression 72 Logistic regression 75 User segmentation 76 Behavioral data 77 Demographic data 79 Predicting user segments 80 CHARTER 4 Freemium Metrics 83 Instrumenting freemium products 83 Minimum viable metrics 83 Working with metrics in the freemium model 84 Retention 86 The retention profile 86 Retention metrics 88 Tracking retention 90 Monetization 91 Conversion 92 Revenue metrics 94 Engagement 97 The onboarding funnel 98 Session metrics 100 Net promoter score 101 Virality ; 102 Virality hooks 103 The k-factor 104 Using metrics in the freemium model 106 Metrics and the Organization 107 Dashboard design 108 Ad-hoc analysis 110 Minimum viable metrics as a source of revenue 111

4 xi CHARTER 5 Lifetime Customer Value 115 Lifetime customer value 115 Lifetime customer value and the freemium model 115 Making use of LTV 117 LTV in, LTV out 118 Retention versus acquisition 121 Discounting LTV 122 Calculating lifetime customer value 123 The spreadsheet approach 124 Constructing the retention profile in a spreadsheet 125 Calculating user lifetime from the retention profile curve 129 Calculating revenue with trailing ARPDAU 130 Structuring the LTV worksheet and deriving LTV 133 ARPDAU versus projected individual revenue 134 The analytics method 135 The Pareto/NBD method 137 The regression method 138 Implementing an analytics model 140 Auditing an analytics model 141 Making decisions with LTV 142 LTV and marketing 143 LTV and product development 145 LTV and organizational priority 146 The politics of LTV 147 CHARTER 6 Freemium Monetization 149 The continuous monetization curve 149 Choice, preference, and spending 149 What is the continuous monetization curve? 150 Engineering a freemium product catalogue 152 Freemium and non-paying users 154 Revenue-based user segments 156 Data products in the freemium model 157 Recommendation engines 158 The dynamic product catalogue 160 Productizing analytics 161 Downstream marketing 162 Reengagement marketing 163 Promotional targeting 164 Measuring downstream marketing 165

5 CHARTER 7 Virality 169 The viral product 169 What is virality? 169 Calculating virality 170 The effects of compounding virality 172 Virality and retention 175 Signal versus noise 177 Quantified virality 178 Viral periods 179 Saturation 185 Building the viral model 189 Engineering virality 192 The viral product 192 Viral networks 194 Increasing viral invitations 195 Increasing viral conversions 197 CHARTER 8 GrOWth 199 Facilitating a large user base 199 Strategie growth 199 Demographic targeting and Saturation 200 Optimizing the onboarding process 201 Optimizing product copy 203 Faid user acquisition 204 Misconceptions about paid user acquisition 205 Advertising exchanges 207 Demand-side platforms 209 Supply-side platforms 210 Paid search 211 Virality and user acquisition 215 Mobile user acquisition 216 Mobile user acquisition and the law of large numbers 216 Mobile user acquisition and adverse selection 218 Alternative user acquisition 219 Cross-promotion, virality, and discovery 219 Search engine optimization 221 Traditional media 223 References 227 Index 229