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Using machine learning and culture to quintuple subscriber conversion

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Using machine learning and culture to quintuple subscriber conversion

  1. 1. Increasing paid conversion 5-fold with machine learning and culture Steven Neubauer, Managing Director NZZ AG @neubauersteven Increasing paid conversion 5-fold with machine learning and culture Steven Neubauer, Managing Director NZZ AG @neubauersteven
  2. 2. Most trusted media brand in Switzerland 150 m CHF in revenues >150.000 paying subscribers
  3. 3. REACH ENGAGEMENT anonymous lead INMA Media Subscriptions Summit, April 19, 2018 SALES INCREASE identified lead REG-GATE PAY-GATE
  4. 4. REACH ENGAGEMENT anonymous lead INMA Media Subscriptions Summit, April 19, 2018 SALES INCREASE identified lead REG-GATE PAY-GATE Conversion on PayGate x5 in last 3 years Personalized content recommend- ations >10.000 registrations per month
  5. 5. PHASE 1 (2012 to 2014): Classic Metered Paywall Conversion rate <0.5%
  6. 6. PHASE 2 (2014 to mid 2017): Developing flexible rules engine Conversion rate 1.2%Dimension Reg- Prompt Order- Prompt Landing page # of articles Reading behavior Prompt ON/OFF Call-to-action Format Personal greeting Time of day Placement of prompt Offering
  7. 7. EXAMPLE: Personal greeting Conversion +25%
  8. 8. PHASE 3 (2nd half 2017): Dynamic Paygate v0.9 – Improving rules engine Conversion rate 2.5% Iterate to improve rules engine Hypotheses-based, data-informed A/B-tests à Dozens landing page templates à Random distribution for 1 month REGISTRATION 200 CHF 20 CHF 10 CHF ORDER Pattern recognition via machine learning Tagging registered users Patterns driving conversion
  9. 9. EXAMPLE: Pattern recognition for pricing preferences
  10. 10. EXAMPLE: Pattern recognition for call-to-action
  11. 11. EXAMPLE: Differentiating the flow Inactive, registered users Email: Read the rest of the month for free # of articles read in previous month Individualized subscription prompt after ... 5 articles 8 articles 11 articles 13 articles ≤5 6-8 9-11 ≥12≤1 No prompt
  12. 12. Phase 4 (2018): Dynamic Paygate v1.0 – Propensity Scoring Conversion rate target >2.5% Propensity score top 20% No Use standard rule set A/B-test Yes Use standard rule set Use specialized rule set, e.g., directly show individualized order prompt with highest conversion probability
  13. 13. EXAMPLE: Propensity scoring with machine learning Propensity score à Time since registration à Time since last visit à # devices used à # newsletters à # active days à ... Random forest
  14. 14. Success criteria § Central, scalable platforms for product and marketing automation § Unified data warehouse § Data and data science resides in marketing § Culture of experimentation and continuous improvement § …
  15. 15. Organizational experiments Self-governed, agile Digital Conversion Team § Purpose § Autonomy § Mastery
  16. 16. What did we do wrong? § Not enough focus on core products § Acceleration trap § We did not fail fast enough § No common understanding of agility and no agile-ready technology architecture § …
  17. 17. Phase 5 (2019): «OmniGate» Anonymous user ORDER home- page show reg-gate classify Engagement? action action Engagement? Send personalized push message send offer via email action ? Paying subscriber RETEN- TION observe Engagement? action propensity to churn? email personalized reading recommendations courtesy call action ORDER Conversion rate target >5%
  18. 18. thank you

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