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(MBL303) Get Deeper Insights Using Amazon Mobile Analytics | AWS re:Invent 2014

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Choosing the right mobile analytics solution can help you understand user behavior, engage users, and maximize user lifetime value. After this session, you will understand how you can learn more about your users and their behavior quickly across platforms with just one line of code using Amazon Mobile Analytics.

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(MBL303) Get Deeper Insights Using Amazon Mobile Analytics | AWS re:Invent 2014

  1. 1. November 12, 2014 | Las Vegas, NV Andy Kelm, AWS Mobile PatrikArnesson& VedadBabic, ForzaFootball Chris Keyser, AWS Partner Program
  2. 2. The King of England sent his best men ahead to learn context to plan the battle
  3. 3. Tactics used: Segmented army into three divisions Chose higher elevation around flat land Waited for the enemy to buy time to rest and prepare Built a system of ditches and pits to bring down the enemy cavalry
  4. 4. Source: britishbattles.com militaryhistory.about.com
  5. 5. Amazon Mobile Analytics Collect, visualize, and understand app usage
  6. 6. Amazon Mobile Analytics
  7. 7. Amazon Mobile Analytics
  8. 8. Amazon Mobile Analytics
  9. 9. Amazon Mobile Analytics
  10. 10. Amazon Mobile Analytics
  11. 11. Amazon Cognito Amazon Mobile Analytics Amazon SNS Mobile Push Kinesis DynamoDB S3 SQS SES AWS Global Infrastructure (11 Regions, 51 Edge Locations) Core Services Mobile Optimized Connectors Mobile Optimized Services Your Mobile App, Game or Device App AWS Mobile SDK Compute Storage Networking Analytics Databases Integrated SDK
  12. 12. But, customers tell us they want to dive deeper
  13. 13. Live score Voting Push notifications
  14. 14. +5 000 000 downloads 50% Sticky factor 800 000 000 push notifications / month 2 500 000 monthly active users 100 000 000 sessions / month 1 600 000 000 events / month FORZA FOOTBALL SINCE 2012
  15. 15. OUR EXPERIENCE PRE AMAZON MOBILE ANALYTICS
  16. 16. 0 440,000 880,000 1,320,000 1,760,000 2,200,000 February 2012 October 2012 April 2013 November 2013 CHOOSE THE RIGHT TOOL FROM THE BEGINNING GA (Google Analytics) GA Mobile GA sample data
  17. 17. SCREENING THE MARKET WE TALKED TO ALMOST EVERY ANALYTICS VENDOR IN THE MARKET
  18. 18. THIS WAS IMPORTANT FOR US WHEN CHOOSING ANALYTICS TOOL Pricing Flexible pricing (Pay as you use) Competitive pricing Features Retention Custom events Mobile friendly Data Ownership Export functionality No sampling
  19. 19. AMAZON MOBILE ANALYTICS + REDSHIFT VISUALIZE DATA THAT MATTERS TO YOU
  20. 20. TWO MONTH RETENTION FRANCE VS AVERAGE
  21. 21. VISUALIZATIONS BY TABLEAU
  22. 22. GROWTH PER COUNTRY THE WORLD CUP IS THE MOST INTERESTING FOR THE AMERICANS World Cup
  23. 23. PENETRATION USERS PER CAPITA Potential Same penetration in the UK as in Denmark would equal 4 400 000 users
  24. 24. LEVERAGE ON MISSION TABLEAU VISUALIZATION The users in the nordic countries are the most interested in voting
  25. 25. HOW AND WHY DO USERS USE OUR APP? DATA GUIDE DESIGN DECISIONS
  26. 26. PUSH NOTIFICATIONS 9/10 PEOPLE I HAVE MET SAY THAT THEY REMOVED THE APP BECAUSE OF TOO MANY NOTIFICATIONS.
  27. 27. NOTIFICATIONS >30% set notifications for more than 11 teams. We wanted to see if the amount of notifications could affect the retention. 0-4 notifications 5-9 notifications 10-14 notifications
  28. 28. PUSH NOTIFICATIONS RETENTION PER GROUP 65 DAYS 10-14 notifications retain best
  29. 29. PUSH NOTIFICATIONS RETENTION PER GROUP 148 DAYS 5-9 notifications retain 1.5x better than 10-14 notifications
  30. 30. USER SEGMENTATION SEGMENT USERS BASED ON USAGE
  31. 31. SEGMENTATION BASED ON ACTIVITY WE WANT TO SEE WHICH GROUP USE EACH FUTURE Low activity Medium activity High activity Build features for the medium activity group
  32. 32. APPROACH TO SEGMENTATION WE WANT TO SEE WHICH GROUP USE EACH FUTURE Experiment Define criteria User group size
  33. 33. EXPERIMENT •Sessions? •Session length? •Days active? •Per day? •Per week? •Per month? HOW TO DEFINE ACTIVITY
  34. 34. CRITERIA HOW TO DEFINE CRITERIA Days active (14 days) •# days active over 2-weekperiod •Gaussian distribution? •Let’s try it out!
  35. 35. USER GROUP SIZE •Averaged over 16 time periods •~60% in medium activity •Result •Low activity: 1-4 days •Medium Activity: 5-12 •High activity: 13-14 HOW TO DEFINE SIZE
  36. 36. MAIN MENU WHY WE DELETED A COMPLETELY NEW FEATURE
  37. 37. MAIN MENU Low activity group: 12% Medium activity: 25% High activity: 35% FILTER MATCHES
  38. 38. Old design New design
  39. 39. 10 GigE (HPC) Ingestion Backup Restore JDBC/ODBC
  40. 40. S3 Redshift
  41. 41. 172.16.0.0/20 Public Subnet 172.16.0.0/22 172.16.0.0/20 Local 0.0.0.0/0 IGW Amazon Mobile Analytics EC2
  42. 42. event_timestamp arrival_timestamp application_key account_id app_title event_type unique_id model make platform platform_version locale app_package_name app_version_name sdk_name sdk_version a_level a_promo_code m_score m_quantity
  43. 43. Activity Monitor (custom application) Amazon SNS Cross-platform Mobile Push event_timestamp arrival_timestamp application_key account_id app_title event_type unique_id model make platform platform_version locale app_package_name app_version_name sdk_name sdk_version a_level a_promo_code m_score m_quantity a_endpoint_arn
  44. 44. http://bit.ly/awsevalshttp://aws.amazon.com Follow on Twitter: @AWSforMobile @FootballAddicts

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