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(MBL309) Analyze Mobile App Data and Build Predictive Applications

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"Amazon Mobile Analytics helps you track key trends such as active users, revenue, retention, and behavioral insights. In this session, you will learn how to make the most of your mobile app data for better business decisions. We will cover out-of-the-box dashboards, how to conduct custom analysis and visualize data, and how to build predictive applications to influence user engagement and monetization.

Who Should Attend: Mobile app and game developers, product managers, data analysts, and business intelligence engineers"

Published in: Technology

(MBL309) Analyze Mobile App Data and Build Predictive Applications

  1. 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. October 2015 MBL309 Analyze Mobile App Data and Build Predictive Applications Sandeep Atluri, AWS Data Scientist
  2. 2. What to Expect from the Session • Collect, analyze, and visualize mobile app data with Amazon Mobile Analytics • Run ad-hoc analysis to gain deeper insights with Amazon QuickSight • Build predictive applications for your mobile app with Amazon Machine Learning
  3. 3. “If you can’t measure it, you can’t improve it” -Lord Kelvin
  4. 4. Retrospective Analyze historical trends to know what's happening in the app Predictive Anticipate user behavior to enhance experience Inquisitive Discover latent user behavior to shape product or marketing decisions Three Types of Data-Driven Decision Making
  5. 5. How many users use the app and how often? What are key user behaviors in the app? Your Mobile App How to predict user behavior and use those predictions to enhance their experience ? In the Context of a Mobile App
  6. 6. Three Types of Data Driven Decision Making Retrospective Analyze historical trends to know what's happening in the app Predictive Anticipate user behavior to enhance experience Inquisitive Discover latent user behavior to shape product or marketing decisions
  7. 7. Let’s just say we have built a music appMusic App
  8. 8. Let’s just say we have built a music app What are some of the questions that would help us in understanding what’s happening in the app? Music App
  9. 9. Engagement How many users use the app daily to listen music ? How many times users open the app to listen music in a day? How many new users have been acquired to the app ? Monetization How many paying users does the app have ? How much does a average paying user pay ? Retention How many people returned to listen music in the first 7 days after they have installed the app ? Behavioral How many users shared or liked a particular artist ? Few Key Questions to Understand Trends in the App
  10. 10. Amazon Mobile Analytics Collect, visualize, and export app usage data
  11. 11. Amazon Mobile Analytics Collect, visualize, and export app usage data
  12. 12. Amazon Mobile Analytics “Collect, visualize and export your app usage data at scale” Accurate results Amazon Mobile Analytics processes ALL data received to provide accurate analytics on your app use. We never provide reports based on sampled data even if you are in the free tier. Your app, your data Your app data is safe with us. We don’t report on or share your data with third parties. Focus on metrics that matter. Usage reports available within 60 minutes of receiving data from an app Fast
  13. 13. Amazon Mobile Analytics Engagement + Monetization Active Users Sessions In-app Revenue Lifetime Value (LTV) Retention Post-install Retention Funnel Behavior Custom Events
  14. 14. Amazon Mobile Analytics Get started by visiting: aws.amazon.com/mobileanalytics/
  15. 15. Retrospective Analyze historical trends to know what's happening in the app Predictive Anticipate user behavior to enhance experience Inquisitive Discover latent user behavior to shape product or marketing decisions Three Types of Data Driven Decision Making
  16. 16. Going beyond standard metrics will give you more insight in to user behavior
  17. 17. How does usage pattern vary for users with different demographic profiles? Who are the most engaged users and what are their usage patterns ? How does user population distribute across countries and platform ? How much time does it takes for a user to convert to a paying user ? Music App Few Questions That Will Help You Understand Your Users Better
  18. 18. Auto Export to Amazon Redshift
  19. 19. Simple & intuitive Integrate with existing data models Automatically collect common attributes Schema for Your App’s Event Data
  20. 20. Now Easy to Query and Visualize Your Mobile App
  21. 21. Now Easy to Query and Visualize Your Mobile App QuickSight New
  22. 22. Introducing Amazon QuickSight
  23. 23. DEMO
  24. 24. Integration with BI Tools is Very Easy
  25. 25. Amazon QuickSight Sign up for the preview: aws.amazon.com/quicksight
  26. 26. Retrospective Analyze historical trends to know what's happening in the app Predictive Anticipate user behavior to enhance experience Inquisitive Discover latent user behavior to shape product or marketing decisions Three Types of Data Driven Decision Making
  27. 27. Predicting user behavior helps in delivering personalized experiences for users
  28. 28. Let’s say we have been observing high user churn in the music app. Now, we want to identify these users in advance so that we could reach out to users before they leave the app Predictive Application by Example Music App
  29. 29. Let’s say we have been observing high user churn in the music app. Now, we want to identify these users in advance so that we could reach out to users before they leave the app How could you identify users who have high probability to churn away from the app? Music App Predictive Application by Example
  30. 30. SELECT e.unique_id, Count(distinct session_id) FROM events e WHERE event_type = ‘_session.start’ HAVING e.date> GETDATE() - 30 You can start by looking at usage patterns of all users in the last 30 days One Way To Do is…
  31. 31. SELECT e.unique_id, Count(distinct session_id) FROM events e WHERE event_type = ‘_session.start’ AND date_part (dow,e.date ) in (6,7) HAVING e.date> GETDATE() - 30 But usage pattern changes on weekends. You can edit the query to filter for weekends only One Way To Do is…
  32. 32. SELECT e.unique_id, Count(distinct session_id) FROM events e WHERE event_type = ‘_session.start’ AND date_part (dow,e.date ) in (6,7) HAVING e.date> GETDATE() - 60 Pattern is not clear. You can go back in time to get a more clear pattern One Way To Do is…
  33. 33. SELECT e.unique_id, Count(distinct session_id), e.music_genre , e.subscription_type , e.locale FROM events e WHERE event_type = ‘_session.start’ AND date_part (dow,e.date ) in (6,7) HAVING e.date> GETDATE() - 60 You want to learn not only from usage data but from custom behavior in the app One Way To Do is…
  34. 34. SELECT e.unique_id, Count(distinct session_id), e.music_genre , e.subscription_type , e.locale FROM events e WHERE event_type = ‘_session.start’ AND date_part (dow,e.date ) in (6,7) HAVING e.date> GETDATE() - 120 ….and again One Way To Do is…
  35. 35. SELECT e.unique_id, Count(distinct session_id) , e.music_genre , e.subscription_type , e.locale FROM events e WHERE event_type = ‘_session.start’ AND date_part (dow,e.date ) in (6,7) HAVING e.date> GETDATE() - 120 Use machine learning technology to learn business rules from your data
  36. 36. Machine learning automatically finds patterns in your data and uses them to make predictions Better Way To Do it is… Users with High probability to churn Users with Low probability to churn
  37. 37. Machine learning automatically finds patterns in your data and uses them to make predictions Your data + Machine Learning Predictive applications in the app Better Way To Do it is… Users with High probability to churn Users with Low probability to churn
  38. 38. Amazon Mobile Analytics Amazon Machine Learning Leverage Mobile App Data to Build Predictive Applications Using Amazon ML
  39. 39. Train model Evaluate and optimize Retrieve predictions Building Predictive Applications with Amazon ML 1 2 3
  40. 40. Build Amazon ML Models with a Few Clicks
  41. 41. Explore Model Quality
  42. 42. DEMO
  43. 43. Amazon Machine Learning Get started by visiting: aws.amazon.com/machine-learning/
  44. 44. Predict users with low probability to purchase in the app and send discount coupon via in-app notification Predict users with high probability to churn from the app and send push them notification to re-engage Identify users with high probability to share the app and reach out to them to do the same Recommend relevant content to users based on similar user’s behavioral patterns A Few Examples of Leveraging Mobile App Data with Machine Learning
  45. 45. Amazon Mobile Analytics Amazon Redshift App events InsightsStrategies Predictions Mobile app developer Amazon Machine Learning + Now Build Predictive Applications Using Your Mobile App Data Easily Your Mobile App QuickSight +
  46. 46. Getting Started: Add Mobile Analytics to your app 1. Visit the AWS Mobile Hub • Add “App Analytics” to your project • Download your iOS or Android Source Code 2. Visit the Amazon Mobile Analytics console • View out-of-the-box dashboards • Turn on Auto-Export to get raw events in S3 and Redshift
  47. 47. Thank you! Questions? Reach us at: amazon-mobile-analytics@amazon.com
  48. 48. Remember to complete your evaluations!

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