Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Getting Started with AWS Mobile Hub

1,721 views

Published on

Getting Started with AWS Mobile Hub by Paul Maddox, Solution Architect

Published in: Technology
  • Be the first to comment

Getting Started with AWS Mobile Hub

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Getting Started with AWS Mobile Hub Paul Maddox, Solutions Architect October 2016
  2. 2. Who am I? • @paulmaddox – feel free to reach out - Mixture of Dev • Building highly scalable infrastructure since 2002 - Mixture of Dev & Ops. Linux, LAMP, Java, C, Go, Docker etc. • Building mobile applications, APIs and backends since 2012 • Solutions Architect @ AWS focusing on Mobile since 2014
  3. 3. DEVELOP TEST ENGAGE Building quality mobile apps
  4. 4. When developing mobile apps today, you want to focus on ... The great stuff that makes your app unique Not… The heavy lifting needed to manage back-end infrastructure AWS Mobile Services eliminate the heavy lifting
  5. 5. DEVELOP TEST ENGAGE
  6. 6. “Mobile” growing in all directions Published mobile apps continue to grow… …As “mobile” platforms expand to new domains TV Watch Car *Source: App Annie
  7. 7. Apps are also getting more complex …To cloud-connected appsFrom basic client apps… Sign-in/Social Push notifications Usage analytics Cloud storage Crash analytics Ads Attribution analytics Config management Custom back ends A/B testing
  8. 8. AWS Mobile SDKs AWS Mobile Hub Authenticate users Analyze user behavior Store and share media Synchronize data Deliver media Amazon Cognito (Sync) Amazon Cognito (Identity) Amazon S3 Amazon CloudFront Store data Amazon DynamoDB Amazon RDS Track retention Amazon Mobile Analytics Send push notifications Amazon SNS Mobile Push Server-side logic AWS Lambda AWS Device Farm Test your app Build and scale your apps on AWS Amazon Mobile Analytics
  9. 9. “AWS has what we need, but…it’s complex” 1. Which services should I use? 2. How do I connect them? Identity provider SDKs + = Example: Login screen & integration code + SDK
  10. 10. There has to be a better way…
  11. 11. <demo> ... </demo>
  12. 12. High performance at any scale; Cost-effective and efficient No Infrastructure to manage Pay only for what you use: Lambda automatically matches capacity to your request rate. Purchase compute in 100ms increments. Bring Your Own Code Lambda functions: Stateless, trigger-based code execution Run code in a choice of standard languages. Use threads, processes, files, and shell scripts normally. Focus on business logic, not infrastructure. You upload code; AWS Lambda handles everything else. Cloud Logic with AWS Lambda
  13. 13. AWS Lambda Console Develop, test and publish your Lambda functions either by the AWS Management Console, AWS CLI or our SDKs. Or use community frameworks such as serverless.com, gosparta.io and more…
  14. 14. Pricing Example AWS Lambda (our example = $1.80/month): Free tier: 1,000,000 invocations/month $0.20/million thereafter plus $0.00001667 per GB/second of memory Our mobile backend services 10,000,000 requests/month, each request invokes a Lambda function that takes 100ms and uses 128MB of memory. $1.80 per month
  15. 15. <demo> ... </demo>
  16. 16. DEVELOP TEST ENGAGE
  17. 17. Instrumentation UI Automation UI Automator Your app Improve the quality of your apps by testing against real devices in the AWS cloud Automated testing on AWS Device Farm (native, hybrid, web) XCTest XCTest UI
  18. 18. Select a device View historical sessionsInteract with the device Introducing Device Farm: Remote access
  19. 19. DEVELOP TEST ENGAGE
  20. 20. “If you can’t measure it, you can’t improve it” -Lord Kelvin
  21. 21. Scalable and generous free tier Focus on metrics that matter. Usage reports available within 60 minutes of receiving data from an app. Fast Scale to billions of events per day from millions of users. Own your data Simply and cost-effectively collect and analyze your application usage data Data collected are not shared, aggregated, or reused. Amazon Mobile Analytics
  22. 22. Daily/monthly active users Sessions Sticky factor In-app revenue Lifetime value (LTV) Retention …. and more (9 predefined metrics with one line of code)
  23. 23. Fast, flexible, global messaging to any device or endpoint Global and fast at high scale Send messages to any device or endpoint Support for multiple platforms or frameworks Amazon Simple Notification Service
  24. 24. Worldwide Delivery of Amazon SNS Messages via SMS
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. Amazon Mobile Analytics Collect, visualize, and export app usage data
  29. 29. Amazon Mobile Analytics Collect, visualize, and export app usage data
  30. 30. <demo> ... </demo>
  31. 31. 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
  32. 32. Going beyond standard metrics will give you more insight in to user behavior
  33. 33. 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
  34. 34. Auto Export to Amazon Redshift
  35. 35. Simple & intuitive Integrate with existing data models Automatically collect common attributes Schema for Your App’s Event Data
  36. 36. Now Easy to Query and Visualize Your Mobile App
  37. 37. Now Easy to Query and Visualize Your Mobile App QuickSight New
  38. 38. Integration with BI Tools is Very Easy
  39. 39. 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
  40. 40. Predicting user behavior helps in delivering personalized experiences for users
  41. 41. 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
  42. 42. 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
  43. 43. 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…
  44. 44. 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…
  45. 45. 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…
  46. 46. 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…
  47. 47. 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…
  48. 48. 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
  49. 49. 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
  50. 50. 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
  51. 51. 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
  52. 52. 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 +
  53. 53. Without worrying about infrastructure On real devices in the cloud Track and improve usage and monetization DEVELOP TEST ENGAGE AWS Mobile Services
  54. 54. Without worrying about infrastructure On real devices in the cloud Track and improve usage and monetization DEVELOP TEST ENGAGE AWS Mobile Services ITERATE

×