connecting your customers – building successful mobile games through the power of aws analytics

1

GDC 2015 – Amazon Developer Day

Connecting With Your Players Using AWS Analytics

Nate WigerPrincipal, Gaming Solutions

@nateware

[email protected]

AWS Gaming Solutions

Projected Mobile App Revenue

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

2011 2012 2013 2014 2015 2016 2017

Ads

IAP

Paid

Source:

Gartner


More Screens = More Revenue


Analytics at Supercell

“You cannot predict success.

You will only find out after releasing it.”

- Ilkka Paananen, CEO, Supercell


Analyze What?

Emotions

• Enjoying game

• Engaged

• Like/dislike new content

• Stuck on a level

• Bored

• Giving up

Behaviors

• Hours played day/week

• Number of sessions/day

• Level progression

• Friend invites/referrals

• Response to mobile push

• Money spent/week


Winning at Free to Play

• Phase 1: Collect Data

• Phase 2: Analyze

• Phase 3: Profit


Example: Level Progression (One Metric)

0

2

4

6

8

10

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10

Tries / Level

# of Tries


Example: Level Progression (Two Metrics)

0

10

20

30

40

50

60

0

2

4

6

8

10

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10

Tries / Level

% Highest Level # of Tries


Wooga – Player Attrition


Wooga – Level Analysis


Solution – Level of the Week


Events & Metrics

• Event = Moment in Time– Login/quit

– Game start/end

– Level up

– In-app purchase

• Metrics = What to Measure– KISS

– Numbers

– Booleans

– Strings (Enums)

• Context = Always Include– User

– Action

– Session (context-dependent)

– Timestamp in ISO8601 2014-03-16T16:28:26


Off The Shelf Analytics

• Easy To Integrate

• Pre-Baked Reports

• Limited Flexibility

• Retention Windows

• Data Ownership


Ok, A Real Business Plan

Ingest Store Process Analyze


Ok, A Real Business Plan

Ingest

• S3 PUT

• Analytics SDK

• Kinesis

Store

• S3

• DynamoDB

• HDFS

Process

• Redshift

• EMR (Hadoop)

• Spark

Analyze

• Tableau

• Pentaho

• Jaspersoft


• Collect Events on Device

• Periodically Store in S3

• Process Data into Redshift

• Analyze with GUI Visualization Tool

Start Simple

2015-03-03,nateware,e4df,login2015-03-03,nateware,e4df,gamestart2015-03-03,nateware,e4df,gameend2015-03-03,nateware,a88c,login2015-03-03,nateware,a88c,friendlist2015-03-03,nateware,a88c,gamestart

Profit!


Redshift at a Glance

10 GigE

(HPC)

Ingestion

Backup

Restore

JDBC/ODBC

• Leader Node– SQL endpoint

– Stores metadata

– Coordinates query execution

• Compute Nodes– Columnar table storage

– Load, backup, restore via Amazon S3

– Parallel load from Amazon DynamoDB

• Single node version available


Tableau + Redshift


Plumbing

① Create S3 bucket ("mygame-analytics-events")

② Request a security token for your mobile app: http://docs.aws.amazon.com/STS/latest/UsingSTS/Welcome.html

③ Upload data from your users' devices

④ Run a scheduled copy to Redshift

⑤ Setup Tableau to access Redshift

⑥ Go to the Beach

http://docs.aws.amazon.com/STS/latest/UsingSTS/Welcome.html


Loading Redshift from S3

copy eventsfrom 's3://mygame-analytics-events'credentials 'aws_access_key_id=<access-key-id>; aws_secret_access_key=<secret-access-key>'delimiter=',';

Scheduled Redshift Load using Data Pipeline:

http://aws.amazon.com/articles/1143507459230804

Search Slideshare for "BDT303" – re:Invent 2014

http://aws.amazon.com/articles/1143507459230804


http://tinyurl.com/k2elyhb

http://tinyurl.com/k2elyhb


• Collect Server Logs

• Periodically Send to S3

• Process into Redshift

• External Analytics Data Too

More Data Sources

EC2

External

Analytics


Logrotate to S3

/var/log/apache2/*.log {sharedscriptspostrotatesudo /usr/sbin/apache2ctl gracefuls3cmd sync /var/log/*.gz s3://mygame-logs/

endscript}

Blog Entry on Log Rotation:

http://www.dowdandassociates.com/blog/content/howto-rotate-logs-to-s3/

ELB Access Logs:

http://docs.aws.amazon.com/ElasticLoadBalancing/latest/DeveloperGuide/acce

ss-log-collection.html

http://www.dowdandassociates.com/blog/content/howto-rotate-logs-to-s3/

http://docs.aws.amazon.com/ElasticLoadBalancing/latest/DeveloperGuide/access-log-collection.html


• Different File Formats

• Device vs Apache vs CDN

• Cleanup with EMR Job

• Output to Clean Bucket

• Load into Redshift

Dealing With Messy Data

EC2


Redshift vs Elastic MapReduce

Redshift

• Columnar DB

• Familiar SQL

• Structured Data

• Batch Load

• Faster to Query

• Long-term Storage

Elastic MapReduce

• Hadoop

• Custom Java / Python

• Unstructured Data

• Streaming Loop

• Scales > PB's

• Transient


• Game Servers Uses DynamoDB

• Directly Export to Redshift

• Or Stage to S3

Integrate With Your Game Database

EC2

DynamoDB


AWS Mobile Analytics SDK


Amazon

Mobile

AnalyticsEngagement +

Monetization

Active Users

Sessions

In-app Revenue

Lifetime Value (LTV)

Retention

Post-install Retention Funnel

Behavior

Custom Events


Integrating Mobile Analytics is Simple

Initialize the MobileAnalyticsManager.

For Custom Events activity reports, add events.

Handle OnResume() and OnPause()

private static MobileAnalyticsManager analytics;

analytics = MobileAnalyticsManager.getOrCreateInstance(

this.getApplicationContext(),"yourCompany.yourAppId",

Regions .US_EAST_1, config, cognitoProvider );

EventClient eventClient = analytics.getEventClient();

MobileAnalyticsEvent visitEvent = eventClient.createEvent("level1Complete");

eventClient.recordEvent(visitEvent);

analytics.getSessionClient().resumeSession();

analytics.getSessionClient().pauseSession();

analytics.getEventClient().submitEvents();


Key Business Metrics

(with one line of code)

1. Monthly Active Users

(MAU)

2. Daily Active Users

(DAU)

3. New Users,

4. Daily Sessions,

5. Sticky Factor,

6. 1-Day Retention,

7. Avg. Revenue per DAU,

8. Daily Paying Users,

9. Avg. Paying DAU


Amazon

Mobile

Analytics Post-install Retention Funnel


# of Likes /Shares

Player Abort

Rates per Level

# of Plays per Hour

In-app Item Popularity

Track Custom Events


Custom Eventspublic void onLevelComplete(String levelName, String difficulty, double timeToComplete, int playerState) {

// Create a Level Complete event with some attributes and metrics

AnalyticsEvent levelCompleteEvent =

analytics.getEventClient().createEvent("LevelComplete")

.withAttribute("LevelName", levelName)

.withAttribute("Difficulty", difficulty)

.withAttribute("EndState", playerState)

.withMetric("TimeToComplete", timeToComplete);

//Record the Level Complete event

analytics.getEventClient().recordEvent(levelCompleteEvent);

}


Amazon

Mobile

Analytics

Fast

Event data is processed in < 60 minutes

Affordable

Free 100 MM Events per Month

$1 per MM Events beyond the free tier

Private

You own your data

We do not use your data, or share with 3rd parties


• Collect Events w/ Mobile Analytics SDK

• Auto-Export to S3

• Process Data into Redshift


Mobile Analytics Auto-Export to S3


• Collect Events w/ Mobile Analytics SDK

• Auto-Export Directly to Redshift


Start Simple


Setup Analytics Auto-Export to Redshift


Setup Analytics Auto-Export to Redshift

http://amzn.to/mobile-redshift-export

http://amzn.to/mobile-redshift-export


Exporting to Amazon Redshift

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


SQL with Redshift – Mobile Analytics Data


SQL – User Segmentation


DAU and MAU in SQL

select date,count(*) over (partition by

date_trunc('day', date) order by date) as dau,

count(*) over (partition by date_trunc('month', date) order by date) as mau

from user_sessionsorder by date;


Measure Retention: Repeated Plays

create view events_by_user_by_month as

select user_id,

date_trunc('month', event_date)

as month_active,

count(*) as total_events

from events

group by user_id, month_active;


First-Pass Retention – Individual Plays

05

10152025303540

# Play Sessions / Month

nateware

Lazyd0g

AK187

3strikes


Cohorts & Cambria

• Enables calculating relative metrics

• Group users by a common attribute– Month game installed

– Demographics

• Run analysis by cohort– Join with metrics

• Use Redshift as it's SQL– Example of where SQL is a good fit


Creating Cohorts with Redshift

create view cohort_by_first_event_date as

select user_id,

date_trunc('month', min(event_date))

as first_month

from events

group by user_id;

http://snowplowanalytics.com/analytics/customer-

analytics/cohort-analysis.html

http://snowplowanalytics.com/analytics/customer-analytics/cohort-analysis.html


Retention by Cohort – Join Events

0

5

10

15

20

25

Week 1 Week 2 Week 3 Week 5 Week 6 Week 7

# Sessions / Week

2013-11

2013-12

2014-01

2014-02

2014-03

2014-04


Moar Cohorts

• Define multiple cohorts– By activity, time, demographics

– As many as you like

• Change cohort depending on analysis

• Join same metrics with different cohorts– Retention by date

– Retention by demographic

– Retention by average plays/month quartile


5-9 notifications retain 1.5x better than 10-14 notifications

Forza: Push Notifications vs Retention


Data Collection

• Mobile Devices

• Game Servers

• Ad Networks

• Size of event ~ 1 KB

• 500M+ events/day

• 500G+/day & growing

• JSON format

Source of Data Data Size & Growth


Redshift Schema

• Every game has its own database

• Each game event = table (e.g., battle_fight, iap)

• 40-50 tables per DB schema

• All game titles ~ 1000 tables in DW


{"player_id":"323726381807586881","player_level":169,"device":"iPhone 5","version":"iOS 7.1.2”,"platfrom":"ios","client_build":"440”,"db":”mw_dw_ios","table":"player_login","uuid":"1414566719-rsl3hvhu7o","time_created":"2014-10-29 00:11:59”}

{"fight_time":"2014-10-29 00:11:59","attacker_id":"413142642356365377”, "attacker_clan_size":500,"attacker_level":270,"db":"mw_dw_ios","table":"battle_fight","uuid":"1414566719-p0oogk0bep","time_created":"2014-10-29 00:11:59"}

• PHP/Python Web Services

• Client side analytics SDK

• JSON data format

Data Stream


Analytics Store Architecture

S3

S3

Consumer

Game DB

Game

ServersKinesis

Amazon

Redshift

Elastic

MapReduce

DSV

JSON


Writing to a Kinesis StreamPOST / HTTP/1.1Host: kinesis.<region>.<domain>x-amz-Date: <Date>Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=content-type;date;host;user-agent;x-amz-date;x-amz-target;x-amzn-requestid, Signature=<Signature>User-Agent: <UserAgentString>Content-Type: application/x-amz-json-1.1Content-Length: <PayloadSizeBytes>Connection: Keep-AliveX-Amz-Target: Kinesis_20131202.PutRecord

{"StreamName": "exampleStreamName","Data": "XzxkYXRhPl8x","PartitionKey": "partitionKey"

}


Amazon

S3

Spark SQL

JDBC Server

SQL

Dashboard

Redshift

Consumer

EMR

Consumer

Game DB

Game

ServersKinesis

DynamoDB

DSV

JSON


Saving Money… EC2 Spot Instances

You bid your own price for instances

c3.large

$0.105/hour

$0.0161/hour*

m3.2xlarge

$0.560/hour

$0.0641/hour*

On-Demand

Spot

On-Demand

Spot

* Price in US-East @ Feb 26, 2014 – 9:40AM PST

85%

off88%

off


Example Price History


Auto Scaling: Let AWS Optimize For You

as-create-launch-config spotlc-5cents

--image-id ami-e565ba8c

--instance-type m1.small

--spot-price “0.05”

. . .

as-create-auto-scaling-group spotasg

--launch-configuration spotlc-5cents

--availability-zones “us-east-1a,us-east-1b”

--max-size 16

--min-size 1

--desiredcapacity 3


EMR Cluster With Spot

aws emr create-cluster --name "Multiple task instance group cluster" --ami-version 3.2.3 \

--ec2-attributes AvailabilityZone=eu-west-1b \

--instance-groups InstanceGroupType=MASTER,Name="Master",InstanceCount=1,InstanceType=m3.xlarge

\

InstanceGroupType=CORE,Name="Core",InstanceCount=4,InstanceType=m3.xlarge \

InstanceGroupType=TASK,Name="Task1",InstanceCount=25,InstanceType=m3.xlarge,BidPrice=0.28 \

InstanceGroupType=TASK,Name="Task2",InstanceCount=10,InstanceType=r3.xlarge,BidPrice=0.28


• S3 as Data Lake

• Redshift for Analysis

• Collect Everything

• Iterate on Reports

Bring It All Back

EC2

External

Analytics


Amazon

S3

Spark SQL

JDBC Server

SQL

Dashboard

Redshift

Consumer

EMR

Consumer

Game DB

Game

ServersKinesis

DynamoDB

DSV

JSON

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Nate WigerPrincipal, Gaming Solutions

@nateware

[email protected]

connecting your customers – building successful mobile games through the power of aws analytics

Technology

aws gaming solutionsoff

aws gaming solutionshttp

gam gaming solutionsredshift

highest level

s3 process data

data phase

scheduled redshift load

amazon s3 parallel load