How to manage an intelligent revenue? Explore the mining gold from terabytes of gaming data using Spark & AWS EMR with wappier.

1. Big Data in Action
“Mining gold from terabytes of gaming data
using Spark & AWS EMR”
29th May 2019, Big Data Athens v 4.0
#AutomagicallyIncreasingRevenue
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2. 2
Theodoros Michalareas, wappier CTO, lover of technology
& all things geeky, startup advisor.
Working on state-of-the-art audience management &
marketing automation tools for mobile game publishers &
online businesses.
tm@wappier.com
https://www.linkedin.com/in/theodorosmichalareas/

3. • SaaS platform for Reward
Programs
• Next Best Actions (NBA) offers
Recommendation Engine
• Mobile game discovery &
social networks
Build Loyalty for Games & Businesses
• Global Pricing optimization
• Understand gamers/customers utility
• Real-Time Bundling
Increase Monetization
• Predictive Analytics & ML Models
• Real-Time Consumer Clustering
• In-Depth Insights/Advanced Analytics
• Multivariate Testing & Counterfactual Analysis
Machine Learning / BI Analytics
• Audience Builder: Dynamic Customers Segmentation
• Predictive Consumer Attributes based on Real-Time
Behavior Modeling & Forecasting
• 3D / VR Data Visualization & Manipulation
Visualizations & Audience Management
What We Do – Intelligent Revenue Management

4. Who We Are – 3.5 Years Startup
Web-Based SaaS Platform
(MEAN+cloud+native SDKs/apps
for iOS/Android + Visualizations)
>1m lines of code
Big Data Infrastructure
(spark-based) manage TBs of data
per app/customer
Machine Learning Framework:
modeling, algorithm selection,
evaluation
Technology
Worked with 10s of game
publishers over the last 3.5 years
Experienced in mobile marketing,
building successful loyalty
programs, customer success
Skilled in Visual Design, Software
Engineering, Big Data Engineering,
BI, Data Science, Live
Ops/marketing
Know-How
3.5-years run, expanding
from 40 to 60 headcount by the
end of 2019
70% of the team in
Engineering & Data Science
Presence: US & Europe
Engineering 1st company,
running code ethos
Team

5. Our Mission
We are transforming the way
app developers and
marketers maximize
consumer revenue
by using powerful AI
that goes beyond
marketing automation.
Bring the
sophistication in
UA to Revenue
Management
Provide AI
Technology to
predict and
influence player
behavior
Improve
consumer LTV
outside of core
gameplay
Let you
focus on
building the
best app
out there
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6. 6
Some of our Customers

7. Maximize Gamers’ Lifetime Value
Segment business customers into
dynamic audiences based on
their probability to churn/buy or
their expected LTV
Acquire
Make your UA budget count
Convert
Retain
More users into engaged players
More players into payers
Extent players lifetime
Extent players monetary value
Increase by
50%
Increase by
30%
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Finding Gold – a typical description
of a game publisher request

8. It’s Really Gold
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Game Title Installs at 6
months
Average Lifetime
Value after 6
months
Estimated revenue
at 6 month
Estimated
incremental
revenue with 5%
additional LTV
Rules of survival 55,728,640 $0.25 $13,932,160 $696,608
Knives Out 46,598,787 $1.66 $77,353,986 $3,867,699
Fortnite 16,106,159 $1.13 $18,199,960 $909,998
Clash Royale 113,076,241 $3.11 $351,667,110 $17,583,355
Puzzle Dragon 145,219 $6.78 $984,585 $49,229
Game of war 5,661,266 $5.51 $31,193,576 $1,559,679
Source: SensorTower

9. Macroeconomic
[GDP, Exchange Rate, Unemployment Rate, …]
Microeconomic
[Device Price, Housing/Rents, …]
Game Market Statistics
[Revenue, Growth, …]
Mobile Tech Statistics
[Smartphone Penetration, Android vs iOS, …]
Device Context
[Device, Device Price, Resolution, Platform, …]
Game Context
[Genre, Rating, DAUs/MAUs, F2B %, …]
Temporal Elements
[Seasonality, Trends, …]
Gameplay Context
[Level, Sessions per Day, Events per Day, Purchase History, …}
Other Game History
(same publisher) [Purchase History, Engagement, …]
Game Data
Small
publisher
Average
publisher
< 1GB daily
< 1 y to reach 1TB
< 10GB daily
< 4 m to reach
1TB
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< 50GB daily
< 1 m to reach
1TB
Large
publisher
BUT we need to mine TBs of data per game

10. Assess
• User reacts to
personalized
recommendation
which results in 30-
50% performance
increase
Recommend
• Platform computes
and recommends
user’s next best action:
• Optimal Tactic
• Optimal Channel
• Optimal Timing
Predict
• Expected user LTV is X
• Expected user next best
tactic is Y (Loyalty AI
Engine)
• Expected user next best
price offer is Z (Pricing
AI Engine)
Analyze
• Data are being
analyzed
• User behavior is
modeled:
• retention curve,
propensity to buy,
probability to
churn, LTV, …
Track/Collect
• User enters game
• Data start being
tracked
• ML algorithms start
being trained
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Machine Learning
Models We Use
Finding Gold – Our Mining Methodology
Revenue Regression Models
Micro-Level Non-Linear Demand Estimation Models
Behavioral Economics Adjustments (Psychological Pricing)
Multi-Armed Bandit Optimization

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1. Access more secondary
and tertiary data on which to base
analyses. These data are mainly
structured in nature
2. Data is constantly updating
and streaming
4. Broaden access for non-
experts to Data Engineering
7. Machines are learning,
enabling the results to
contribute to the source data
and inform future decisions
3. New tools are available
that integrate analytics and enable
data exploration and correlations
5. Enable multiple modeling
combinations & iterations
6. Run time wappier platform
enabled tactics
Key
Big Data Infrastructure: ML Workflow & Soft. Stack
Define the
problem
Analyze data,
synthesize
Does data confirm
hypotheses?
Act
Implement,
Measure
Review, Learn
Primary
data
Secondary
data
YesNo
Cycle time reduced to
minutes
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2
3
4
5
6
7
Staging
Area/Data
Lake
Transform
Extract/Load
Develop
multiple
hypotheses

12. 12
Challenges in Mining Gold / Optimizing Games Revenue
Big Data Volumes per
Customer/Volume
A typical game can range
between 10s of MB of
data to 10s of GB of data
daily – data science
teams need a platform to
support big data
volumes
Variable Number of
Projects - Variety
Variable number of
projects /active
publishers from small to
large – data need to
imported/staged and
transformed as soon as
we have access to them
Cost of Exploration –
Velocity/Veracity
Initial exploratory phases
involve process that need
to access/process big
data – Infrastructure
needs to be able to grow
to support different
workloads
Cooperation between
Teams - Agility
Allow different data
science teams to work
on different data sets
based on security and
auditing rules

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Auto Scaling
based on
memory
consumption
AWS
CloudTrail
CloudWatch alarm IAM encrypted
data
permissions role
Python (boto)
bucket with
objects
bucket with
objects
bucket with
objects
OrchestrationBig Data Computing
Data Storage Admin
AWS EMR Data Lake Architecture

14. 14
SPARK Rules

15. Lessons Learned
Unless you have <1TB to
manage cloud-based
solution is a must
AWS EMR has a flexible
deployment model that
can be cost constrained
You need to experiment to
find the best policy to use
EMR autoscaling for your
SPARK workloadI
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PS: We Are Hiring!
https://wappier.com/join-us/
The team is growing!

17. Thank you!
#Automagically Increasing Revenue
info@wappier.com +1 877 WAPPIER
+44 20 7100 1736
www.wappier.com
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