Transforming Raw Big Data into Extraordinary Performance

1. 1© 2012 Opera Solutions. All rights reserved.
Opera Solutions
Transforming Raw Big Data into
Extraordinary Performance
Richard Palmer
9 May 2013
GIAF2

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Introduction to Opera
Solutions

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Opera Solutions
Employees: 700 - Consulting Data Science Software
Global Network: North America Europe Asia
Key Focus:
Data Scientists 230+
Focus: Man + Machine
Consumer Marketing, Finance, Gaming &
Leisure, Government

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What We Believe
Human Behaviour can be expressed mathematically
Machine intelligence is critical – but must be married to human insight
It’s not the data, it’s the signals in the data
Most organisations’ infrastructure, operations and scientific skills haven’t
caught up to Big Data

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Topics for Today
 BIG Data & Predictive Analytics
 Video on Demand Case Study
 On-line Gaming Case Study

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Big Data & Predictive
Analytics

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“Have I got a girl
for you!”
The History of Predictive Analytics
Appearance?
TypeofRelationship?
Arbitrary structure
imposed on the data
to make it solvable

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What will the consumer
rent next?
If the consumer’s last rentals were:
Netflix Prize - A Watershed Event

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Complications
 We know nothing about the customer and there are 7 million of them
 Most customers have only rented a small number of Movies – less than 1%
on average out of 17 thousand available movies
 Customers are biased in their feedback and may only rate movies they love
or hate

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Breakthrough techniques –Factor Modelling
users (customers)
items(movies)
The objective of the Factor Models is to compute predictions for these
unseen events – and thus output a probability that a user will purchase/add
to wishlist/rate etc. a particular item they have not yet experienced.
Use all the data and
let the structure
inside the data
reveal itself

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Approach unleashing a step-change in performance for our clients
N E T F L I X P R I Z E C O M P E T I T I O N
V I D E O - O N - D E M A N D
O P E R A T O R
• 400% increase in take-rate
over in-house solution (based
on most-popular title)
• Out-performed 7 competing
solutions in a bake-off by a
wide margin
Take-Rate (%)
4X
C R E D I T C A R D
A S S O C I A T I O N
• 20% lift in merchant behaviour
model accuracy vs. in-house
models (in-house models
developed by internal risk
teams using Merchant
Category Code as input
variable to models)
Accuracy of Merchant
Behaviour Model (%)
0 2,000 4,000 6,000 8,000 10,000 12,000
Dimensionality
38.1%
43.1%
45.4%
Merchant Code
Aggregated
Merchant ID
Merchant +
Merchant Code
Dataset Complexity
In-house
Model
Opera
Model
20% Lift in
Accuracy
10
45
In-House Opera

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Customer factors
Item factors
Customer bias
Item
bias
Global
mean
Customer factors
Item factors
Customer
bias
Item
bias
Global
mean
Customer factors
Item factors
Customer
bias
Item
bias
Global
mean
VS
if
N
f
cficci
VUbbR 1
ˆ
if
N
f
cficci
VUbbR 1
ˆ
if
N
f
cficci
VUbbR 1
ˆ
Summary – human behaviors can be expressed mathematically and when
combined with machine intelligence data can provide valuable Signals

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Video on Demand
Case Study

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Viewers
Converting BIG Data to small data – the ‘dimensional reduction journey’
RAW CONSUMPTION DATA SPARSE MATIRX DIMENSIONAL REDUCTION
2.3 Billion rows of data
Content & Time of View
Factor
models, clusterin
g and neural
networks to
extract Natural
Clusters from
consumption
data
1 0 0 1 1
0 0 0 0 0
0 0 1 0 0
0 1 0 0 0
0 1 0 0 0
11 Million Viewers
14K of VoD Items
~0.13% filled
Very high dimensional
representation
9 Natural Clusters of customer
behaviour (Archetype)
Time
Signal
Generator

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1 2 3 4 5 6 7 8 9
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
Series xxx
V I E W E R A R C H E T Y P E S
Colour code:
High
Medium
Low
Archetypes display highly distinctive content viewing behaviourSERIES

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And have distinct demographic signatures
Archetype 4 Archetype 5
Have
Children
ABC1 House
wives
16-34
Male
Have
Children
ABC1 House
wives
16-34
Male

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1. Drive higher cost
per impression via
more accurate
targeting
2. Drive higher
advertiser share-of-
wallet via superior
campaign ROI
3. Drive higher
Viewer satisfaction
via more relevant
Ads
Application – targeting of advertising based on predicted demographic
segment
• Content information;
Viewer Archetype,
series participation,
flicking, etc.
• Time information,
including intensity,
Time of View
Archetype, intensity
etc.
Viewing Behaviour
• Type and number of
hardware devices
• Type of software (i.e.,
browsers used)
• ISP information
Device Information
• Location
• Travel behaviour
• Regional
demographics (e.g.,
income, education)
Locality Information
• Page hits
• Referral sites and
sections
• On site search terms
• Off site search terms
Browsing Behaviour
Micro-targeted Ads
Micro-targeting signals made available through Opera’s Signal HubTM (examples)
Increased Value
VoD Viewer Micro-
targeting
1,500 signals in total

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Example Demographic Prediction: Female Age 16 to 34
AUC KS
Test 0.85 0.58
# Unregistered
Targetable Vod Views
Estimated Recall
Lots Very good
%PositivesCaptured
% Negatives Captured
Females behaving
like females
(positives)
Males behaving
like females
(negatives)
Cut-off at 80%
correctly classified

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Online Gaming
Case Study

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Driving customer loyalty through in improving player recommendations
• 3-4% increase in
average weekly
gameplay
• 50% increase is average
rating
50k games
15 million users

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Simple standalone solution
Product/
Content
Manager
Players
Opera
Recommendation
Solution
Operation types:
New User
Registration
Consume
Recommendation
Rate
Game
Other
Actions
Add Game
Remove
Game
Review
Statistics
Backup
System
Install
Updates
Input
Output
Statistic
Control
Other
IT
Manager
Opera
Solutions

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Questions