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Games Analytics Industry Fourm 2 - Opera Solutions
1.
1© 2012 Opera
Solutions. All rights reserved. Opera Solutions Transforming Raw Big Data into Extraordinary Performance Richard Palmer 9 May 2013 GIAF2
2.
2© 2012 Opera
Solutions. All rights reserved. Introduction to Opera Solutions
3.
3© 2012 Opera
Solutions. All rights reserved. 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
4.
4© 2012 Opera
Solutions. All rights reserved. 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
5.
5© 2012 Opera
Solutions. All rights reserved. Topics for Today BIG Data & Predictive Analytics Video on Demand Case Study On-line Gaming Case Study
6.
6© 2012 Opera
Solutions. All rights reserved. Big Data & Predictive Analytics
7.
7© 2012 Opera
Solutions. All rights reserved. “Have I got a girl for you!” The History of Predictive Analytics Appearance? TypeofRelationship? Arbitrary structure imposed on the data to make it solvable
8.
8© 2012 Opera
Solutions. All rights reserved. What will the consumer rent next? If the consumer’s last rentals were: Netflix Prize - A Watershed Event
9.
9© 2012 Opera
Solutions. All rights reserved. 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
10.
10© 2012 Opera
Solutions. All rights reserved. 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
11.
11© 2012 Opera
Solutions. All rights reserved. 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
12.
12© 2012 Opera
Solutions. All rights reserved. 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
13.
13© 2012 Opera
Solutions. All rights reserved. Video on Demand Case Study
14.
14© 2012 Opera
Solutions. All rights reserved. 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
15.
15© 2012 Opera
Solutions. All rights reserved. 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
16.
16© 2012 Opera
Solutions. All rights reserved. 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
17.
17© 2012 Opera
Solutions. All rights reserved. 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
18.
18© 2012 Opera
Solutions. All rights reserved. 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
19.
19© 2012 Opera
Solutions. All rights reserved. Online Gaming Case Study
20.
20© 2012 Opera
Solutions. All rights reserved. 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
21.
21© 2012 Opera
Solutions. All rights reserved. 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
22.
22© 2012 Opera
Solutions. All rights reserved. Questions