TV Marketing and big data: cat and dog or thick as thieves? Krzysztof Osiewalski & Cyril Papadacci,...
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Transcript of TV Marketing and big data: cat and dog or thick as thieves? Krzysztof Osiewalski & Cyril Papadacci,...
© King.com Ltd 2015 – Commercially confidential
TV Marketing and big data:TV Marketing and big data:TV Marketing and big data:TV Marketing and big data:
Cat and Dog
or
Thick as Thieves?
Krzysztof Osiewalski
Senior Econometrician / Data Scientist
Marketing Data Science
Cyril Papadacci
Senior Econometrician / Data Scientist
Marketing Data Science
© King.com Ltd 2015 – Commercially confidential
We make great gamesAbout King
• More than 185 fun titles played in over 200 countries and regions around the world.
• 364 million average monthly unique users (Q1 2015). • Studios in Stockholm, London,
Barcelona, Bucharest, Malmo,
Berlin, Singapore and Seattle.
• Offices in San Francisco,
Malta, Tokyo, Seoul and
Shanghai.
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© King.com Ltd 2015 – Commercially confidential
Some stats and factsAbout King
1400Employees (approx)
Four global franchises:
Founded in 2003, studios in Stockholm, London, Barcelona, Malmo, Bucharest, Berlin, Singapore and Seattle.
Global leader in cross-platform casual
games.Candy Crush Pet Rescue Farm Heroes Bubble Witch
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© King.com Ltd 2015 – Commercially confidential
Some stats and facts
1.000.000.000.000
Millions of players around the world.
Approximately 1.6 billion average daily game plays across our games
in Q1 ‘15
More than 1 trillion levels played!
• Games popular across platforms, and can be played anywhere, anytime on most devices.
• 3 games in the top 10 grossing games on the Apple App Store and on Google Play in the US in Q1 ‘15 .
• Our Saga games allow players to switch platform without losing their progress.
About King
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© King.com Ltd 2015 – Commercially confidential
The evolution of KingAbout King
• Founded in 2003
• Originally games were only available through our site and portals including AOL and Yahoo!
Online skill Social Mobile
• Launched first game on
Facebook in Q2 2011• Launched first game on
mobile H2 2012
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© King.com Ltd 2015 – Commercially confidential
Big Data
The definition of Big Data [Gartner, 2001]
“Big data” is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight enhanced insight enhanced insight enhanced insight and decision makingdecision makingdecision makingdecision making.
The big data V’s:• VolumeVolumeVolumeVolume quickly evolving (TB in 2012 � PB today)• VarietyVarietyVarietyVariety numbers, text, language, sounds, coordinates, etc…• VelocityVelocityVelocityVelocity needs fast collection and processing
• VeracityVeracityVeracityVeracity inconsistencies over time, missing data, etc.• ValueValueValueValue capturing business opportunities, optimizing ...
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© King.com Ltd 2015 – Commercially confidential
A bit about KingBig Data
Big Data serves business needs
• Infrastructure and analytics should ultimately help answer business questions
• Provide decision makers with a better real-time understanding of the business at a very granular level [importance of visualizationvisualizationvisualizationvisualization]
• Help measure the impact of actions in order to have a more data-driven strategy
• Efficient use of data fosters a more agile approach to driving the business
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© King.com Ltd 2015 – Commercially confidential
Our data is… growingData at King
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Exceeds Qlikview
capacity
Exceeds Infobright
capacity
© King.com Ltd 2015 – Commercially confidential
A bit about KingData at King
Our data describes the activity of our player base
• The information that we gather typically looks like this:
• User #123456
• Installed Game G on date t0 from source S
• Played R1, R2,… game rounds on dates t1, t2,…
• Passed level L1 on date t1
• Failed level L2 3 times on date t2
• Sent m1, m2,…. messages on dates t1, t2,…
• Did n1, n2,… transactions on date t1, t2,…
Acquisition
Engagement/Retention
Skills/level difficulty
Virality
Monetization
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© King.com Ltd 2015 – Commercially confidential
A bit about KingWhat do we use our data for?
Several marketing areas benefit from the rich data that we have
• Performance Performance Performance Performance MarketingMarketingMarketingMarketing� CLV/RPI modelling� Marketing campaign performance analysis (e.g. digital, TV)
Typical business questions:• What was the impact on all KPIs of the last digital ad campaign?• How much are these players likely to spend within next year?• Up to what threshold can we pay for acquiring this group of users?
• How do we measure the ROI of a TV campaignHow do we measure the ROI of a TV campaignHow do we measure the ROI of a TV campaignHow do we measure the ROI of a TV campaign????
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© King.com Ltd 2015 – Commercially confidential
A bit about KingWhat do we use our data for?
Different channels, different challenges
TVTVTVTV(and outdoor, press, radio...)
DigitalDigitalDigitalDigitaladvertisingadvertisingadvertisingadvertising
Mass audienceAnonymous User level
Full history of conversion funnel at individual levelFull history of conversion funnel at individual levelFull history of conversion funnel at individual levelFull history of conversion funnel at individual level Measurement only on aggregated metricsMeasurement only on aggregated metricsMeasurement only on aggregated metricsMeasurement only on aggregated metrics
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© King.com Ltd 2015 – Commercially confidential
User acquisition – econometric ‘top down’ approach
Baseline basket and TV group installs follow similar patterns in a period without TV spend
Clear difference between baseline basketand TV group installs in period of TV marketing spend
What do we use our data for?
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© King.com Ltd 2015 – Commercially confidential
364 m
We have more players than the entire US populationWe have more players than the entire US populationWe have more players than the entire US populationWe have more players than the entire US population
320 m
King in numbersA reminder about King
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© King.com Ltd 2015 – Commercially confidential
TV has effects on multiple player segmentsOther components
User acquisitionUser acquisitionUser acquisitionUser acquisition
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© King.com Ltd 2015 – Commercially confidential
User acquisitionUser acquisitionUser acquisitionUser acquisition
ReactivationReactivationReactivationReactivation Active usersActive usersActive usersActive users
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TV has effects on multiple player segmentsOther components
© King.com Ltd 2015 – Commercially confidential
Reactivation measurement
Continuously active playersContinuously active playersContinuously active playersContinuously active players
W08 W09W07W06
TV campaignTV campaignTV campaignTV campaign
New installsNew installsNew installsNew installsReturners/ReactivatedReturners/ReactivatedReturners/ReactivatedReturners/Reactivated
W10W05
EconometricsEconometricsEconometricsEconometricsAnonymous uAnonymous uAnonymous uAnonymous user level analysisser level analysisser level analysisser level analysis
Big Data Aggregate time series
Other components
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© King.com Ltd 2015 – Commercially confidential
A potentially heavy taskAnalysis across the board
24 TV countries
7 games
TV activity since beginning 2013
Hundreds of campaigns
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© King.com Ltd 2015 – Commercially confidential
“Compression” with JSON
UserID week game_rounds
123456789 2014W51 13
123456789 2015W03 9
123456789 2015W04 1
123456789 2015W08 123
123456789 2015W09 444
123456789 2015W10 12
123456789 2015W11 13
UserID game_rounds_blob
123456789 {"2014W51":13,"2015W08":123,"2015W09":444,"2015W04":1,"2015W03":9,"2015W10":12,"2015W11":13}
Need for speed
Raw data model
“Compressed” version – unique UserID record
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© King.com Ltd 2015 – Commercially confidential
Complexifying can sometimes make things simplerNeed for speed
Unique UserID JSON tableOptimally structured & partitioned
Heavy map stage:• 9TB• Useless (in this case) partitioning
Heavy map stage
Heavy reduce stage
Fixed one-off cost
Light map stage• Use of partitions• Filter at map stage
Trivial reduce stage
Small va
riable cost
MMMM
RRRR
MMMM MMMM
RRRR RRRR
MMMM
RRRR
MMMM MMMM
RRRR RRRR
MMMM MMMM MMMM
RRRR
Heavy reduce stage:• Requires proper distributing
• Requires scripting in memory over each user, given all his data
• Potentially unbalanced due to multiple filters
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© King.com Ltd 2015 – Commercially confidential
Complexifying can sometimes make things simplerNeed for speed
Unique UserID JSON tableOptimally structured & partitioned
Heavy map stage:• 9TB• Useless (in this case) partitioning
Heavy map stage
Heavy reduce stage
Fixed one-off cost
Light map stage• Use of partitions• Filter at map stage
Trivial reduce stage
Small va
riable cost
MMMM
RRRR
MMMM MMMM
RRRR RRRR
MMMM
RRRR
MMMM MMMM
RRRR RRRR
MMMM MMMM MMMM
RRRR
Heavy reduce stage:• Requires proper distributing
• Requires scripting in memory over each user, given all his data
• Potentially unbalanced due to multiple filters
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100’s x 100’s x 100’s x 100’s x slowslowslowslow
1x slow1x slow1x slow1x slow
100’s x 100’s x 100’s x 100’s x fastfastfastfast
© King.com Ltd 2015 – Commercially confidential
Value of JSON for us
� Increasing fixed cost, but reducing the variable one� Full analysis of a single campaign reduced to less than 10 minutes
Value of flexibility for the business� Crucial when need for testing different scenarios� Another level of confidence in the achieved ROI�Opening new horizons: halo effect, cross market effect
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