TV Marketing and big data: cat and dog or thick as thieves? Krzysztof Osiewalski & Cyril Papadacci, King

© 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

[email protected]

Cyril Papadacci

Senior Econometrician / Data Scientist

Marketing Data Science

[email protected]


About King

Page 3


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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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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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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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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Some of our gamesAbout King

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Data at King

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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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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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Our data is… growingData at King

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Our data is… growingData at King

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Exceeds Qlikview

capacity

Exceeds Infobright

capacity


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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Data value for King- in marketing

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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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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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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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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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TV has effects on multiple player segmentsOther components

User acquisitionUser acquisitionUser acquisitionUser acquisition

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User acquisitionUser acquisitionUser acquisitionUser acquisition

ReactivationReactivationReactivationReactivation Active usersActive usersActive usersActive users

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TV has effects on multiple player segmentsOther components


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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A potentially heavy taskAnalysis across the board

24 TV countries

7 games

TV activity since beginning 2013

Hundreds of campaigns

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“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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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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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


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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© King.com Ltd 2015 – Commercially confidential Page 28

© King.com Ltd 2015 – Commercially confidential Page 29

Thank you


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TV Marketing and big data: cat and dog or thick as thieves? Krzysztof Osiewalski & Cyril Papadacci, King