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BIG DATA = BIGDISRUPTION?
NUOVE FRONTIERE DELBRAND ENTERTAINMENT
Catarina SismeiroImperial College London
Associate Professor ofMarketing
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OVERVIEW
1. Big data
2. Branded Entertainment and how itcan benefit from big data analytics
3. Applications and examples
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BIG DATA? ATA THAT REQUIRES
NEW SKILLS AND A
W WAY OF LOOKING AT STORAGE ANDPROCESSING
“Big data describes datasets that are so lar
complex, or rapidly changing that they push the v
limits of our analytical capability. It's a subjective te
What seems ‘big’ today may seem modest in a f
years when our analytic capacity has improve
Joel Gurin, author of Open Data Now
What are the typical challenges?! High Volume
!
High Velocity
! Extremely Unstructured
Not Just associated with internet(“Old Data” can be very “big”!)
New data available in a world of “all-things-digital”(e.g., text, image, voice/sound, and vide
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PROJECT
A BOLD IDEA TO
SHOWCASE THEOWER OF BIG DATA ANALYTICS
Black Box… the system does not try to understa
“why” but wants only to predict
When something changes in the market, predicti
can go wary.More than just Big Data… need Intelligent Data
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NOW CLOSED
Thank you for stopping by.
Google Flu Trends andDengue Trends are no
publishing
current estimatesand Dengue fever based on
patterns. […] It is still early d
nowcasting and similar tounderstanding the spread of d
like flu and dengue – we'reto see what comes next. […]
Sincerely,The Google Flu and Dengue
Team.
https://www.google.org/flutabout/
PROJECT A BOLD IDEA TO
SHOWCASE THEOWER OF BIG DATA ANALYTICS
Black Box… the system does not try to understand
“why” but wants only to predict
When something changes in the market, predicti
can go wary.More than just Big Data… need Intelligent Data
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BRANDENTERTAINMENT
GREATOPPORTUNITIES ANDGREAT CHALLENGES
IN TODAY’SETWORKED SOCIETY
Not new… but new opportunities for content creati
and distribution
Power of amplification and serendipity of soc
networks (emerged more as a platform of u
distributed content than one of user generated conte
Great potential and but also significant risks
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HOW CAN BIG DATA ANALYTICS HELP
BRANDENTERTAINMENT?
Start simple and reuse existing tools and analyses (
of the gains from the initial 20% of effort)
Think of the relevant unit of analysis
(e.g., if I make decisions weekly should I have an h
demand analysis?)
Combine methods
! Discovery and Sensing
! Measurement and Prediction
! Targeting and Personalization
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APPLICATIONPREDICTING VIRALITY ANDHELPING DESIGNUSING TEXT ANALYSIS
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TEXT ANALYSIS:SIMPLE EXAMPLE
Calzedonia Ocean Girls SonoStupende!!!”
After stemming and after removing
extremely common and extremelyuncommon words, retained only:
calzedonia, ocean, girls,
Stupende
Encode as TextVector (word_id : count)
(5:1,7:1,142:1,846:5)
Create word dummies to use in models
Message Word 1 Word 2 … Wor
1 1 0 0
2 0 0 1
… …
N 1 1 0
0! Word not present1! Word present
Stemming example: eater, eating ! eat
This post would include one mention tomonitored brand. The same can be done
longer texts (e.g., article) like in the vira
study
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BEYOND VOLUM
SENTIMENT A
MEANING
The apparentincrease in braninterest was no
necessarily positiSentiment Analy
and Content Anal(i.e., what is said
if it is positive o
negative) is ver important
But remember t
ABERCROMBIE AND
FITCH
“FITCH”
GOOGLERENDS DATA
“FITCHTHE
HOMELESS”
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FINDINGS
CONTENT VIRALIT
DEPENDS ON:
!
Position of the content(top of the list)
! Practical utility of post
! EMOTIONS
Content associated witharousal positive (awe, surand humour) or negati
(anger or anxiety) emotimore viral
Content that evokes lo
arousal, or deactivatinemotions (sadness) is less
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ANALYSIS
MEASURING BRANDSTRENGTH (IFEGY ATLAS)
Free or commercisoftware perform t
mining for differelanguages
Data can come fr blogs, online
conversations in somedia, news, ademails, commerci
offers, reportsThink of the potent
What are peopldiscussing afterwatching the lasepisode of RDS
Academy? Discover ideas for
next episode? Discwhat worked and
did not?What content on
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APPLICATIONTARGETING MOBILE USERS WITHMULTIMEDIA MESSAGES: POWEROF TEXT AND IMAGE
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15
lemobile phone company wishes to target
stomers with alternative messagesessages include offers from manytegories and combine TEXT + IMAGE+
RICE
ifficulty in learning using traditional testing
ethods due to the large scale of offers ande reduced time to learn (e.g., most expireuickly not enough testing opportunities)
Solution – Statistical methods (SVR), text mining (sim
word count), and automatic processing ofimages (Textons) to predict performancedifferent users
– Given limited testing possibilities, test onlmost promising offers as per the model
–
Practical issues of deployment that lead tadaptive system (control vs. testing group
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digital representation of images:ixels (a structure that provides a
location”; could be similar to a Cartesianrid though alternatives exist)
olour encoding (e.g., RGB)
m their digital representation we canprocess images using a variety of
rs, extract features (e.g, extract textextract components, determine howuch vegetation, or how much “skin”)nd, considering their overall look atl, classify and group similar images
COMPOSED INTO
LOUR COMPONENTS
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MAGE ALYSIS
:IXELS ANDILTERS
Examples of Filtered Images
There are many different filters
some allow the classification on
overall look and feel of the imaothers try to extract components
the image or identify edges a
ridges
red values are then
ed (e.g., K-means) and
ntroid of each cluster
ecomes a visual
“word” (Texton)
then describe each
e with respect of how
f each “visual word” it
contains
lso possible to extractic image components
Pixel
Each image is made of pixels
(similar to a coordinate, a location)
associated to a specific value (e.g.,
in RGB colour encoding)
For simplicity we are consideringhere a rectangular grid (this is not
the only possibility)
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AUTOMATIC
PROCESSING OFVISUAL
CONTENT
Automatic imageclustering works verywell in finding similar
types of images
The type of imagebeing used can then
be entered as avariable in models of
performance
Or develop system topredict potentialoutcome
many spetools anddifferent w
of
implementimage min
Some ar “supervisand requi
some huinput anothers acomplete
unsupervi
The 80/20
applies alhere(start sim
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1.0
1.1
1.2
1.3
1.4
1.5
10 15 20 25 30 35 40 45 50 55
Price
Price and T
Price, Texto
Text
R e v e n u e
L i f t
System Testing Constraints
RESULTSrmation on images and
t help to significantlyprove performance
e methodology can belied to devise a systemest different messages
ormance metrics couldde clicks, views, shares
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APPLICATIONREAL TIME MARKETING: SENSING AND MONITORING
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“THE STRATEGY A
PRACTICE OFREACTING WITH
IMMEDIACY IN DIGI
CHANNELS TOEXTERNAL EVENT AND TRIGGERS”
OUTCOMES*
81% increased customengagement
73% improved customexperiences
59% increased conversi
rates
Challenges: identifyinopportunities that are
adequate for your target
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REACTION
Sensing can be done
humans or by automasystems
Sensing systems can ron conversation dat
among the targetconsumers of your pro
(specialized) and setautomatic warnings
Creative can be testusing automatic syste
similar to the one
developed for the previexample relying on im
and text
Sensing also useful for
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APPLICATIONWHAT IS THE VALUE OF A SOCIALMEDIA CONVERSATION FOR ABRAND?
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24
lemsearch has shown that companies canfectively stimulate online conversations
rough innovative campaigns, and that thesenversations increase product sales
ow much to invest in these activities dependsn much conversations are worth in terms ofvenue they generate
ow much is a conversation in different social
edia worth for soft drinks brands that sellostly offline?
Solution – Collect data on online conversations and
determine when a brand is mentioned
–
Link conversation data to offline sales dataperiod) and account for the effect of adver(paid media)
– Use statistical models to determine how deis stimulated by conversations
– Simulate the value of online conversations uthe estimated model
– 18 brands, 19 months, 12 US markets
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0
510
15
20
25
30
35
40
Facebook Blog/Discussion
Boards
Twitte
Revenue Change Due to 1,000Additional Conversations Across All
Brands
(in US Dollars)
Facebook Blog/Discussion
Boards
Average MonthlyConversations Mentioning a Brand
(in ‘000s)
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0
10
20
30
40
50
60
Facebook Blog/Discussion Boards Twitter
FOR ALL BRANDS… B AND SMA
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BIG DATA ANALYTICSCAN INDEED HELP
BRANDENTERTAINMENT
Remember…
Start simple and reuse existing tools and analyses (
of the gains from the initial 20% of effort)
Think of the relevant unit of analysis (more than big
it is intelligent data!)
Combine methods
How can we benefit?
!
Discovery and Sensing!
Measurement and Prediction
!
Targeting and Personalization
Consider variety of dataBe creative in data structures and
analyses
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THANK YOU
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New YorkPublic
LibrarySeptember
2011Flash Mob
Partners &
Spade
MarketDisruption
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Warby Parker
Searches
WHAT WORKS?THE CASE OF
WARBY PARKER
Lets do some analysis with Google trends… over tim
the company has grown in terms of interest in the
market. We notice some significant spikes…
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Google Trends: Searches
for Warby Parker in 2011
tor Traffic to Website
rce: Annual Report 2011
INTEGRATIN AN
INTERACTI
NDIFFERECHANNE
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NEW VERSUSTRADITIONAL
Internet is growing fast and new media is gaining
importance
Traditional media is still huge Opportunities exist to
take advantage of their integration and interaction
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Decompose the images i
different “layers” (using fil
Create a Dictionary of Vi
Words or Textons = Centroipixel clusters
Even if the system has ne
seen an image before, it
process it using the filtersthen assign each pixel to
closest cluster and deter
how many of the Textons (
words) we have in the im
Final result is a distributioTextons for each pictur
TRAININGIMAGES
ALLGESVENNEW
ES,
VEREENORE
)
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WHY?
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APPLICATION
SOCIAL MEDIA AND ONLINECONTENT CONSUMPTION:PREDICTION AND SOCIALINFLUENCE
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News Sites andFacebook
PREDICTING WITH SOCIAL
MEDIA
36
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33% improvement in
Page Views prediction
over a model that was
performing alreadyextremely well
Improvement came
mostly from the
information of what
Facebook friends wereviewing at the news site
Predicting
site visit