MAKING SENSE OF
BIG DATA
HELLO! NICE TO MEET YOU!
Shingly Lee
Amy Martin
Workshop Coordinator
Brand Lover
Eclectic foodie on Instagram:
@shinglysylee
Workshop Coordinator
Fascinated by the latest and greatest
Buyer @ Walmart (starting 2015)
Travel Enthusiast
…AND A SPECIAL GUEST!
DR. CEREN KOLSARICI
• Assistant Professor, COMM 433 Marketing Analytics
• Ian R. Friendly Fellow of Marketing
• New Researcher Achievement Award
• Distinguished as the American Marketing
Association-Sheth Consortium Fellow
• Ph.D. in Marketing, Mcgill University
1
The Big Data
Movement
2
What’s the Big
Deal?
3
How Can
Analytics Help?
4
Case Studies ft.
Dr. Ceren
Kolsarici
UNLOCKING
BIG
DATA
Why
NOW?
CONSUMER LANDSCAPE IS CHANGING
1. THE
CONSUMER LANDSCAPE IS CHANGING
Making Sense of Big Data 7
2. MARKET
CONSUMER LANDSCAPE IS CHANGING
3. THE EMPOWERED
CONSUMER
@JeffWeiner
Making Sense of Big Data 11
MEDIA FACEOFF
TRADITIONAL
MEDIA FACEOFF
NEW MEDIA
Making Sense of Big Data 13
DATA EXPLOSION
90%
OF THE DATA IN
THE WORLD TODAY
HAS BEEN CREATED
IN THE LAST
TWO YEARS
Making Sense of Big Data 17
Making Sense of Big Data 21
Making Sense of Big Data 23
Making Sense of Big Data 25
BOTTOM LINE IMPACT
(Well Above Average) (Average marketing program) (Below Average) (Well Below Average)
Marketing Performance Critical Troubling Average Pleasing Amazing
Marketing Share Growth Precipitous Significant Modest Increase Dramatic
Decline Decline Decline Increase
New Product Success Rate 0% 5% 10% 25% 40%+
Advertising ROI Negative 0% 1 - 4% 5 - 10% 20%
Promotional Programs Disaster Un - profitable Marginally Unprofitable Profitable Very Profitable
Customer Satisfaction 0 - 59% 60 - 69% 70 - 79% 80 - 89% 90 - 95% Customer Retention/Loyalty 0 - 44% 45 - 59% 60 - 74% 75 - 89% 90 - 94% (Above Average) Zone of Exceptional Marketing Zone of Death Wish Marketing
GENERATING THE PERFECT CONTENT: NETFLIX
• In Q3 of 2011, Netflix announced
continuing its DVD service under the name
Qwikster and a price increase for its
streaming service.
• To keep and grow its subscriber base,
repair the damaged brand name Netflix
turned to a brand-new strategy: creating its
original content.
• Create the “perfect” TV-series.
• Netflix did not need a fortune teller to see
how successful their new show would be.
They knew! Even before anyone shouted
"action."
• But how?
• No one in the industry knows
more about the audiences
than Netflix
– 33 million subscribers
worldwide
– 30 million plays a day: when
you pause, rewind and
fast-forward, star ratings,
searches, time and day,
devices
• Tags for the movies and TV
shows: Genre, cast, award
nomination, length,
production studio, etc.
Traditionally
Match available shows with
audiences based on
preferences (i.e.
Recommendations)
New
Design original content
(Why not?)
DATA
• No primary data collection:
audience testing, market
research, focus groups, etc.
• By being a great "data detective"
– Let the data
predict
what people
would like based on their past
viewing habits
– Mine extremely rich data to
generate actionable insights
• But how?
– Looking for correlated patterns
of behaviour across individuals
HOW DID NETFLIX KNOW THE RECIPE FOR
SUCCESS?
• House of Cards was the first original series by Netflix
– Political drama based on BBC mini-series of the same name
– Costs $100m for two seasons
•
The show quickly became critics’ and audiences’ favorite
– First season: 13 episodes, February 1, 2013, 9 Emmy and 14 GG
nominations
– Second season: 13 episodes, February 14, 2014, 13 Emmy nominations
– IMDB rating: 9.1
INSIGHTS
OVERALL IMPACT
• It would only make sense to
invest if audience likes it and
Netflix can get new
subscribers (i.e. 500K new
subscriptions in two years to
break even)
What is the marketing decision driven by the data analytics?
Create a political thriller and involve Kevin Spacey and David Fincher U.S. series version of the old British miniseries House of
What does the analysis reveal?
E.g. A sizeable segment of subscribers who watch political thrillers also watch Kevin Spacey movies and David Fincher movies. They also watch an old British miniseries called House of Cards.
What do you do with the data to help you answer your question?
E.g. Netflix looks for patterns in viewing habits, correlation analysis.
What data do you have available (or can obtain) to help you answer this question?
E.g. Netflix has data on viewing habits of subscribers and what their portfolios of shows viewed look like.
What is the business/marketing question you want to answer?
E.g. What type of show should Netflix invest in developing that will appeal to subscribers and attract new ones?
THE KEY DRIVERS FOR SUCCESS FOR PRODUCTS WITH SEQUENTIAL
DISTRIBUTION: ADVERTISING AND WOM SYNERGIES
"Dynamic Effectiveness of Advertising and Word-of-Mouth in the Sequential Distribution of Short Life Cycle Products," Norris I. Bruce, Natasha Zhang, Ceren Kolsarici, Journal of Marketing Research, 2012,49(4), 469-86
• Windowing or sequential
distribution is most
prevalent for new products
with short life cycles.
– Motion pictures, book
publishing, fashion, music
and art
• Revenues from sequential
distribution are crucial for
firms:
– Hollywood studios, on
average, spend $71M to
produce and $36M to market
a film
– A movie on average only
makes $47M theatrical
• The two key drivers for movie revenues
are advertising and third-party reviews
(e.g. critics’ reviews and WOM)
• How do ad effectiveness and WOM
effectiveness fluctuate between box
office to rental stages of a movie?
• How do they differ and interact?
• How do they vary across different
movies?
• Is there a better way to allocate
advertising resources?
WHEN AND HOW MUCH TO ADVERTISE FOR
A MOVIE?
• For both theatrical and video stages
– Revenues ($)
– Advertising Spending ($)
– IMDB ratings (Volume & Valence)
– Critics’ reviews (Valence)
• Movie specific variables
– Genre, Runtime, Big Studio, Oscar
Nominations, Sequel, Budget etc.
DATA
Firms can use advertising and WOM strategically to support and elevate
each other's effectiveness at different stages of the PLC.
Theater Stage
Video Stage
DESCRIPTIVE ANALYTICS
•
Diminishing ad effectiveness over time
•
Advertising wear-in possible, particularly for new products
•
Advertising and WOM exert independent yet interdependent influences on
demand
•
Higher ad elasticities early on in PLC replaced by higher WOM elasticities later
PREDICTIVE ANALYTICS: AD AND WOM
SYNERGY IN ACTION
• More efficient media
planning would generate
greater profits
– 30%(70%) of films could designate
lower-than-observed (higher-lower-than-observed)
theatrical ad budget
– 17% (73%) of films could designate
lower-than-observed (higher-lower-than-observed)
video ad budget
• Recommended pre-
versus
post release budget split.
– Critics' favourites (allot more to pre-)
– Action (allot less to pre-)
Up to 15% increase in log
revenues with the new
allocation pattern.
INSIGHTS
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