Discovery-Oriented Social Media Research

Discovery-Oriented
Social Media Research:
from Descriptive to
Analytic
Deqiang Zou
School of Management, Fudan University
November 2, 2011

Why We Need to Know Research

• From a professor? After all, we can get
 Best practice from leading-edge business experts
 Latest observations and insights from marketing research or
consulting firms, and
• Professors always have some theories
 Unfortunately, I do not have any theories particularly for luxury
goods
• If no theories, maybe you can expand my horizon
 By showing me what I don’t know

2
© Copy rights reserved, Zou Deqiang

Political Science

Greg Miller (2011), “Social
Scientists Wade into the
Tweet Stream,” Science, 333
(6051), 1814‐1815.
3

The Global Mood: Psychologist

4
© Copy rights reserved, Zou Deqiang Miller (2011)

Discovery Orientation

• This is not my job
 Transmit the knowledge
• I can share something with you
 A discovery oriented, critical, and generalizable mindset, so that
you’d be a
 Qualified consumer or even producer of social media research
• The key
 A transformation: from descriptive to analytic

5

Descriptive: Military Budget

6
http://www.guardian.co.uk/news/datablog/2010/apr/01/information‐is‐beautiful‐military‐spending

Analytic: Who’s the Big Spender

Descriptive: Who has more soldiers?

8

11
GroupM and CIC (2011), “The Voice of Luxury: Social Media and Luxury Brands in China,” GroupM
Knowledge ‐ CIC White Paper on Luxury, August.

So What?

• Buzz volume vs. Sales volume?
• NSR vs. Brand attitude in the market place?
• We need marketing research to support marketing
decision making
 If X, then Y
 Straightforward?
 No, marketing is losing its clout. We cannot be over-optimistic
 “Marketing has lost its seat at the (boardroom) table.” (from a
CMO)

V. Kumar and Denish Shah (2009), “Expanding the Role of Marketing: From Customer
Equity to Market Capitalization,” Journal of Marketing, 73 (6), 119‐136. 12

The Cruel Reality

• The CMO is currently the most frequently fired C-level
executive, with an average tenure of less than 24
months (Welch 2004)
• A research based on a multi-industry sample of 167
firms finds that the CMO presence in top management
teams has almost no impact on firm performance (Nath
and Mahajan 2008)
• There’s a perceived lack of marketing accountability,
which has undermined marketing’s credibility, threatened
marketing’s standing in the firm, and even threatened its
existence as a distinct capability within the firm

13
© Copy rights reserved, Zou Deqiang Kumar and Shah (2009)

Establish the Accountable Relation

• Possible reasons lie in the failure of the marketer to
accurately prove his or her worth and/or the inability to
relate marketing performance to reliable financial metrics
(Lehmann 2004; Rust, Lemon, and Zeithaml 2004)
• This entails relating marketing performance to a higher-
level financial metric that is of concern to the CEO
• CIC and its partners have taken initiatives in attaching a
monetary label to each online buzz

14
© Copy rights reserved, Zou Deqiang Kumar and Shah (2009)

15
R3 and CIC (2010), Internet Word of Mouth Proven to Have Impact on Auto Sales in China.

What is the Causality?

R3 and CIC (2010)

Causality: Temporal Sequence

Take the Teane for example, its positive IWOM led the way by one month
© Copy rights reserved, Zou Deqiang for positive sales results R3 and CIC (2010)

Confounding? (I)

18
© Copy rights reserved, Zou Deqiang R3 and CIC (2010)

Confounding? (II)

19
© Copy rights reserved, Zou Deqiang R3 and CIC (2010)

Discovery: Three Components

• Y= f (x)

Why?

How?

So what?

20

21
Nielsonwire (2011), How Social Media Impacts Brand Marketing, Oct 14.

Diagnose: One by One

• Why
 Differentiated effect of positive vs. negative WOM
 Other marketing efforts, e.g., pricing, channel, communication?
• So what
 Sales volume or incremental sales volume?
• How
 Clarify the causal relation, the underlying mechanism
 WOM can be the consequences of other marketing efforts
 Where is competition?

22

Price Elasticity
• The refrigerated juice own- and cross-elasticities

a The percentage change in the sales of MinuteMaid with response to a
23
1% change in the price of Tropicana
Dominique M. Hanssens, Leonard J. Parsons, and Randall L. Schultz (2003), Market Response Models:
Econometric and Time Series Analysis (2nd Ed.), Springer.

Groupon’s Influence on Reputation

http://www.technologyreview.com/blog/arxiv/27150/ (Sep 12, 2011)
http://www.36kr.com/p/46496.html (Chinese) 24
Source: http://arxiv.org/abs/1109.1530

Conceptual Framework of UGC and
Stock Performance

Third party
reports

Gerard J. Tellis and Seshadri Tirunillai (2011), “Does Chatter Really Matter? Validity and
Meaningfulness of User‐Generated Content (UGC) for Brand Equity,” Marketing Scholar Forum (XI),
Beijing: Peking University.

Summary of the Measures

UGC Metrics Brand Performance
• Chatter (volume) • Abnormal Returns
• Ratings • Risk
• Positive Chatter • Trading Volume
• Negative Chatter
• Competitor metrics
(chatter, positive,
negative)

Tellis and Tirunillai (2011)


Effect of Chatter on Brand Performance

Brand Performance
Trading
Returns Risk
Volume
Chatter +++ 0 +++
Negative
Chatter ‐‐‐ ++ ‐‐‐
Cause
Positive
Chatter
0 0 0

Ratings 0 0 0
(Based on Granger Causality Test)


Effects of UGC On Brand Equity

Returns
Immediate 5%
Chatter
Accumulated 15%
Immediate 1%
Ratings
Accumulated 2%
Immediate ‐3%
Negative Chatter
Accumulated ‐7%
Immediate 1%
Positive Chatter
Accumulated 3%


Effects of UGC On Competitor Equity

Returns
Chatter Immediate 5%
Competitor Immediate -1%
Chatter Accumulated -2%
Competitor Immediate 2%
Negative Chatter Accumulated 3%

• Any opportunities for new research?
 A missing link: online buzz the product market the financial
market


Online Consumer Review and Box
Office Revenue
• The volume, but not the valence,
of online user reviews has a
positive impact on box office
revenues (Liu 2006)
• The valence, but not the volume,
of online user reviews has a
positive impact on box office
revenues (Chintagunta et al. 2010),
exploiting the sequential release
of movies across markets

Pradeep K. Chintagunta, Shyam Gopinath, and Sriram Venkataraman(2010), “The Effects of
Online User Reviews on Movie Mox Office Performance: Accounting for Sequential Rollout
and Aggregation across Local Markets,” Marketing Science, 29(5), 944‐957.
Yong Liu (2006), “Word of Mouth for Movies: Its Dynamics and Impact on Box Office
Revenue,” Journal of Marketing, 70(3), 74‐89. 30

The $ Value of the Star

Luca (2011)
31

It Pays!

• The impact of consumer reviews on the restaurant
industry:
 a one-star increase in Yelp rating leads to a 5-9 percent increase
in revenue,
 this effect is driven by independent restaurants; ratings do not
affect restaurants with chain affiliation, and
 chain restaurants have declined in market share as Yelp
penetration has increased

Michael Luca (2011), “Reviews, Reputation, and Revenue: The Case of Yelp.com,” HBS
Working Paper, September. 32

Our Research Plan

• Relate monthly car sales in China to CIC data
 Buzz volume and valence at attribute level
 Incorporate competition effects
• Even more ambitious idea
 Relate OCJ sales data to natural language analysis of its
infomercial at attribute level
• Because analysis at attribute level is highly informative
and insightful
 Analyze and visualize market structure by automatically eliciting
product attributes and brand’s relative positions from online
customer reviews (Lee and Bradlow 2011)

36

Thomas Y. Lee and Eric T. Bradlow (2011), “Automated Marketing Research Using Online 38
Customer Reviews,” Journal of Marketing Research, 48 (5), 881‐894.

Mapping the Market Using Customer Reviews
Lee and Bradlow (2011)

Technically
sophisticated
consumers:
low‐light or ISO
controls and
lens
characteristics
(e.g., name
brand optics
such as Zeiss)

Lay consumer would notice: easy‐to‐use menus and navigation 39
interfaces, the number of pictures available, and video capabilities

Evolution of Market Structure

40
Lee and Bradlow (2011)

Market Structure by Cons and Pros

41
© Copy rights reserved, Zou Deqiang Lee and Bradlow (2011)

42
CIC, 新浪 (2011), “微博引领的中国社会化商业发展与变革,” CIC·新浪合作微博白皮书, 10月.

We Care About, More

• Tweet diffusion model for
Burberry 3D fashion show

GroupM and CIC (2011)

43

Descriptive Statistics

GroupM and CIC (2011), “The Voice of Luxury: Social Media and Luxury Brands in China,”
GroupM Knowledge ‐ CIC White Paper on Luxury, August.

44

Scale and Speed

• To profile the consequences of the tweet diffusion
process
 Scale: how many people are infected?
 Speed: how fast are people infected?
• Infection?
 Yes, fashion is contagious.
 Its diffusion is just like epidemics

45

Can the Diffusion Process Be Predictive?

• In recent years, scientists have improved disease surveillance
systems that enable public health officials to follow the
emergence and spread of infectious diseases
 Most systems collect information from the entire population, such as
how many people have visited the doctor for flulike symptoms and how
many flu tests have been submitted to the health department
 In recent flu seasons, the lag time—the gap between a person showing
symptoms of an illness and that data being available to
epidemiologists—has decreased to as little as 1 week
• Although this improvement gives epidemiologists a better idea
of how many people are ill, it doesn’t allow them to track an
outbreak in real time or anticipate its spread

http://news.sciencemag.org/sciencenow/2010/09/social‐network‐predicts‐flu‐spre.html 46

Social Network Predicts Flu Spread

• Researchers who tracked flu symptoms in the friends of
a group of college students during the 2009 H1N1
"swine flu" pandemic predicted the flu outbreak in the
general college population with at least 2 weeks'
advance notice
 Randomly chose 319 Harvard undergraduates, who then named
425 of their friends. Checked on the health of these 744 students
between 1 Sep 2009, and 31 Dec 2009 using two different
methods: a twice-weekly e-mail survey and the students' records
at the campus health clinic
 Students in the friend group showed signs of the flu between 14
and 69 days before the epidemic peaked in the control group of
randomly selected undergraduates

47

Segmentation: Friendship Paradox

• Their predictions depend on a characteristic of social
networks known as the friendship paradox, which
states that your friends have more friends than you do
 Although it seems that on average your friends should have the
same number of friends as you do, a person named as a friend
actually has more friends than you, because people named as
friends tend to be more popular
 They also tend to be better connected and more central to the
social network
• Previous research showed that well-connected people in
a network caught infectious diseases before those with
fewer connections

48

Progression of flu contagion in the
friendship network over time


Differences in Contagion

Nicholas Christakis: How social networks predict epidemics (Sep 2010),
http://www.ted.com/talks/nicholas_christakis_how_social_networks_predict_epidemics.html 50

Is it Marketing Relevant?

• "What our method offers is the premise of predicting
the future," Christakis says
 To apply the strategy to the general population, he notes, all
researchers would have to do is ask a randomly selected group
of people to identify their friends and then track when these
friends become ill. "Today, you can know where the epidemic will
be in 2 weeks.“
• This lead time can give public health officials more time
to develop an effective response to the outbreak
• In the context of social media
 We care about the “lead time”
 How to define “a fiend”?

51

The Long-term Downside of
Overnight Success
• Professor Jonah Berger, and Gael Le Mens tracked the
popularity of first names over 100 years in France and
the United States
 The names that soar into popularity fastest, they discovered,
also tend to fall out of favor more quickly.
 "We often see products, ideas and behaviors catch on and
spread like wildfire. New high-tech gadgets or YouTube videos
go from unknown to amazingly popular," says Berger. "But we
know less about why once-popular things become unpopular."

52
© Copy rights reserved, Zou Deqiang Berger and Le Mens (2009)

A few trajectories of first-name popularity

Jonah Berger and Gael Le Mens (2009), “How Adoption Speed Affects the Abandonment
of Cultural Tastes,” Proceedings of the National Academy of Sciences, 106, 8146‐8150. 53

Bill Marsh and Alicia DeSantis (2009), “Quick Arriving Fads Quick to Flame Out,” The New 54
York Times, May 16.

100 Years of Names

• Names are also a good proxy for products and services
that convey symbolic meaning about identity
• Being the same, yet different
 parents' attitudes toward naming their children reflect a
fundamental tension between an individual's desire to conform
and fit in with others, while at the same time preserving a
distinctive identity
• That’s how people buy luxury goods

55

Social Preference in Conspicuous
Consumption
• Snobs
U s ( z1e , p1 )   s v1  p1   ts  s z1e (1)
• Conformists
U c ( z1e , p1 )   c v1  p1   tc  c z1e (5)

Wilfred Amaldoss and Sanjay Jain (2005), “Pricing of Conspicuous Goods: A Competitive 56
Analysis of Social Effects,” Journal of Marketing Research, 42 (February), 30‐42.

Settle the Conflicting Motives

• Conflicting motives for similarity, identity-signaling, and
uniqueness can be resolved at different product levels
 People tend to choose options preferred by in-group members
on dimensions linked to their social identities (e.g., brands), and
this is driven by desires to be associated with those groups
 Higher needs for uniqueness lead people to make
differentiating choices among group associated options (i.e.,
select less popular products from in-group associated brands)
• Evidence in social media?
 Effects with regard to their WOM behavior?

Cindy Chan, Jonah Berger, Leaf Van Boven (2011), Differentiating the “I” in “In‐Group”:
How Identity‐Signaling and Uniqueness Motives Combine to Drive Consumer Choice,
Journal of Consumer Research, Conditionally Accepted 57

Two Types of WOM

• A psychosocial cost associated with positive WOM:
 positive WOM can decrease the uniqueness of one’s
possessions, which hurts high-uniqueness individuals
• High-uniqueness individuals are less willing to generate
positive WOM for publicly consumed products that they
own
• They are as willing to discuss product details
• How to figure them out
 In the context of social media?

Amar Cheema and Andrew M. Kaikati (2010), “The Effect of Need for Uniqueness on
Word Of Mouth,” Journal of Marketing Research, 47 (3), 553‐563. 58

Conspicuous Consumption and Race

• Blacks and Hispanics devote larger shares of their expenditure
bundles to visible goods (clothing, jewelry, and cars) than do 60
comparable Whites Charles, Hurst, and Roussanov (2009)

Alternative Explanations

• Inherent ethnic difference
• Conspicuous consumption is used as a costly indicator
of a household’s economic position
 Status signaling: individuals derive utility from status, which
depends on others’ beliefs about their income. Although income
(or wealth) is not observed, visible consumption is
 Visible consumption rise in own income, and fall in the income of
the reference group— for each race group
 An individual’s reference group is defined as persons of the
individual’s race living in his state
• Reference group in social media

Kerwin Kofi Charles, Erik Hurst, and Nikolai Roussanov (2009), “Conspicuous Consumption and
Race,” Quarterly Journal of Economics, 124 (2), 425‐467. 61

Not everything that can be
counted counts, and not
everything that counts can be
counted

- Albert Einstein

Computers are useless. They
can only give you answers.

- Pablo Picasso

Discovery: Review

• Y= f (x)

Why?

How?

So what?

64

Yuval Atsmon, Vinay Dixit, Glenn Leibowitz, and Cathy Wu (2011), “Understanding China’s
Growing Love for Luxury,” McKinsey Consumer & Shopper Insights, McKinsey Insights China.

65

68
deqiang_zou@fudan.edu.cn

Discovery-Oriented Social Media Research

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