NYU Stern Spring 2013 - MITweb.mit.edu/sinana/www/Digital Analytics & Strategy 2013 - S3&S4.pdf ·...
Transcript of NYU Stern Spring 2013 - MITweb.mit.edu/sinana/www/Digital Analytics & Strategy 2013 - S3&S4.pdf ·...
NYU Stern Spring 2013
Digital Analytics & Strategy Page 1
Digital Analytics & Strategy
Sessions 3 & 4: Network AnalyticsNetworks, Influence and Social Media Analytics
Prof. Sinan Aral
Digital Analytics & Strategy: Sessions 3 & 4
Learning Objectives: Network Analytics
1. Understand why (Social) Networks are so critical to demand prediction and marketing.
2. Understand Economic Network Effects and why they are
so essential to Digital Strategy.
3. Understand the importance of Causal Statistical
Estimation in effective Social Network Marketing efforts.
4. Understand Viral Product Design and its implications for
a) Social Contagion in Product Adoption, b) Sustained
Product Use and c) the relationship between the two.
5. Consider how to identify influence in social media and
common misconceptions about influence and influencers.
(Why) are Online Social Networks so Important?
Lots of Personal Information is Revealed
• An Advertisers Dream!
Online Social Networks and eCommerce
Your Connections Reveal Your Preferences
• “Homophily” – People tend
to interact with others like themselves… “Birds of a
feather flock together.”
• If your friends like hiking, soccer, reading and the
Killers you are more likely
to like hiking, soccer, reading and the Killers.
• In this way – network connections can reveal
your preferences.
Online Social Networks and eCommerce
• Friends can tell friends how much they like what
they bought or experienced.
• You trust your friends opinions more than a generic
advertisement.
• Example: Facebook’s “Beacon” System
• Trust and privacy must be carefully addressed by
social network based advertising. (Beacon was shut
down and Mark Zuckerberg apologized).
• If we have time (1:27):
http://www.youtube.com/watch?v=1CGF00VIxB8
Peer to Peer Marketing
Online Social Networks and eCommerce
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Predicting Demand (Purchase) from Networks
• Knowing who is connected with whom enables you
to identify the mostly likely adopters of products.
“Network Neighbors Study” (Hill, Provost, Volinsky)*
Marketing to Offline Social Networks
* “Network-based Marketing: Identifying likely adopters via consumer networks. ” Statistical Science 21 (2) 256–276, 2006.
• Product: new communications service
• Long experience with targeted marketing
• Sophisticated segmentation models based on data,
experience, and intuition
– e.g., demographic, geographic, loyalty data
– e.g., experience and intuition regarding the types of
customers known or thought to have affinity for this type
of service
• Added: Whether ‘network neighbors’ (by phone calls)
had already adopted the service.
Predicting Demand (Purchase) from Networks
“Network Neighbors Study” (Hill, Provost, Volinsky)*
Marketing to Offline Social Networks
* “Network-based Marketing: Identifying likely adopters via consumer networks. ” Statistical Science 21 (2) 256–276, 2006.
Prior Adopter
Non Adopter
Network
Neighbor
Adopter
Network
Neighbors
Non-Adopters
Predicting Demand (Purchase) from Networks
“Network Neighbors Study” (Hill, Provost, Volinsky)*
Marketing to Offline Social Networks
* “Network-based Marketing: Identifying likely adopters via consumer networks. ” Statistical Science 21 (2) 256–276, 2006.
1
4.82
2.96
0.4
Non-NN 1-21 NN 1-21 NN 22 NN not
targeted
(0.28%)
(1.35%)
(0.83%)
(0.11%)
Relative Sales Rates for Marketing Segments
Network Effects
Digital Strategy: Session 5
Sources of Positive Feedback
Supply-side economies of scale (Traditional markets)
• More customers � more units produced � lower average
cost per unit
• Marginal cost less than average cost
• Spreading fixed costs across more units
• Manufacturing efficiencies, learning by doing
Demand-side economies of scale (Digital markets)
• More units consumed � higher value per unit
• The value of the good comes from the network of consumers
who use it (at least in part)
• Most commonly caused by network effects (Microsoft,
Playstation, Facebook)
• Positive relationship between popularity and value
Consumer expectations are key!
Virtuous vs. Vicious Cycle
• Expectations matter! Users want to join the
network of winners
• “Rich get richer, poor get poorer”
number of compatible users
value to
user
virtuous
vicious
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Markets With Network Effects
• A market exhibits network effects (also known as
“increasing returns to scale” in consumption) when the value to a buyer of an extra unit is higher when more
units are sold, everything else being equal
– A node can reach more nodes in a large network
– Large sales of components of type A induce larger
availability of complementary components B1, ..., Bn,
thereby increasing the value of components of type A
The Model
• Value of a product in a market with
network effects is given by:
Zt is the size of the network at time t,
α represents the value without network effects
γ represents value from network effects.
tZV γα +=
Network Markets: History Matters (I)
� A and B are incompatible but have the same price
� A is available at time 0. B will be available at time t, but
customers do not know its availability until t.
� A and B have intrinsic values of a and b respectively
� Network value is c per user for both products
� Customer arrival rate is 1 per unit time
Network Markets: History Matters (II)
a
b
a+ct
t0
Value
Time
Q: Which product will a new customer at time t adopt? Why?
Network Markets: History Matters (III)
� The superior product, B, is not adopted.
� For network products, both intrinsic performance and installed
base matter.
� A has an inferior performance, but has an installed-base advantage by time t, with total value a+ct>b.
� This is precisely why the inefficient QWERTY keyboard hasn’t
been replaced.
Network Markets: Compatibility Matters
� What happens if B is compatible with A?
a
b
b+ct
t0
Value
Time
Q: What’s the network size of B at time t? Why?
a+ct
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Network effects: Tipping
number of users
value to
each user
� More units consumed –> higher value per unit
� Tipping: Success feeds on itself and strong positive feedback can
lead to a “winner-take-all” situation. (eg: Netscape vs. Mosaic, IE vs.
Netscape, Wintel vs. Apple, Nintendo vs. Atari)
� Inferior products that move first may dominate
� Product introduction is difficult, entry strategy is crucial
PRODUCT A
PRODUCT B
Network effects and ‘tippy’ markets
tZV γα +=
Network effects and ‘tippy’ markets
PRODUCT A
PRODUCT B
tZV γα +=
Network effects and ‘tippy’ markets
PRODUCT A
PRODUCT B
Network effects and ‘tippy’ markets
PRODUCT A
PRODUCT B
Network effects and ‘tippy’ markets
PRODUCT A
PRODUCT B
NYU Stern Spring 2013
Digital Analytics & Strategy Page 5
PRODUCT A
PRODUCT B
Network effects and ‘tippy’ markets
Social Capital in Networks
Digital Strategy: Session 5
Value in Structure
• Putting aside personal data, there is valuecreated as a function of network structure.
• Value created for the people or entities in the network:
• How structure generates value for people in the network, (in different network positions)…
“Social Capital”
Social Capital
Social Capital: “[T]he aggregate of the actual or potential resources which are linked to possession of a durable network of … institutionalized relationships of mutual acquaintance or recognition.”
-- Bourdieu (1986: 243)
• Resources?
• Actual or Potential?
• Durable?
• Why include “recognition”?
Why Should We Care?
Individuals in Favorable Network Positions,
those with more “Social Capital”…
• Find better jobs more quickly
• Are more likely to be promoted earlier
• Close deals faster
• Receive higher performance evaluations
• Receive larger bonuses
• Enhance the performance of their teams
• Are more likely to generate innovation (good ideas)
What is a favorable position?
Why is it favorable?
How do benefits accrue to actors in those positions?
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What is a Social Network?
� A (Social) Network: A collection of (individuals)
things connected through different types of ties.
(friendship, work relationship, family ties, co-purchase)
� Nodes – People or things that are connected.
� Edges – Links, ties or relationships that connect them.
� Network Structure – The shape of the web of
connections.
Examples of Social Networks
Interdisciplinary collaboration network at the Santa Fe Institute
Examples of Social Networks
High-school friendship network
Examples of Social Networks
High-school dating network
Notice how the
structure is different for friendship and
dating!
Structure can tell us
a lot about how social groups are
organized, how
information flows, how influence
spreads!
Examples of Social Networks
Phone Call Traffic at a Global Media Firm
We Can Map How
Information Flows!
Examples of Social Networks
Firm Communication Network
Communication Network:Firm Headquarters
.55 (.15).32 (.20)Researcher
Information
Diversity
Network
Constraint
.55 (.14).30 (.18)Consultant
25.3212.20Clustering
Coefficient
11.02 (32.44)5.41 (19.08)Average
Density
.59 (.12).24 (.14)Partner
.57 (.14).29 (.17)Mean
3473Recruiters
HQFIRM
Firm Communication Network
Communication Network:Firm Headquarters
.55 (.15).32 (.20)Researcher
Information
Diversity
Network
Constraint
.55 (.14).30 (.18)Consultant
25.3212.20Clustering
Coefficient
11.02 (32.44)5.41 (19.08)Average
Density
.59 (.12).24 (.14)Partner
.57 (.14).29 (.17)Mean
3473Recruiters
HQFIRM
Email Network at an Executive Recruiting Firm
Location, Location,
Location – Geography Matters!
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Examples of Social Networks
Sexual contact network
We Can Model How
Diseases Spread!
Examples of Social Networks
Internet Relay Chat (IRC) Channel
Examples of networks
Machine Learning Papers
Networks of Things!
In this case, papers
linked by citations!
Examples of networks
The Web, circa 1998
Web Sites Connected
through Hyperlinks
Examples of networks
Books linked by co-purchases (we return to this in “The Long Tail”)
Networks Are Dynamic
• http://www.youtube.com/watch?v=8TRzrgMlOKc
• http://www.youtube.com/watch?v=55Q4BwkkRQU
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What are “strong ties” “weak ties”?
Strong Ties and Weak Ties
A B C D
“Strong Tie” “Weak Tie”
More Frequent Interaction
Less FrequentInteraction
More EmotionalAffect
Less EmotionalAffect
More Trust Less Trust
More InformationShared
Less InformationShared
Strong Ties and Weak Ties
What is “the strength of weak ties”?
Why are weak ties important?
Building Blocks of Network Structure
A
B
C
“The Forbidden Triad”(Granovetter 1973)
� If A has a strong tie to B, and
� If A has a strong tie to C
=> It is highly likely that B and C
have at least a weak tie.
Why?
“Triadic Closure”
Building Blocks of Network Structure
“Triadic Closure” => “Clustering” in Networks
A
B
C
D
E
F
G
H
I
J
K
l
Clustering
Co-authorship Graph
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Clustering
Biotech Collaborations
Clustering
Owen Mundy’s Facebook Graph
Small Worlds Graphs
Watts and Strogatz (1998)
Normal Small Worlds Random
Implications for Degree Distributions
Barabasi and Albert (1999)
Clustering Enables Brokerage
Clustering => Opportunities for “Brokerage”
Bridges are usually Weak Ties
Opportunities for “Brokerage”…
are typically enabled by weak ties
Bridging Weak Ties
span “Structural
Holes”
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Who is the Broker?
Burt (2005:14)
S-hole is the mechanism underlying Granovetter’s claim that weak ties are
more useful because they give actors access to nonredundant information
Two Main Benefits to Structural Holes
Information
Control
Early Promotion Profit Margins
Innovation Creativity and Good Ideas Information Advantage
Value of information comes from from its uneven distribution across local network neighborhoods.
Connection to diverse neighborhoods gives access to novel pools of information.
Novel information is valuable due to its local scarcity.
Actors with scarce, novel information can
� broker opportunities, engage in information arbitrage
� use information as a commodity, or
� apply information to problems that are intractable given local information (innovation).
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Source of Information Advantages
Constrained Unconstrained
A 40 Year Old Assumption
� Network structure is associated with
productivity and performance.
� Productivity of information workers (Aral et al 2006)
� Productivity of R&D teams (Reagans & Zuckerman 2001)
� Labor Market Outcomes (Montgomery 1991, 1992)
� Wages, Promotion (Burt 1992), Job Placement (Granovetter 1973)
� Innovation (Burt 2004)
� Key theoretical mechanism: access to
information.
DIVERSE
NETWORKS
DIVERSE, NOVEL
INFORMATION
PRODUCTIVITY,
PERFORMANCE,
INNOVATION
The Diversity Bandwidth Tradeoff
Channel BandwidthLowHigh
Network DiversityLow High
A Constrained Network of
Strong, High Bandwidth
Ties
A Diverse Network of
Weak, Low Bandwidth
Ties
Channel BandwidthLowHigh
Network DiversityLow High
A Constrained Network of
Strong, High Bandwidth
Ties
A Diverse Network of
Weak, Low Bandwidth
Ties
Diversity � weak ties => lower bandwidth, frequency, topical dimension, detail, complexity.
The Theory is Problematic because Structural Diversity is likely to be associated with weak
ties. Creating A Tradeoff Between Network Diversity and Channel Bandwidth.
Information Environment Mediates Tradeoff
1. Information Overlap – The degree to which
topics are uniformly or heterogeneously
distributed over nodes.
2. Size of the Topic Space – How many distinct
topics exist in the network.
3. Information Turbulence (the Refresh Rate)
– How often actors’ information is refreshing or
changing per unit time.
Cohesion (The Strength of Strong Ties)
The Value of Cohesive Networks: High trust in a community with
cohesive networks - strong ties fosters mutual assistance obligations
and the social control of deviant behaviors (Coleman 1988)
Ronald Burt: Ego gains numerous competitive advantages and higher
investment returns if ego’s weak, direct-tie relations span structural
holes, thus serving as bridge between alters
Holes create social capital via brokerage opportunities
► Ego actor gains earlier access to flows of valuable information
► Ego fills structural holes by forging new ties linking its unconnected alters,
extract “commission” or “fee” for providing brokerage services
► Low network constraints result in high performance rewards
► Ego maximizes its self-interests by controlling & exploiting information, playing
one actor against another (“tertius gaudens”)
Local vs Global Structural Holes
Reagans and Zuckerman (2001)
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Summary
1. Triadic Closure (Forbidden Triad)
2. Clustering and Small Worlds (Watts & Strogatz)
3. Strength of Weak (Bridging) Ties (Granovetter)
4. Structural Holes and Brokerage (Burt)
5. The Diversity Bandwidth Tradeoff (Aral and Van Alstyne)
6. Network Cohesion (Strength of Strong Ties)
(Coleman)
7. Local vs Global Structure (e.g. Reagans and Zuckerman)
Causal Inference, “Influentials” and Network Marketing
Digital Strategy: Session 5
@sinanaral @sinanaral
@sinanaral@sinanaral
72 © 2009 Sinan Aral. All rights Reserved.
1. Convince you that :
Success in Social Network Marketing is about
Identifying Causal Peer Influence in Networks.
2. Show you Two Examples of How We Do It
Observational
Experimental
Causality
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“Obesity is Contagious”
Christakis & Fowler (2007)
74 © 2009 Sinan Aral. All rights Reserved.
“Obesity is Contagious”
“Di
Christakis & Fowler (2007)
“We evaluated a densely interconnected social network of 12,067 people assessed repeatedly
from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was
available for all subjects. We used longitudinal statistical models to examine whether weight
gain in one person was associated with weight gain in his or her friends, siblings, spouse, and
neighbors.”
“[S]tatistical analysis involved the specification of longitudinal logistic-
regression models in which … obesity status at any given time (t+1) was a function of
various attributes, such as the ego’s age, sex, and educational level; the ego’s obesity status
at the previous time point (t); and most pertinent, the alter’s obesity status at times t
and t+1.”
75 © 2009 Sinan Aral. All rights Reserved.
�Obesity Movie
76 © 2009 Sinan Aral. All rights Reserved.
“Obesity is Contagious”
“Di
“CONCLUSION: Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties.”
“Results: Discernible clusters of obese persons were present in the network at all time points …
A person's chances of becoming obese increased by 57% (95% confidence interval [CI],
6 to 123) if he or she had a friend who became obese in a given interval ... These effects were
not seen among neighbors in the immediate geographic location. Persons of the same sex
had relatively greater influence on each other than those of the opposite sex. The spread of
smoking cessation did not account for the spread of obesity in the network.”
Christakis & Fowler (2007)
77 © 2009 Sinan Aral. All rights Reserved.
Two Main Arguments Support Claims of Influence
1. Assortative Mixing – Correlation of
Observed Behaviors and Network Structure [e.g. Birke & Belchamber 2009, Christakis & Fowler
2007, 2008, Java et. al. 2006]
2. Temporal Clustering – Friends adoption of
the behavoir is correlated in time [e.g.
Anagnostopoulos et al. 2008, Crandall et. al. 2008,
Christakis & Fowler 2007, 2008]
78 © 2009 Sinan Aral. All rights Reserved.
Logic of the Temporal Clustering Argument…
Anagnostopoulos et. al. [15: 3] argue “if influence does not play a role, even though an agent’s probability of activation could depend on her friends, the timing of such activation should be independent of the timing of other agents.”
Is this convincing?
NYU Stern Spring 2013
Digital Analytics & Strategy Page 14
79 © 2009 Sinan Aral. All rights Reserved.
Research Questions
To what extent do networked social relationships influence
economic decisions (e.g. product adoption), creating
systematic population level patterns in product demand and
other behaviors?
Can we distinguish “influence” in networks from “homophily”
and other confounding factors? If so, what is the relative
importance of each in explaining clustering, and what is the
bias in parameter estimates that do not adequately identify
influence?
@sinanaral
Influence in Social Media Networks
@sinanaral
@sinanaral
@sinanaral
Social Influence:
How the behaviors of one’s peers change
the likelihood that (or extent to which) one
engages in a behavior.
…Behavior change…
Aral, S. (2011) “Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion.” Marketing Science; 30(2): 217-223.
A Stricter Definition of “Influence”@sinanaral
Causal Inference
@sinanaral
The “Reflection Problem”
Human behaviors cluster in
network space and time…
but is this because of
peer influence or
alternate explanations?
@sinanaral
� Homophily (Aral et al. 2009)
� Latent Homophily (Shalizi and Thomas 2011)
� Confounding Factors (Aral and Walker 2011, 2012)
� Simultaneity (Godes and Mayzlin 2004)
� Unobserved Dyanmic Heterogeneity (Van den Bulte and Lilien 2001)
� Truncation (Van den Bulte and Iyengar 2010)
� Other Contextual and Correlated effects (Manski 1993)
Estimation Challenges
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@sinanaral
“If you see a crowd of people all put up their umbrellas at the same time, you don’t assume that social influence is responsible.”
– Max Weber
@sinanaral
“Yahoo Study” - Data
� Global IM Network of 27 Million Users from Yahoo! (Daily Traffic)
� Detailed demographics and geographic data.
� Comprehensive, detailed and precise data on online behaviors/activities.
� Day by Day adoption and usage of a mobile service application (Yahoo Go) launched in July 2007. (532,365 Adopters)
Jul 1 Aug 1 Sep 1 Oct 1 Nov 10
2500
5000
7500
10000
12500
Ad
op
ters
pe
r d
ay
Time
Aral, Muchnik & Sundararajan (2009) “Distinguishing Influence Based Contagion from Homophily Driven Diffusion in Dynamic Networks,” PNAS, December.
@sinanaral
0
2
4
6
8
10
12
14
16
20 40 60 80 100 120 140 160
INF
LU
EN
CE
DAYS SINCE LAUNCH
P(Adoption | Friend Adopts)
Controlling for correlated preferencesand confounding effects
The “iPad Effect”
@sinanaral
Homophily Exaggerated Among Early Adopters
Jul Aug Sep Oct
0.7
0.8
0.9
Adopter friends
Non-adopter friends
Random user
Co
sin
e D
ista
nc
e
Time
Sim
ilari
ty
Time
@sinanaral@sinanaral @sinanaral
Constructed Observational Evaluation(Eckles 2012; Bakshy et al 2011)
1. HDPSM can achieve 80% Error Reduction.
2. Context Relevant Variables are Key.
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“VIRAL DESIGN” Can we engineer products so
they are more likely to be virally shared?
@sinanaral
Aral & Walker (2011) “Creating Social Contagion through Viral Product Design: A Randomized Trial of Influence in Networks,” Management Science, September.
@sinanaral
The Setup
App
� Randomly Enabled Viral Messaging.� Observed the Adoption and Use of the App by Friends of
Control and Experimental Group Users.
@sinanaral
Data
~ 10K Experimental Users
~ 1.4M Friends of Experimental Users
We Observe Application Diffusion Over this Network
1. Facebook Profiles
2. Adoption
3. Use
Flixster - An Example Facebook Application
Flixster - An Example Facebook Application
Users can invite their friends
to adopt the application and join their social network on
the application itself.
Users can invite their friends
to adopt the application and join their social network on
the application itself.
Users can invite their friends
to adopt the application and join their social network on
the application itself.
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Users can invite friends to install this application and
include a personal message
Users can invite friends to install this application and
include a personal message
Users can invite friends to install this application and
include a personal message
Users can invite friends to install this application and
include a personal message
Invites are a form of Viral Messaging
Another form of viral messaging is
notifications
Notifications are generated
automatically when a user takes an
action within an application. They
are delivered to a user’s Facebook
friends like this
In addition to viral messaging, Facebook applications also make use of traditional
online advertisements by placing ads directly inside the application region.
There is a market for Within-Application
advertising.
@sinanaral
� Leakage and contamination could occur if peers are
a) connected through indirect pathways,
b) connected to multiple treated peers in different treatment groups or
c) connected to multiple treated peers in the same treatment group.
� These scenarios are rare in our data
� We control for leakage and peers of multiple treated users by only evaluating recruited users and right censoring contaminated peers.
� Contaminated: Any peer with multiple treated peers after time t at
which they have multiple treated peers.
� Sensitivity analysis shows censoring has little effect. This may
make our results more conservative but minimizes contamination.
Contamination, Leakage & Interference
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@sinanaral
“Inside-Out” Estimation
βλλ kiX
kkik etXt )(),( 0=
Variance Corrected Stratified Duration Models
),()0|1( 1 jtj ijititit ywxFyyP ∑=== − βγ
Conventional Approach in Observational Data
@sinanaral
Personal
Invitations
Passive
Awareness
Influence Per
Message
Global Diffusion
Stickiness
↑6% ↑2%
↑98% ↑246%
↑17% 0%
Which Features Spread Contagion Best?
Viral
Invitation
Viral
Notification
Campaign
FB
Banner
Web
Banner
CTR
Relative Marketing Effectiveness
6% 2%2%-5.9%
.10%-.20%
.07%
@sinanaral
@sinanaral
@thesocialcure
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@sinanaral
• Research in 1950’s emphasized importance of personal influence
– Trusted ties more important than media
influence in determining individual opinions
• Also found that not all people are equally influential
– A minority of “opinion leaders” or “influencers” are responsible for influencing
everyone else
• Called this “the two-step flow” of information
– “One in ten Americans tells the other nine how to vote, where to eat, and what to buy.” (Keller and Berry, 2003)
The Two-Step Flow, Opinion Leaders, & Influencers
@sinanaral
The “Law of the Few”
• Since 1950’s idea that minority of special people has a vastly disproportionate impact on social change has caught on
– Gladwell (2000) called this “the law of the few”
• Not really what K&L were saying
– Two-step flow says on that influencers decide which
information to pass on
– Law of the few says that they trigger social epidemics
• Nevertheless, this idea has become extremely popular
@sinanaral
=
“Social epidemics ... are also driven by the efforts of a handful of exceptional people” Gladwell (2000)
IT’S SUCH A GOOD STORY…
@sinanaral
Individuals work like this…Influentials Hypothesis assumes
society works the same way…
IT MAKES SENSE…
@sinanaral
Marketers Love Influencers
“Influencers have become the ‘holy
grail’ for today’s marketers.”
—Rand (2004)
@sinanaral
BUT GRAILS ARE HARD TO FIND…
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@sinanaral
Experiment 2
We also Randomized Receipt of Notifications
Only a Randomly Selected Subset of Neighbors
Receive Passive Viral Messages
Identifying “Influentials” and “Susceptibles”
Allows us to test:
Randomized Trails of Influence and Susceptibility to Influence
Aral, S. & Walker, D. (2012) “Identifying Influential and Susceptible Members of Social Networks.” Science; 337 (6092): 337-341.
@sinanaral
Results
� Influence increases with Age
� Susceptibility decreases with Age
� Women are less susceptible to influence than men
@sinanaral
Results
� Influence transmits over relationship pairs of the same age.
� Suggestive evidence that older people influence younger people more than younger people influencing older people.
� Women are less susceptible to influence than men
� Influence transmits more relationship pairs where the sender is of the same or greater level of relationship commitment as the recipient.
Susceptibility to Influence
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
Single Relationship Engaged Married Its Complicated
@sinanaral @sinanaral
Results
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@sinanaral @sinanaral
@sinanaral
Learning Objectives: Network Analytics
1. Understand why (Social) Networks are so critical to demand prediction and marketing.
2. Understand Economic Network Effects and why they are
so essential to Digital Strategy.
3. Understand the importance of Causal Statistical
Estimation in effective Social Network Marketing efforts.
4. Understand Viral Product Design and its implications for
a) Social Contagion in Product Adoption, b) Sustained
Product Use and c) the relationship between the two.
5. Consider how to identify influence in social media and
common misconceptions about influence and influencers.