Rsc aral final

From Conversations to Conversions: Identifying Peer Influence and Social Contagion in Networks The Rise of Social Commerce October 6, 2010

Sinan Aral NYU, MIT & Social Amp sinan@stern.nyu.edu

Agenda 1. Convince you that : Success in Social Commerce is about Identifying Causal Peer Influence in Networks.

2. Show you Two Examples of How We Do It One Observational One Experimental

The Importance of Social Networks

Interest in Social Networks has exploded...

Many organizations are attempting to collect and analyze large scale social network data…

Call networks

Affiliation networks

Email networks

Friendship networks

Terrorist networks

Organizational networks

New unprecedented data allow us to tackle previously intractable research questions (Lazer et. al. 2009)

A Program of Research To mine massive social network data to better understand:

How behavioral contagions spread in human social networks How peers influence each other to buy products, be more productive, vote for a different political candidate... Â Product Adoption & Demand

 Disease Prevention (Condom Use)

 Productivity of Information Workers

 Market Trends (Buzz)

 Viral Peer-to-Peer Marketing

 Economic Development

Causal Statistical Estimation

The “Reflection Problem”

Manski (1993)

Identifying Causal Peer Effects in Networks is Notoriously Difficult

Now lots of empirical evidence that human behaviors tend to cluster in network space and time,… but is this because of peer influence or alternate explanations?

“Obesity is Contagious”

Christakis & Fowler (2007)

But Homophily can also explain such evidence A long line of theory indicates: • “Birds of a feather, flock together” – attributed to Robert Burton (1577-1640) Recently: Lazersfeld and Merton (1954) and Blau (1977) among others.

• (People) love those who are like themselves -- Aristotle, Rhetoric and Nichomachean Ethics • Similarity begets friendship -- Plato, Phaedrus • Hanging out with a bad crowd will get you into trouble -- Sinan’s Mom

Peer Influence is Critical to Social Commerce

We commonly assume: 1. *Social* Commerce is More Engaging than Commerce (Consumers shop for and are persuaded by their friends) 2. Observing *Social* Commerce Provides More Intelligence (Data on Social Shopping enables Targeting and Referral Strategies that provide demonstrable Sales Lift)

These assumptions necessitate causal evidence of peer influence in social commerce outcomes.

Consider the Implications of Homophily vs Influence for Marketing Strategy…

Target “Influentials” vs. Segment the Market based on consumer profiles.

Dynamic Matched Sample Estimation Aral, Muchnik and Sundararajan (2009)

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 for 5 months.

12500

532,365 Total Adopters

Adopters per day

10000 7500 5000 2500 0 Jul 1

Aug 1

Sep 1

Oct 1

Nov 1

Observe Clustering in Longitudinal Data

Distinguishing Influence from Homophily “Influence” Estimates Comparing Adoption in Treated and Untreated Cases Under Randomized Matching Over Time (Methods used by those who take AM as evidence of influence)

“Influence” Estimates Comparing Adoption in Treated and Untreated Cases In Our Dynamic Matched Sampling Framework Over Time

Much of the estimated influence is really observable homophily.

Viral Product Design Can firms explicitly engineer products so they are more likely to be shared amongst peers?

Viral Product Characteristics

Viral Product Features

Content likely to inspire viral sharing

Modalities of use

Usefulness, topicality, prominence, positive valence and unexpectedness (Berger and Milkman 2009, Stephen and Berger 2009, Berger and Heath 2005, Phelps et al 2004, Heath, Bell and Sternberg 2001).

Invites Notifications Hypertext Embedding (No literature)

Targeted Messaging Active-Personalized Messaging is thought to be more persuasive and thus more effective.

People tend to activate their strong ties.

Strong ties exhibit greater homophily.

Greater pressure for conformity.

Deeper knowledge of one another’s preferences.

We tend to trust information from close trusted sources.

Reciprocity.

Personalized messages have been shown to be more persuasive and thus more effective.

But, these messaging channels require more effort and time which may curtail their use.

Passive-Broadcast Messaging may reach more people but may be less persuasive.

Which is more effective is an empirical question.

Viral Feature Space PERSONALIZATION INCREASING BROADCAST

ACTIVE

PERSONALIZED Peer Network

High Effort Minimal Effort

PASSIVE

No Effort

ACTIVITY INCREASING

Population

Specific Individuals Personalized Referrals

Generalized Hypertext Embedding

Personalized Hypertext Embedding

Automated Broadcast Notifications

Greater marginal peer influence per message

Collaborative Bookmarking

Automated Targeted Notifications

More messages generated

Randomized Experiments on Facebook Facebook is an ideal experimental environment in which to study peer influence: Out in the ‘real’ online world, not in a laboratory.

Hundreds of millions of individuals interacting: 500M+ users (35M+ login daily), tens of billions of relationships.

Detailed digital records of user’s online representation and interactions Demographics, preferences, views and interests, social behavior (group participation, communications, status updates), relationships, product preferences, product adoption outcomes

Experimental control is possible through the use of multiple Experimental Facebook Applications developed in concert

The Setup App

Randomly Enabled Viral Messaging.

Observed the Adoption and Use of the App by Friends of Control and Experimental Group Users.

Data

Profile and Network Data on ~10 Million Users Profiles are Dynamic – we observe changes weekly

~ 10K Experimental Users ~ 1.4M Friends of Experimental Users We Observe Application Diffusion Over this Network Adoption Use

Experiment: Randomized Viral Messaging We designed multiple experimental versions of a Facebook Application that has been widely advertized and adopted.

As users adopted the application…

Experiment: Randomized Viral Messaging We designed multiple experimental versions of a Facebook Application that has been widely advertized and adopted.

As users adopted the application…

… we randomly assigned them to Control and Experimental groups.

Experiment: Randomized Viral Messaging We designed multiple experimental versions of a Facebook Application that has been widely advertized and adopted.

‌ we randomly assigned them to Control and Experimental groups.

Experiment: Randomized Viral Messaging We designed multiple experimental versions of a Facebook Application that has been widely advertized and adopted.

‌ and collected data on their personal attributes and preferences from their Facebook profiles‌

Experiment: Randomized Viral Messaging We designed multiple experimental versions of a Facebook Application that has been widely advertized and adopted.

‌ as well as data on their social networks and the personal attributes and preferences of their network neighbors.

Experiment: Randomized Viral Messaging Experimental Group

Control Group

Experimental Group

Viral Messaging Enabled Experimental Users Send Active and Passive Viral Messages to their Neighbors

Control Group Viral Messaging Disabled

Experiment: Randomized Viral Messaging Experimental Group

Control Group

Experimental Group

We also Randomized Receipt of Passive Viral Messages Only a Randomly Selected Subset of Neighbors Receive Passive Viral Messages

Control Group Viral Messaging Disabled

Experiment: Randomized Viral Messaging

We then compare:

Control Group

Experimental Group

9click throughs 9adoption 9usage data of neighbors of: 9Experimental Group 9Control Group

Experiment: Randomized Viral Messaging

We then compare:

Control Group

Experimental Group

9click throughs 9adoption 9usage data of neighbors of: 9Experimental Group 9Control Group

Allows us to test: 1. Average Treatment Effect of Viral Messaging Capabilities on Peer Adoption and Network Propagation 2.Randomized Trails of Susceptibility to Peer Influence via Viral Messaging

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 friends to install this application and include a personal message

Invites are a form of Viral Messaging

Another form of In addition to viral messaging, Facebook applications also make use of traditionalviral messaging is notifications online advertisements by placing ads directly inside the application region.

There is a marketNotifications for Within-Application are advertising. generated automatically when a user takes an action within an application. They are delivered to a user’s Facebook friends like this

Conventional Approach in Observational Data

P ( yit = 1 | yit −1 = 0) = F ( xit γ , β ∑ j wij y jt ) “Inside-Out” Estimation

A Variance Corrected Stratified Proportional Hazards Model

λk (t , X ki ) = λ0 k (t )e X

ki β

Adoption Diffusion Differs Significantly

Passive

Active

Marginal Influence Per Message

2%

6%

Global Adoption Diffusion

246%

98%

Use and Engagement

Network Externalities: 33% Increase in Use Per Friend Referred But: Only Active Recruitment Creates Sustained Engagement

Susceptibility to Influence

Thank You!

From Conversations to Conversions: Identifying Peer Influence and Social Contagion in Networks Sinan Aral NYU Stern School of Business sinan@stern.nyu.edu