Social Network
Michel Bruley
WA - Marketing Director
Extract from various presentations: B Wellman, K Toyama, A Sharma, Teradata Aster, …
February 2012
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Social Network
A social network is a
social structure between
actors, mostly individuals
or organizations
It indicates the ways in
which they are connected
through various social
familiarities, ranging
from casual acquaintance
to close familiar bonds
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Society as a Graph
People are represented as
nodes
Relationships are represented
as edges: relationships may be
acquaintanceship, friendship,
co-authorship, etc.
Allows analysis using tools of
mathematical graph theory
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Social Network Analysis
Social network analysis [SNA] is the mapping and measuring of
relationships and flows between people, groups, organizations, computers
or other information/knowledge processing entities:
Little Boxes
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Glocalization
Networked Individualism
Connections

Size
– Number of nodes

Density
– Number of ties that are present / the amount of ties
that could be present

Out-degree
– Sum of connections from an actor to others

In-degree
– Sum of connections to an actor
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Distance
 Walk
– A sequence of actors and relations that begins and
ends with actors
 Geodesic
distance
– The number of relations in the shortest possible
walk from one actor to another
 Maximum
flow
– The amount of different actors in the neighborhood
of a source that lead to pathways to a target
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Some Measures of Power & Prestige

Degree
– Sum of connections from or to an actor
• Transitive weighted degreeAuthority, hub, pagerank

Closeness centrality
– Distance of one actor to all others in the network

Betweenness centrality
– Number that represents how frequently an actor is
between other actors’ geodesic paths
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Cliques and Social Roles
 Cliques
– Sub-set of actors
More closely tied to each other than to actors who are not
part of the sub-set:
– A lot of work on “trawling” for communities in the webgraph
– Often, you first find the clique (or a densely connected
subgraph) and then try to interpret what the clique is
about
 Social
roles
– Defined by regularities in the patterns of relations
among actors
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Network Analysis Example
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Centrality: strategic positions
Degree centrality:
Local attention
Closeness centrality:
Capacity to communicate
Beetweenness centrality:
reveal broker
"A place for good ideas"
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Social Network Analysis: what for?

To control information flow
To improve/stimulate communication
To improve network resilience
To trust

Web applications of Social Networks examples:



– Analyzing page importance (Page Rank, Authorities/Hubs)
– Discovering Communities (Finding near-cliques)
– Analyzing Trust (Propagating Trust, Using propagated trust to fight spam In Email or In Web page ranking)
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Tangible Outcomes from SNA
Sell More
Better Knowledge
Sharing
Organisational Re-structures
that work
Preserving Expertise
Building Better
Communities
More Innovation
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Competitive Intelligence
Ways to use SNA to Manage Churn
Reduce Collateral Churn
–
–
Reactive
Identify subscribers whose loyalty is
threatened by churn around them
Reduce Influential Churn
–
–
–
Has churned
Prevent collateral
churn
Preventive
Identify subscribers who, should they churn,
would take a few friends with them
Need to go beyond individual value to
network value !
•
A subscriber with negative margin can have
very significant network value and still be very
valuable to keep
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Prevent influential
churn
Cross-Sell and Technology Transfer
2 smartphone users around you 
smartphone affinity x 5 !!
Adopted
Leverage Collateral Adoption
–
–
Reactive
Identify subscribers whose affinity for products is
increased due to adoption around them & stimulate
them
Offer product
Identify influencers for this adoption
–
Proactive
–
Identify subscribers who, should they adopt, would
push a few friends to do the same
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Push for adoption
Acquisition – Member gets Member
Campaign Topic
Acquire New Members
Description
One of an Operator‘s major objectives is to keep (or even extend) the market position.
As the main competitors are making ground by eg. attractive tariffs or through the
acquisition of new retail partners, acquisition of new customers becomes a very important
objective.
This campaign format focuses on influencers in social communities, who are most likely to
recommend a (off-net) friend to become a new subscriber of the Operator.
The recommendation itself, as well as the subscription is incentivised for both, the subscriber
and the recommending person.
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Householding / Family identification
a)
Identify « same household »
relationships
– Construct probable household units
•
•
–
b)
Identify probable decider(s)
When multiple SIM cards purchased by
same person, identify that other family
members are using Sims
–

Identify onnet penetration
Identify competitor position
Age-related calling patterns
Combination of a) and b)
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Community Identification and Marketing
Households / Families
a)
Seasonal workers
b)
SMEs
c)
Students
d)
Schoolchildren
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Customer Lifestage analysis
Analysis based on identifying critical life stage events using social
network changes
a)
Going to University
b)
Moving
c)
Changing job
d)
Starting a relationship – Moving as a couple
e)
Imputing demographics
– Age related patterns in the social network
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Winback
Campaign Topic
Retention
Description
SNA offers an opportunity to detect potential churners earlier (possibly before they have
completely ceased all on-net activity) and also identifies the individuals who are likely to
have the best chance of persuading them to return. The aim is to use SNA to detect
potential churners during the process of leaving and motivate them to stay with the
Operator.
Current Approach:
New Approach
Active
Inactive
Churn detected
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Churn detected
Competitor Insights
a)
Tracking dynamic changes in social networks based on competitor marketing
activities
• Reaction and impact of mass market campaigns
• Introduction of new products and tariffs
• Network evolution
b)
Improved accuracy in estimating operator market share
• What does a competitor’s mass market campaigns do to the
market?
c)
Segmenting competitors’ subscribers
• Tracking segments based on selected SNA KPIs
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Other business applications
Facilitate Pre- to Post-Migration
Identify Rotational Churners, switching between operators
Identify Internal Churners
Better customer lifecycle management by tracking customer network
dynamics over his Lifecyle with the operator
–
Networks grow and change over time. This will influence how the operator
interacts with the customer
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Teradata Aster: See the Network
Understand connections among users and organizations
Challenges
Examples
• Large number of entities with rapidly
growing amount of data for each
• Connectivity changing constantly
Aster Data Value
•
SQL-MapReduce® function for Graph
Analysis eases and accelerates analysis
•
Ability to store and analyze massive
volumes of data about users and
connections
• High loading throughput and incremental
loading to bring new data into analysis
• Link analysis: predicting connections (among
people, products, etc.) that are likely to be of interest
by looking at known connections
• Influence analysis: identifying clusters and
influencers in social networks
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Teradata Aster References
Social Network & Relationship Analysis
Select Aster Data Customers in
Digital Marketing Optimization
Analysis of user behavior,
intent, and actions across
search, ad media and web
properties, in order to
drive increased ROI.
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