Social Network

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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
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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 web-graph
• 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
Organisational Re-structures that work
Sell More
Preserving Expertise
Better Knowledge Sharing
Building Better Communities
More Innovation
Competitive Intelligence
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Ways to use SNA to Manage Churn

• Reduce Collateral Churn
• Reactive
• Identify subscribers whose loyalty is threatened
  by churn around them
• Reduce Influential 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

Has churned
Prevent collateral churn
Prevent influential churn
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Cross-Sell and Technology Transfer

• 2 smartphone users around you ? smartphone
  affinity x 5 !!
• Leverage Collateral Adoption
• Reactive
• Identify subscribers whose affinity for products
  is increased due to adoption around them
  stimulate them
• Identify influencers for this adoption
• Proactive
• Identify subscribers who, should they adopt,
  would push a few friends to do the same

Adopted
Offer product
Push for adoption
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Acquisition Member gets Member
Campaign Topic
Acquire New Members
Description
One of an Operators major objectives is to keep
(or even extend) the market position. As the
main competitors are making ground by eg.
attractive tariffs or through theacquisition of
new retail partners, acquisition of new customers
becomes a very importantobjective. This
campaign format focuses on influencers in social
communities, who are most likely torecommend 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
subscriberand the recommending person.
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Householding / Family identification

• Identify  same household  relationships
• Construct probable household units
• Identify onnet penetration
• Identify competitor position
• Identify probable decider(s)
• When multiple SIM cards purchased by same person,
  identify that other family members are using Sims
• Age-related calling patterns
• Combination of a) and b)

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Community Identification and Marketing

• Households / Families
• Seasonal workers
• SMEs
• Students
• Schoolchildren

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Customer Lifestage analysis Analysis based on
identifying critical life stage events using
social network changes

• Going to University
• Moving
• Changing job
• Starting a relationship Moving as a couple
• 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
havecompletely 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
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Competitor Insights

• 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
• Improved accuracy in estimating operator market
  share
• What does a competitors mass market campaigns do
  to the market?
• 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
• 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

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Teradata Aster References
Social Network Relationship Analysis

• Analysis of user behavior, intent, and actions
  across search, ad media and web properties, in
  order to drive increased ROI.