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Social NetworkSocial Network
Michel BruleyWA - Marketing Director
February 2012February 2012
Extract from various presentations: B Wellman, K Toyama, A Sharma, Teradata Aster, …
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Social NetworkSocial 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 GraphSociety 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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Little Boxes Networked IndividualismGlocalization
Social Network AnalysisSocial 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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ConnectionsConnections
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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DistanceDistance
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 & PrestigeSome Measures of Power & Prestige
Degree– Sum of connections from or to an actor
• Transitive weighted degreeAuthority, 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 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 ExampleNetwork Analysis Example
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Centrality: strategic positionsCentrality: strategic positionsDegree centrality:
Local attention
Beetweenness centrality:reveal broker
"A place for good ideas"
Closeness centrality: Capacity to communicate
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Social Network Analysis: what for?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 SNATangible Outcomes from SNA
Sell More
Preserving ExpertiseBetter Knowledge Sharing
Building Better Communities
More Innovation Competitive Intelligence
Organisational Re-structures that work
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Ways to use SNA to Manage Ways to use SNA to Manage ChurnChurn
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 collateralchurn
Prevent influentialchurn
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Cross-Sell and Technology Cross-Sell and Technology TransferTransfer
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 MemberAcquisition – 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 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 Householding / Family identificationidentification
a) Identify « same household » relationships– Construct probable household units
• Identify onnet penetration• Identify competitor position
– Identify probable decider(s)
b) 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 Community Identification and MarketingMarketing
Households / Families
a)Seasonal workers
b)SMEs
c)Students
d)Schoolchildren
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Customer Lifestage analysis Customer Lifestage analysis
Analysis based on identifying critical life stage events Analysis based on identifying critical life stage events using social network changesusing 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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WinbackWinback
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 tohave 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 Churn detected
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Competitor InsightsCompetitor 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 applicationsOther 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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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
Teradata Aster: See the NetworkTeradata Aster: See the NetworkUnderstand connections among users and organizations
Examples
• 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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Analysis of user behavior, intent, and actions across search, ad media and web properties, in order to drive increased ROI.
Teradata Aster ReferencesTeradata Aster References
Select Aster Data Customers in Digital Marketing Optimization
Social Network & Relationship Analysis