big data: social network analysis

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www.decideo.fr/bruley Social Network Social Network Michel Bruley WA - Marketing Director February 2012 February 2012 Extract from various presentations: B Wellman, K Toyama, A Sharma, Teradata Aster, …

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Introduction to the big data social network analysis

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Page 1: Big Data: Social Network Analysis

www.decideo.fr/bruley

Social NetworkSocial Network

Michel BruleyWA - Marketing Director

February 2012February 2012

Extract from various presentations: B Wellman, K Toyama, A Sharma, Teradata Aster, …

Page 2: Big Data: Social Network Analysis

www.decideo.fr/bruley

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

Page 3: Big Data: Social Network Analysis

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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

Page 10: Big Data: Social Network Analysis

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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

Page 11: Big Data: Social Network Analysis

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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