Harvesting Information Excellence

Information Excellence

2012 MAY Session

Mobile Social Network Analytics A deployment use case

Dr. Jay Bharatheesh SimhaCTOAbiba Technologies

Social Media measurement and ROI: marrying big social-media-data with

Business contextIn house use case

Dr. Shesha Shah, Social Media Marketing and Intelligence team DELL India DGA Team

Thank You

for hosting us today

Today’s Speakers

Dr. Jay B.Simha is Chief Technology Officer, ABIBA Systems, a Telecom BI & Analytics company based out of Bangalore.

He has over 15 years of experience in R&D, Business Intelligence and Analytics consulting. He has implemented large scale systems for telecom, BFSI and manufacturing industries in Business Intelligence and analytics.

Prior to this he worked on medical data analysis with Siemens, working on algorithm design and data analysis.

He holds a post graduate in Mechanical Engineering and Computer Science. He holds a Doctoral degree in Data Mining and Decision Support and Post Doctoral from Louisiana State University ,USA.

He is active in research and has interests in business visualization, predictive analytics and decision support. . He has so far published about 40 papers in international journals and conferences in the areas of business intelligence and analytics.

He has won numerous best paper awards in prestigious conferences, confirming the quality of work.

Dr. Jay Bharatheesh Simha

Mobile Social Network Analytics marrying Big data with social data for profitability

Jay B. Simha

Information trinity

What to make out of this data?

ACQUIRE

WALLET SHARE RETENTION

Analytics approaches

DOMAIN EXPERTS

SOCIAL NETWORKS

HYBRID ENSEMBLES

BEHAVIORAL MODELING

Domain Experts

RULE BASED

VERIFICATION DRIVEN

RULE COMBINATION

DIFFICULT

What Behavioral Analytics do?

ACQUIRE

WALLET SHARE RETENTION

What Behavioral Analytics do?

What customers do?

Social Networks

Who has more Power?

1

2

3

4

5

Pat

1

23

4

5

Chris

Social Networks

Social Networks – power metrics

Social Networks – Reachability metrics

Social Networks – Reachability metrics

Number of paths and distances

Social Networks – Message/effect transferability

Mobile Social Network Analytics

• A special case of SNA• Deals with observable relations• Contains potential information

of distinct social groups• Real BIG DATA, which needs

crunching at massive scales• RDBMS have limitations on

these scales ( 1TB+)

Mobile Social Network Analytics -Centrality

First order centrality

Second order centrality

MSNA – Typical Data Extraction

Mobile Social Network Analytics - Process

Domain based Statistical/Behavioural MSNA

Data size Medium Medium Large

Number of variables Small Large medium

Data types Mostly Demographic Mostly behavioural Mostly relations

Data granularity Quasi aggregate Aggregate Detailed

Segment profile Homogeneous Homogeneous Heterogeneous

Techniques Profiles RFM

k-means Decision trees Neural networks

Neighbours Ego networks Subgroups

Segment profile Homogeneous Homogeneous Heterogeneous

Information Individual with group Individual with group Group and Individual within group

Effectiveness Medium High High

Comparing approaches to analytics

Mobile Social Network Analytics

How Hybrid Modeling is used?

MSNA – A sample visualization

MSNA – Full sub base

MSNA – Acquisition

MSNA – Retention

MSNA – Retention (HVC)

MSNA – Wallet share

MSNA – Demo

THANK YOU

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