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A must-read for any company wanting to learn how to "do" social network analysis rather than run another software program, Social Network Analysis in Telecommunications shows how social network analysis, analytics, and marketing knowledge can be combined to create a positive customer experience within the telecommunications industry.
Social Network Analysis in Telecommunications explores:
The telecommunications environment
How telecommunications companies can effectively enhance their relationships with customers
The groundwork for defining social network analysis
Tools that can be used to address social network problems
Social network modeling
The key to any successful company is the relationship it builds with its customers. Essential reading for professionals eager to distinguish their products in the marketplace and successfully connect with customers, this timely book shows you how to get it done right, through social network analysis.
Foreword.
Preface.
Acknowledgments.
Part I: Foundation of Social Network Analysis.
Chapter 1: An Introduction to Social Network Analysis.
Evolution of Social Network Analysis.
Actor and Relations Building Social Networks.
Influence.
Structures of Social Networks.
Analyses of Social Networks.
Graph Theory.
A Note on Statistics and Social Network Data.
Summary.
Chapter 2: Formal Methods for Network Analysis.
A Graphical Approach.
Levels of Measurement
Summary.
Chapter 3: Theoretical Foundation.
Data for Social Network Analysis.
Identifying Populations, Samples, and Boundaries.
Modality and Levels of Analysis.
Correlating Nodes into the Network.
Scales of Measurement.
Summary.
Chapter 4: Measures of Power and Influence.
Types of Networks.
Degree Centrality.
Closeness Centrality.
Betweenness Centrality.
Freeman’s Approach to Binary Relations.
Summary.
Part II: Social Network Analysis Case Study.
Chapter 5: Telecommunications Environment.
New Challenges in Telecommunications Market.
Social Networks in Telecommunications' Environment.
Traditional Predictive Models Based on Artificial Neural Networks.
Combined Modeling Approach.
Feasible Action Plan Based on the Combined Approach.
Benefits from the Combined Modeling Approach.
Summary.
Chapter 6: Social Network Modeling.
Customer Influence Factor Modeling.
The Data Extraction Process for Social Network Analysis Modeling.
The Data Preparation Process for Social Network Analysis Modeling.
Computing the Basic Social Network Measures.
Computing the Customer Influence Factor.
Adjusting the Influence Factor According to the Past Events.
Summary.
Chapter 7: Assessing the Social Network Model.
Assessing the Customer Influence Factor Due to the Business Events.
Plotting the Social Network.
Establishing the Distance for the Customers Based on Similarity.
Summary.
Chapter 8: Evaluating the Business Results.
Correlation between the Customer Influence and the Past Events of Churn.
Correlation between the Customer Influence and the Past Events of Bundle Diffusion.
The Social Network’s Evolution in a Chain Process Perspective Considering. Business Events.
The Bundle Diffusion Process Analyzed over Time.
Enhanced Data Analysis Visualization.
Geographical Visualization Analysis.
Summary.
Chapter 9: Final Remarks for the Case Study.
Products and Service Choice.
Further Inferences and Future Works.
Summary.
Part III: SAS Capabilities for Social Network Analysis.
Chapter 10: Basic Statistics.
Descriptive Analysis.
Univariate Analysis.
Grouping Based on Relationship Similarities.
Summary.
Chapter 11: Overview of the Link Analysis Node.
Centrality Measures.
Result Analysis.
Summary.
Chapter 12: Visualization Capabilities for Social Network Analysis.
Network Visualization Workshop.
%DS2CONST Macro.
%DMLINKVW Macro.
Summary.
Chapter 13: A Note about OPTGRAPH.
Recognizing Groups inside Network.
Individual Measures for the Network.
Summary.
Bibliography.
About the Author.
Index.
Overview
A must-read for any company wanting to learn how to "do" social network analysis rather than run another software program, Social Network Analysis in Telecommunications shows how social network analysis, analytics, and marketing knowledge can be combined to create a positive ...