Social Network Analysis in Telecommunications E-Book

Carlos Andre Reis Pinheiro

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 392

Veröffentlichungsjahr: 2011

Beschreibung

A timely look at effective use of social network analysis within the telecommunications industry to boost customer relationships The key to any successful company is the relationship that it builds with its customers. This book shows how social network analysis, analytics, and marketing knowledge can be combined to create a positive customer experience within the telecommunications industry. * Reveals how telecommunications companies can effectively enhance their relationships with customers * Provides the groundwork for defining social network analysis * Defines the tools that can be used to address social network problems A must-read for any professionals eager to distinguish their products in the marketplace, this book shows you how to get it done right, with social network analysis.

Leseprobe

Table of Contents

Cover

Series page

Title page

Copyright page

Dedication

Foreword

SOME PEOPLE ARE SPECIAL

THE CONNECTOR WHO CHANGED THE COURSE OF HISTORY

COMMUNICATIONS SERVICE PROVIDERS AND THE TIPPING POINT

THE ROLE OF MAVENS IN SOCIAL NETWORKS

Preface

Acknowledgments

Part I: Foundation of Social Network Analysis

CHAPTER 1 An Introduction to Social Network Analysis

EVOLUTION OF SOCIAL NETWORK ANALYSIS

BUILDING SOCIAL NETWORKS BASED ON NODES AND LINKS

INFLUENCE

STRUCTURES OF SOCIAL NETWORKS

ANALYSES APPROACH FOR SOCIAL NETWORKS

GRAPH THEORY AND SOCIAL NETWORK ANALYSIS

STATISTICS AND SOCIAL NETWORK ANALYSIS

SUMMARY

CHAPTER 2 Formal Methods for Network Analysis

A GRAPHICAL APPROACH FOR SOCIAL NETWORK ANALYSIS

LEVELS OF MEASUREMENT FOR SOCIAL RELATIONS

SUMMARY

CHAPTER 3 Theoretical Foundation

TYPE OF DATA FOR SOCIAL NETWORK ANALYSIS

IDENTIFYING NODES AND LINKS WITHIN SOCIAL NETWORKS

MODALITY AND LEVELS OF ANALYSIS

CORRELATING NODES WITHIN THE NETWORK

SCALES OF MEASUREMENT

SUMMARY

CHAPTER 4 Measures of Power and Influence

TYPES OF NETWORKS

MEASURING POWER BY DEGREE CENTRALITY

ADDITIONAL LEVELS FOR THE DEGREE OF CENTRALITY

MEASURING POWER BY CLOSENESS CENTRALITY

EIGENVECTOR

MEASURING THE POWER BY BETWEENNESS CENTRALITY

SUMMARY

Part II: Social Network Analysis Case Study

CHAPTER 5 Telecommunications Environment

NEW CHALLENGES IN THE TELECOMMUNICATIONS MARKET

SOCIAL NETWORKS IN THE 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

DATA EXTRACTION PROCESS FOR SOCIAL NETWORK ANALYSIS MODELING

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 PAST EVENTS

SUMMARY

CHAPTER 7 Assessing the Social Network Model

ASSESSING THE CUSTOMER-INFLUENCE FACTOR DUE TO BUSINESS EVENTS

PLOTTING THE SOCIAL NETWORK

ESTABLISHING THE DISTANCE FOR CUSTOMERS BASED ON SIMILARITY

SUMMARY

CHAPTER 8 Evaluating the Business Results

CORRELATION BETWEEN CUSTOMER INFLUENCE AND PAST EVENTS OF CHURN

CORRELATION BETWEEN CUSTOMER INFLUENCE AND PAST EVENTS OF BUNDLE DIFFUSION

THE SOCIAL NETWORK’S EVOLUTION IN A CHAIN PROCESS PERSPECTIVE USING BUSINESS EVENTS

THE BUNDLE DIFFUSION PROCESS ANALYZED OVER TIME

ENHANCED DATA ANALYSIS VISUALIZATION

GEOGRAPHIC 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

GROUPING BASED ON RELATIONSHIP SIMILARITIES

SUMMARY

CHAPTER 11 Overview of the Link Analysis Node

DEFINING NODES AND LINKS

USING LINK ANALYSIS MACROS TO CALCULATE THE NETWORK MEASURES

CENTRALITY MEASURES

RESULT ANALYSIS

SUMMARY

CHAPTER 12 Visualization Capabilities for Social Network Analysis

NETWORK VISUALIZATION WORKSHOP

NETWORK GRAPHS

DS2CONST MACRO

SUMMARY

CHAPTER 13 A Note about OPTGRAPH

RECOGNIZING GROUPS INSIDE A NETWORK

INDIVIDUAL MEASURES FOR THE NETWORK

SUMMARY

Bibliography

About the Author

Index

Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

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For more information on any of the above titles, please visit www.wiley.com.

Copyright © 2011 by SAS Institute Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Reis Pinheiro, Carlos Andre, 1940-

 Social network analysis in telecommunications / Carlos Andre Reis Pinheiro.

p. cm. – (Wiley and SAS business series)

 Includes index.

 ISBN 978-0-470-64754-7 (hardback); 978-1-118-01077-8 (ebk); 978-1-118-01094-5 (ebk); 978-1-118-01095-2 (ebk)

 1. Telecommunication–Customer services. 2. Social networks. 3. Customer relations–Technological innovations. I. Title.

 HE7631.R45 2011

 384.068’8–dc22

2010039895

This book is dedicated to my small but crucial social network, which supports me unconditionally. This small social network has been serving as my inspiration to do everything in my life. This book is dedicated to my wife Daniele, my son Lucas, and my daughter Maitê.

Foreword

SOME PEOPLE ARE SPECIAL

Large communications service providers lose some customers every month and they work hard to keep this number as low as possible. In fact, most have become quite good at understanding the causes of customer churn and improving customer service to minimize the things that make customers leave. Of course, the competition has also improved at creating offers to attract your customers away. This healthy competition is good for consumers and is the source of great innovations, but it forces even the best service providers to constantly ask, “How can we do better?”

One way to do better is to pay attention to special people–connectors. So who are these connectors and what makes them so special?

THE CONNECTOR WHO CHANGED THE COURSE OF HISTORY

Tensions ran high between American colonists and British soldiers in the spring of 1775. On the morning of April 19th, a few hundred British soldiers set off from Boston to capture a cache of arms and arrest rebel leaders. Marching north into the small towns of Lexington and Concord, the British were astonished to encounter fierce and well organized resistance. Soundly beaten, they beat a hasty retreat back to Boston under constant harassment from colonial militia. The nascent rebellion was given a huge boost in confidence. Thus began the war for American Independence from Great Brittan.

How the colonial militia was notified in time and able to assemble ahead of the advancing British troops is a story well known to American schoolchildren, many of whom have read or perhaps even memorized the Henry Wadsworth Longfellow poem called “The Midnight Ride of Paul Revere,” Revere rode his horse through the night alerting militia men of the approaching army. Hundreds quickly turned up ready to fight the heavily armed British troops.

A young man named William Dawes also rode out of Boston the same night with the same message. Starting earlier than Revere, and riding through more populous towns along a more westerly route, Dawes could have been expected to arouse even more rebels. He didn’t. The few militia men who responded to Dawes turned up too late to be of much use.

Paul Revere is an essential figure in American History. William Dawes is but a footnote. Both men carried the same message into towns where the people had equal motivations. One started a word-of-mouth epidemic, the other was mostly ignored. But why? Malcolm Gladwell proposes an answer to this question in his 1999 book The Tipping Point: How Little Things Can Make a Big Difference. According to Gladwell, Paul Revere was a special type of person—a connector.

William Dawes was a shoemaker—age 26. He rode through towns knocking on random doors and shouting that the British were coming. But people in these towns didn’t know who he was, didn’t have confidence that his message represented an immediate threat. They didn’t see their neighbors doing anything so they went back to bed, figuring they would check it out in the morning. Paul Revere on the other hand was well known. He was a successful businessman who at age 40, had been at the center of events in and around Boston for years. When Paul Revere rode into a town he didn’t knock on a random door but went straight to the local militia commanders. Opening their doors in the middle of the night and recognizing Paul Revere on the doorstep, they sprung into action, alerted their neighbors, and spread the word. Revere alerted enough of the right people, so the alarm reached the tipping point.

COMMUNICATIONS SERVICE PROVIDERS AND THE TIPPING POINT

This story is a classic example of how connectors are essential to get an idea to tip. But what can this history lesson from 235 years ago tell us about today’s fast-moving high-technology marketplace? Instead of riding around on horses shouting to each other, we use mobile phones, e-mail, and social network sites. But these special people, the connectors, are still with us. The technology may have changed but connectors still influence our behavior and they are worth paying attention to.

Connectors know lots of people, many times the number of people the average person knows. But, they also know essential facts about people. Chances are you know someone like this, someone that seems to know everyone and stops to talk wherever they go, but it isn’t all small talk. Connectors know what to talk about with the people they meet. If you tell a connector about your hobby they can give you the names of several people who share that interest. Tell them about a problem you have and they will give you the names of people who can be helpful. Tell them about a great new product and a connector will spread the word. Research has shown that most people have at one time or another found a job because a connector put them in contact with someone they did not know who helped them get the job. Connectors play a vital role in our social networks.

The impact of connectors to a small neighborhood business is quite obvious. A local restaurant owner would obviously want to give his best table to a customer who is very influential in the neighborhood while the lone traveling businessman will likely find himself seated near the kitchen. I bet tavern owners in colonial Boston bent over backward to make sure Paul Revere had a good table, a well-prepared meal, and excellent service. The smart tavern owner would realize that he had more customers on nights when Paul Revere stopped in, he observed other customers saying hello to Paul and asking him to please join them. People stayed longer and spent more money when Paul was around. Then as now, owning a tavern is high risk business venture and the presence of one man like Paul Revere can determine if the business will tip toward profitability.

A communications service provider is not a local tavern. The managers can’t personally know 10 million customers. Yet the impact of connectors can be vital clues to what others will be doing in the future. Lose a connector to a competitor and next quarter your churn numbers may shoot up. Entice a connector away from your competitor and others may follow. Have a new service, get the word to a connector and see how fast it spreads.

But how does a communications service provider know that someone is a connector? You can’t just put a check box on the service application that says, “Are you a connector, yes or no?” Connector is not a binary status like gender. Sure connectors could be expected to be heavy service users, but just adding up call minutes and numbers of messages is not a good indicator, because these calls and messages may be only a small number of people. Looking at the number of people someone calls won’t help either. A taxi driver may speak with hundreds of people a month, but if he’s just arranging pick-ups, he’s not a connector.

THE ROLE OF MAVENS IN SOCIAL NETWORKS

The ideas connectors spread are not always their own. Paul Revere didn’t see the British troops on the move before setting off to spread the word. A stable boy overheard a conversation between British officers. Other people saw an unusual amount of activity on British ships and around their barracks. They knew exactly where to go with this news, to the home of Paul Revere. Connectors don’t just indiscriminately spread the word, they consolidate ideas, filter the important from the trivial, and think about who would want to know that idea.

I attended a wireless industry conference last year and met a man named Michael Gartenberg. To say that Michael is talkative would be an understatement. He has a lot to say, but he is not interested in small talk or talking about himself. He asked me a lot of questions to find out what I was interested in. As he learned more about me he started offering me all kinds of suggestions. Easily switching between his three handsets, he showed me dozens of applications and could talk in depth about how they worked on different platforms. Michael is a geek, no doubt about it, but a special kind of geek—a maven. Mavens, Malcolm Gladwell says, are one of the other types of people who contribute to tipping points.

Michael Gartenberg is very smart and loves to play with his gadgets. He knows a lot about the industry and has a widely read blog. He makes his living as a consultant, but never asked me about getting consulting business from SAS. From my perspective, his motivation seemed to be more about helping me than impressing me. According to Gladwell mavens combine intense curiosity with a passion to spread their knowledge in a way that is helpful.

By paying attention to mavens we can spot new ideas. Mavens share those ideas with connectors, and by watching connectors we can see which ideas become trends and spread through a social network. If you have a new product or service, show it first to a maven who will help you improve it. If a maven likes it, he will pass it along to connectors and this new idea may be one of the few that reach the tipping point.

The author of this book is a maven. I have had the pleasure of meeting Dr. Pinheiro at several SAS events and he is a classic example of Gladwell’s definition. By leading analytic projects in a broad range of subjects, including customer segmentation, churn prediction, payment risk, customer profitability, and revenue assurance, Dr. Pinheiro has become one of the world’s top experts in telecommunications data and analytical models. His willingness to share his knowledge has benefitted many SAS® users. Dr. Pinheiro’s presentation at SAS forums are always well attended and highly rated.

Among our telecommunications industry customers, social network analysis is one of the most frequently discussed topics. A recent survey of telecommunications service providers by industry analyst firm The Yankee Group validates that social network analysis is one of their top areas for new investment.

It is with great pleasure that I am able to introduce this book, which will guide SAS users on every aspect of social network analysis, from theory to practice. By applying the principles and examples laid out by Dr. Pinheiro, your attempts to increase profitability will reach a tipping point.

Ken King, Product Manager, SAS Institute Inc.

Preface

Traditional methods and techniques for identifying patterns in business data—such as K-means, self-organizing maps of Kohonen, and others—can achieve good results. However, they are usually focused on identifying individuals’ behaviors. They are based on a set of information about individuals and are clustered according to similar characteristics. All behaviors are placed in one of those cluster groups, so presumably individuals included in each group have similar characteristics and behaviors. However, as with any analytical model, it is an approximation of similar behaviors. Each group holds a particular average behavior. Each observation inside the group holds its own behavior. Some observations have behavior similar to the average group, some do not. By being aware that a clustering model is an approximation, any practical action should consider, for instance, the closest observations to the center of each cluster, assuring that these observations hold behavior similar to the cluster. Some particular clusters exhibit an average behavior. Some observations (customers) are close to this behavior, some are not. This is the approximation. The closer to the cluster’s center the observations are, the closer their individual behavior will be to the average cluster.

Social network analysis is also an approximation, but about the group rather than about the individual. The characteristics that are taken into consideration are not ones assigned to the individuals, but rather to the groups’ behaviors and the connections’ attributes. The main objective is to gather knowledge, not about the individuals, but about the group in which these individuals are set. More important than the individual’s characteristics is the group behavior, how the individuals inside a group relate to each other. In most cases the relationships among the individuals are the key to recognizing the group’s behavior. The individuals’ attributes are nothing more than additional information about the nodes inside a network. The links assigned to the network reveal the group’s behavior and the features of the community.

Attributes in relation to nodes and links depict the network structure and the knowledge of communities inside the entire network. This approach allows companies to understand beyond the customers, recognizing the relationships, and, hence, identifying the influential nodes, the ones that marketing campaigns should target.

The first part of this book describes the theory of social network analysis, presenting a sociological perspective, where this methodology started, but most important, the mathematical foundations that allow us to create metrics and compute measures to analyze the network in practice. Distinct network measurements and methods of calculation are discussed in this first part. The majority of the theoretical foundations presented in this book are based on the work of professors Robert A. Hanneman and Mark Riddle, of the Department of Sociology at the University of California, Riverside.

This first part also covers the concept of social network analysis, drawn from sociological studies. A theoretical foundation, based primarily on the graph theory, is presented in order to establish a base for the concept of social networks, the possible measures, and the structures assigned to the social networks in general. Formal methods used for the presentation and analysis of social networks are covered in Part II. Methods used to measure social networks are presented, depicting the algorithms and the methods used to compute the most relevant measures of centrality and power.

Part II of this book presents a case study, in which the concepts covered in Part I, through the use of SAS® capabilities, were used to distinguish results in terms of business challenges. The methodology used for social network analysis was applied to a telecommunications company in order to understand the customers’ relationship, and, hence, to identify influential customers from the perspective of distinct businesses. Two different business issues were addressed by using social network analysis to prove the value of this approach to telecommunications. The first issue dealt with the recognition of influential customers in terms of churn events, and, therefore, identification of the crucial customers who should be retained. The second approach dealt with bundle diffusion, identifying the central customers who should be targeted by marketing campaigns in order to spread the bundle quickly and effectively through the network.

This case study was elaborated, developed, and deployed during postdoctoral research at Dublin City University. This research was accomplished inside the Business Informatics Group at the School of Computing, under the direction of Dr. Markus Helfert.

Part III of this book presents the SAS capabilities used to address the challenge of social network analysis. Different modules of SAS solutions can be deployed to analyze social networks, compute measurements, calculate scores, identify communities, and draw the network structure. SAS® Enterprise MinerTM features the Link Analysis node, which provides a wide range of functionality to create and analyze the relationship among the nodes according to their links. Macros related to the Link Analysis node will be described in an isolated way, presenting a distinct approach to SAS capabilities when used to analyze social networks. Multidimensional scaling, used to establish coordinates based on the distance among the nodes will be shown as well, as an alternative method of identifying clusters of nodes based on the strength of their relationships. Some additional features to plot a network structure will be presented, such as using the macro %ds2const, which draws different types of networks.

In Part III, basic statistics are presented as a complementary way to analyze social networks. Statistical processes used to sort, create frequencies, identify extreme values, compute coordinates based on multidimensional scale and clustering nodes, and link connected components and communities are also presented at the end of Part III. The basic task of social network analysis is computing network measures. A more complex task is to analyze the network measures upon a particular business perspective. The measurements of nodes and links should be compared according to a particular target, highlighting the influential nodes, the strong links, the leaders of the network, as well as the followers. The analysis of the network measurements reveals the main characteristics of the social network and makes it possible to understand it from a business point of view.

Acknowledgments

I would like to thank everyone who assisted me in the production of this book. The actions of many people from all over the world were instrumental to the success of this project, especially the support of my family, discussions with my colleagues, and kindness from my friends.

A special thanks to my supervisor Dr. Markus Helfert, who led me through eighteen months of research during a postdoctoral term at Dublin City University. Dr. Markus leads the Business Informatics Group at the School of Computing where a tremendous amount of research in data management and analytics takes place. Thanks also to all members of the Business Informatics Group for sharing ideas and discussing projects and applications with no objections.

Many thanks to James Davey, Gavin Kiernan, and Brian Buckley for making available all telecommunications data that supported this research, and, most important, for sharing such valuable knowledge during this study.

Also thanks to my colleagues from SAS Ireland, John Farrelly, Eamonn McQuaid, Eoin Byrne, John Curran, Karl Langan, Sarunas Tankeliavicius, Kielty Hughes, Alan Gormley, David Ferguson, Martin Duffy, Turlough Fitzpatrick, Paul Power, John Lyons, Julianne Purcell, and Linda Moran. Special thanks to John Curran, Karl, and Eoin, who shared their very good knowledge of analytics.

Very special thanks to SAS Press, particularly to Stacey Hamilton and Shelley Sessoms, for helping me in this endeavor. This book would not have been possible without the unique involvement of Stacey. Thanks also to Stacey Rivera from Wiley for conducting such a rigorous review process.

Thanks to Ken King for his knowledge of telecommunications, analytics, SAS® software, and many other things. Also thanks for the great foreword and the amazing story.

Over these 18 months of research at Dublin City University, my family and I had the privilege of sharing great moments with our extended family and friends. We realized that in terms of a social network, the following group of people became very important to build our own. I really appreciate the kindness and friendship from all our “visitors.” We certainly have built the best social network ever. Thus, many thanks to my mother Suely; to Alex and Irma; to my sister Joseanne and my brother-in-law Gustavo; to my father-in-law Ivan and my mother-in-law Ana; to my “brother” Andre Brugger; to Rita and Sonia; to Claudio and Daniela; to Marco Aurelio and Rosane; to Marcelo and Patricia; to Michele and Daniel; and finally to Graziela and Eduardo. This is the sort of social network that never ends.

Part I: Foundation of Social Network Analysis

The first part of this book presents the basic theories of social network analysis. Chapter 1 introduces the foundation of this social science and the approaches to analyze social relations. Chapter 2 presents the formal theory and the most important concepts of social network analysis. Chapter 3 describes the formal methods to analyze social networks. Finally, Chapter 4 presents the most relevant measurements of social network analysis, such as degree, power, and influence.