Social Networks and their Economics E-Book

Daniel Birke

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2013

Seitenzahl: 363

Beschreibung

Reveals how consumer choice can be better understood and influenced using social networks analysis (SNA) Intuitively, we all appreciate that we can be influenced by our friends and peers in what we do, how we behave, and what products we consume. Until recently, it has been difficult to measure this interdependence, mainly because data on social networks was difficult to collect and not readily available. More and more companies such as mobile phone carriers or social networking sites such as Facebook are collecting such data electronically. Daniel Birke illustrates in compelling real-world case studies how companies use social networks for marketing purposes and which statistical analysis and unique datasets can be used. Social Networks and their Economics: * Explores network effects and the analysis of social networks, whilst providing an overview of the state-of-the art research. * Looks at consumption interdependences between friends and peers: Who is influencing who through which channels and to what degree? * Presents statistical methods and research techniques that can be used in the analysis of social networks. * Examines SNA and its practical application for marketing purposes. * Features a supporting website href="http://www.wiley.com/go/social_networks">www.wiley.com/go/social_networks featuring SNA visualizations and business case studies. Aimed at post-graduate students involved in social network analysis, industrial economics, innovation and consumer marketing, this book offers a unique perspective from both an academic and practitioner point of view on how social networks can help understand and influence consumer behaviour. This book will prove to be a useful resource for marketing practitioners from companies where social network data is available and for consulting companies who advise businesses on marketing and social media related issues.

Leseprobe

Contents

Cover

Title Page

Copyright

List of figures

List of tables

Preface

Acknowledgements

Chapter 1: Consumer choice in social networks

1.1 Motivation

1.2 Using mobile telecommunications to illustrate the economics of social networks

1.3 Structure of the book

1.4 Why you should read this book

References

Chapter 2: Research into social networks in economics, sociology and physics

2.1 Introduction

2.2 The economics of networks: Key findings from economics and marketing

2.3 Social network analysis: Key findings from sociology

2.4 Key findings from physics research into complex networks

2.5 Empirical research on social networks and network effects

2.6 Summary

References

Chapter 3: Marketing in social networks: The iPhone

3.1 Executive summary

3.2 Case study 1: Social network and viral marketing

3.3 Case study 2: Social advertising on Facebook

3.4 Introduction to the empirical study

3.5 Product diffusion in social networks

3.6 Modelling diffusion in social networks

3.7 Model estimation

3.8 Model results

3.9 Discussion

References

Chapter 4: Switching and churn in social networks

4.1 Executive summary

4.2 Case study: Customer retention in social networks

4.3 Introduction to the empirical study

4.4 Key findings from the switching cost literature

4.5 Modelling concept

4.6 Description of the data used: Another large-scale mobile network

4.7 Model results

4.8 Discussion

References

Chapter 5: How social networks influence consumer choice of mobile phone carriers in the UK, Europe and Asia

5.1 Executive summary

5.2 Case study: Using homophily for social network marketing

5.3 Introduction to the empirical study

5.4 Methodology

5.5 Understanding the properties of the social networks

5.6 The impact of friendship on operator choice

5.7 Robustness of results

5.8 Are stronger relationships more influential?

5.9 Friendship networks and choice of handset brand

5.10 Multi-country case study of operator choice in social networks

5.11 Discussion

References

Chapter 6: Coordination of mobile operator choice within households

6.1 Executive summary

6.2 Case study: Social network marketing to communities

6.3 Introduction to the empirical study

6.4 Data

6.5 Descriptive statistics

6.6 The model

6.7 Multinomial logit model

6.8 Multinomial probit model

6.9 Discussion

References

Chapter 7: How pricing strategy influences consumer behaviour in social networks

7.1 Executive summary

7.2 Case study: Pricing digital products with network effects

7.3 Introduction to the empirical study

7.4 The mobile telecommunications industry in the UK

7.5 The model: Price discrimination between on- and off-net calls

7.6 Estimation results: Adapting consumption choice to price signals

7.7 Discussion

References

Chapter 8: Conclusions

8.1 Main results

8.2 Implications of interdependent consumer choice

8.3 Looking ahead: How social network analysis is changing research and marketing practice

References

Appendix A: Success factors for viral marketing campaigns

A.1 Proposition excellence

A.2 Observability of the product or its use

A.3 Design the campaign around a good understanding of the specific role of word-of-mouth in propagating your product

A.4 Word-of-mouth for economic benefit

A.5 Exploit storytelling and tap into the zeitgeist

A.6 Exploit influential expert users

A.7 Appeal to communities of interest

A.8 Conclusion

References

Appendix B: Student questionnaire

Index

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Library of Congress Cataloging-in-Publication Data

Birke, Daniel.  Social networks and their economics : influencing consumer choice / Dr Daniel Birke.   pages cm  Includes bibliographical references and index.  ISBN 978-1-118-45765-8 (cloth) 1. Social networks--Economic aspects. 2. Consumer behavior. I. Title.  HM741.B57 2013  658.8'34--dc23

2013017209

A catalogue record for this book is available from the British Library.

ISBN: 978-1-118-45765-8

List of figures

2.1 Overlapping social networks.

2.2 Technology diffusion in markets with network effects.

2.3 Increase in the use of ‘social networks’ in the title of academic publications by publication year.

2.4 A sociogram with undirected (a) and directed relationships (b).

2.5 Examples of transitive (a) and intransitive ties (b).

2.6 Sociogram of an undirected graph (a) and a direct graph (b).

2.7 Transition from a regular (a) to a random (c) graph via a small-world graph (b).

2.8 Penetration of social networking on smartphones.

2.9 (a) Outside-in and (b) inside-out approach for measuring network influence.

3.1 Social network pressure and influence.

3.2 Applications of social network analysis across the customer life cycle.

3.3 Spreading of new products in networks.

3.4 Two-stage adoption process.

3.5 Time structure of the model.

3.6 Duration data. Reprinted by permission of Taylor & Francis Ltd.

3.7 Correlation of iPhone purchases over time.

3.8 Level of iPhone virality over time.

3.9 iPhone diffusion speed over time.

4.1 Illustrative benefits of combining traditional churn models with social network model.

4.2 Illustrative results from prediction of churn influencers.

5.1 Illustrative levels of handset homophily. To preserve the confidentiality and anonymity of the mobile phone carriers, the figures in the table are illustrative only, but based on typically observed values from analysing data from a variety of mobile phone carriers.

5.2 Predicting user characteristics in a social network. (Please see plate section for color version of the figure.)

5.3 UK 2005: Student class social network. (Please see plate section for color version of the figure.)

5.4 UK 2005: Full student class social network. Reproduced by permission of Elsevier. (Please see plate section for color version of the figure.)

5.5 UK 2005: Nationality and ethnicity of students. Reproduced by permission of Elsevier. (Please see plate section for color version of the figure.)

5.6 UK 2006: Student class social network. Reproduced by permission of Elsevier. (Please see plate section for color version of the figure.)

5.7 Malaysia: Student class social network. Reproduced by permission of Elsevier. (Please see plate section for color version of the figure.)

5.8 The Netherlands: Student class social network. Reproduced by permission of Elsevier. (Please see plate section for color version of the figure.)

5.9 Italy: Student class social network. Reproduced by permission of Elsevier. (Please see plate section for color version of the figure.)

6.1 Typical family communications patterns.

7.1 Number of mobile phone subscribers in the UK (in thousands).

7.2 Development of subscriber market shares.

7.3 Development of on- and off-net call volumes.

7.4 Price and volume ratios between off- and on-net calls.

7.5 Fitted and observed values for off-/on-net calls.

A.1 Personalised Nokia handsets.

List of tables

3.1 Example of survival analysis applications.

3.2 iPhone uptake rate by number of ‘infected’ neighbours.

3.3 Definition of variables.

3.4 Regression results from log-normal base model.

4.1 Churn and iPhone uptake rate by number of ‘infected’ neighbours. Reprinted by permission of Taylor & Francis Ltd.

4.2 Definition of variables.

4.3 Regression results from log-normal base model.

4.4 Regression results from survival analysis model. Reprinted by permission of Taylor & Francis Ltd.

5.1 Customer-related knowledge by industry.

5.2 Permutation of rows and columns (QAP).

5.3 Nationality and gender of respondents.

5.4 Out-degree and nationality.

5.5 Frequencies for choice criteria.

5.6 Do you know which operator your friends/family/partner uses?

5.7 Duration of mobile phone usage per week.

5.8 Number of SMS sent per week.

5.9 Mixing patterns between students from different nationalities.

5.10 Determinants of choosing the same operator (UK 2005).

5.11 Calculation of operator coordination measure.

5.12 Degree of coordination (UK 2005) by operator.

5.13 Degree of coordination (UK 2005) by nationality.

5.14 Friendship determinants.

5.15 Predicted probabilities of calling each other.

5.16 Non-respondents and nationality.

5.17 Non-respondents and gender.

5.18 Non-respondents and operator choice.

5.19 Regression results from robustness checks.

5.20 Determinants of choosing the same operator (UK 2006).

5.21 Determinants of mobile handset choice.

5.22 Handset choice by operator (expected values in brackets).

5.23 Sample size and response rates.

5.24 Do you know which operator your friends/family/partner use?

5.25 Determinants of choosing the same operator (The Netherlands).

5.26 Determinants of choosing the same operator (Italy).

5.27 Operators chosen when respondents have multiple operators.

5.28 Degree of coordination by operator.

5.29 Operator coordination between respondents and fathers (expected figures in brackets).

5.30 Operator coordination between respondents and mothers (expected figures in brackets).

5.31 Operator coordination between respondents and siblings (expected figures in brackets).

5.32 Operator coordination between respondents and partners (expected figures in brackets).

5.33 Degree of coordination in different countries.

5.34 Observed versus expected percentage of same operator dyads amongst friends.

6.1 Number of respondents.

6.2 Survey participation of wave 3 respondents.

6.3 Observed number of operators per household (wave 3). With kind permission of Springer Science+Business Media.

6.4 Expected number of operators per household (wave 3). With kind permission of Springer Science+Business Media.

6.5 Determinants of operator choice (MNL model). With kind permission of Springer Science+Business Media.

6.6 Predicted probabilities of operator choice (MNL model). With kind permission of Springer Science+Business Media.

6.7 Coordination of operator choice by type of relationship.

6.8 Hausman-test results for IIA assumption.

6.9 Determinants of operator choice (MNP model).

6.10 Predicted probabilities of operator choice (MNP model).

7.1 Observed shares (by volume of calls). With kind permission of Springer Science+Business Media.

7.2 Expected shares (by volume of calls). With kind permission of Springer Science+Business Media.

7.3 Expected shares (by volume of calls) second quarter 1999. With kind permission of Springer Science+Business Media.

7.4 Regression results for off-/on-net call volumes. With kind permission of Springer Science+Business Media.

A.1 Is your product / campaign suitable for word-of-mouth marketing?

Preface

To understand what influences consumers in their purchasing decisions has been at the heart of marketing for decades. Intuitively, everybody understands that purchasing decisions are based on our own individual preferences and that we are at the same time influenced by our friends and peers in what we do, how we behave and what products we consume. However, until recently, it was difficult to measure this interdependence, mainly because data on social networks were difficult to collect and not readily available. Nowadays, more and more companies, like mobile phone companies or social networking sites like Facebook, collect such data electronically. There is, therefore, a strong academic and practitioner interest in measuring how consumers are influenced by their social network in the products they consume.

This book uses the author's unique experience in carrying out academic research on consumer choice in social networks, starting up a company that successfully commercialised these insights and working in a top-management consultancy advising companies on Marketing and Sales. It is relevant for both an academic and a practitioner audience:

From an academic perspective, the book is most relevant to final year undergraduate, postgraduate and university researchers in industrial economics and consumer marketing. Each chapter uses different empirical studies demonstrating how consumption interdependences can be measured. A number of different research techniques (primary and secondary surveys, electronic data collection) and different statistical techniques (survival analysis, multinomial logit, time-series statistics, permutation tests) are used. The case studies and related questions can be used in the class room.The book is also directly relevant for marketers interested in how to turn social network data into actionable insights and campaigns. Based on the author's experience working together with a large number of marketing and sales departments, each chapter starts with an executive summary of relevant aspects from a practitioner point of view. Furthermore, each chapter is preceded by a case study discussing practical implications of the research in areas such as social network marketing, retention, pricing strategy and so on. For example, Chapter 4 on how switching of mobile phone providers is influenced by one's peers, is preceded by a case study on how several mobile phone providers are using these insights to reduce customer churn among their subscribers. Appendix A includes a discussion of the success factors for viral marketing campaigns.

This book mainly covers the following topics:

Network effects and the analysis of social networks: Overview of the state-of-the art research.Consumption interdependences between friends and peers: Who is influencing whom through which channels and to what degree?Statistical methods and research techniques that can be used in the analysis of social networks.Social network analysis and its practical application for marketing purposes.

This book contains an accompanying website. Please visit www.wiley.com/go/social_networks

Acknowledgements

I am very grateful to my wife Yundan and my children for coping with their husband/daddy locking himself in the office to write this book. To them I am dedicating this book.

This book has benefited from a number of people and I am very grateful for this help and support. First and foremost I would like to thank my PhD advisor Peter Swann who supported me from the first meetings at Manchester Business School, through meetings at Bridgewater Hall to the award of my PhD at Nottingham University Business School, and since then as a very good friend. I in particular enjoyed the stimulating discussions which helped me not only to write my PhD thesis, but to understand what is needed to become a good academic.

Towards the end of my PhD in 2006 I started with Idiro Technologies, a software company specialising in analysing very large social networks in order to derive marketing recommendations. I had four fantastic years with Idiro and thoroughly enjoyed being able to translate my PhD research into practical use and being able to work with our customers on combining the model predictions with the other elements of successful marketing campaigns. I am in particular grateful to Aidan Connolly, Brendan Casey and my team members. A special thanks goes to Simon Rees, Sales & Marketing Director of Idiro Technologies for his deep insights into the mobile telecommunications industry (and many great nights in Istanbul!). Simon also contributed the discussion of the success factors for viral marketing campaigns in Appendix A which is a great reference resource for organizations who want to run a viral marketing campaign. Thank you as well to Robert Walker from Ernst & Young's Customer practice who enabled me to take a three months sabbatical to write this book.

I would also in particular like to thank John Belchamber, Ricardo Correia, Paul David, Chris Easingwood, Nicolas Economides, Koen Frenken, Sourafel Girma, Gautam Gowrisankaran, Francesco Lissoni, David Paton, Roy Radner, Paul Stoneman, Arun Sundararajan, Steve Thompson, Reinhilde Veugelers and many others who gave me helpful comments and suggestions.

This book also would not have been possible without the extensive access to data that I was able to gain from a number of sources. I would like to thank Idiro Technologies and two mobile phone companies that shall remain anonymous for providing me access to the data for Chapters 3 and 4. By enabling and supporting me to run surveys with their students at the University of Utrecht, University of Nottingham in Malaysia and at the University of Brescia, Koen Frenken, Yoong Hon Lee and Francesco Lissoni made the data collection for Chapter 5 possible. Ben Anderson from Chimera, the Institute for Social and Economic Research at the University of Essex, the ESRC data archive and Nicoletta Corrocher helped me with data for Chapter 6. Last but not least, I would like to thank Hilary Anderson from OFCOM who granted me access to the data on which Chapter 7 is based.

I would also like to gratefully acknowledge financial support from Nottingham University Business School and the ESRC, which allowed me to focus on my research during my PhD years.

Last but not least I would like to thank my publisher Wiley & Sons and their team for shepherding and guiding me through the publication process, in particular Richard Davies, Heather Kay, Debbie Jupe, Ilaria Meliconi, Paulina Shirley and Jo Taylor.

Daniel Birke

1

Consumer choice in social networks

1.1 Motivation

1.2 Using mobile telecommunications to illustrate the economics of social networks

1.3 Structure of the book

1.4 Why you should read this book

References

1.1 Motivation

The basic conjecture of this book is that consumers do not make decisions in isolation, but are influenced by and influence other consumers with whom they interact. Everyday experience suggests that we are frequently influenced by others: we ask our peers for restaurant tips, hear about new products from them, make joint consumption decisions for family cars within families, consume similar products to our peers in order to ‘keep up with the Joneses’ and use similar products as people we regard highly and aspire to. These processes happen within social networks, which in this book means all social relationships between people. In recent years, social networks such as Facebook have become very popular. Thinking about ones social relationships as a social network has consequently become very intuitive for many people – whether these relationships are maintained via Facebook, mobile phones or via traditional offline channels.