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Creating Value with Data Analytics in Marketing: Mastering Data Science 2nd edition [Minkštas viršelis]

, , (Metriclab Big Data Analytics, The Netherlands),
  • Formatas: Paperback / softback, 314 pages, aukštis x plotis: 246x174 mm, weight: 1060 g, 7 Tables, color; 205 Line drawings, color; 205 Illustrations, color
  • Serija: Mastering Business Analytics
  • Išleidimo metai: 08-Nov-2021
  • Leidėjas: Routledge
  • ISBN-10: 0367819791
  • ISBN-13: 9780367819798
Kitos knygos pagal šią temą:
Creating Value with Data Analytics in Marketing: Mastering Data Science 2nd editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 314 pages, aukštis x plotis: 246x174 mm, weight: 1060 g, 7 Tables, color; 205 Line drawings, color; 205 Illustrations, color
  • Serija: Mastering Business Analytics
  • Išleidimo metai: 08-Nov-2021
  • Leidėjas: Routledge
  • ISBN-10: 0367819791
  • ISBN-13: 9780367819798
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780367819798.html
Raktažodžiai:

This book is a refreshingly practical yet theoretically sound roadmap to leveraging data analytics and data science. The vast amount of data generated about us and our world is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value in marketing.

Creating Value with Data Analytics in Marketing

provides a nuanced view of big data developments and data science, arguing that big data is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. The second edition of this bestselling text has been fully updated in line with developments in the field and includes a selection of new, international cases and examples, exercises, techniques and methodologies.

By tying data and analytics to specific goals and processes for implementation, this is essential reading for advanced undergraduate and postgraduate students and specialists of data analytics, marketing research, marketing management, and customer relationship management.

Online resources include chapter-by-chapter lecture slides and data sets and corresponding R code for selected chapters.



This book is a practical yet theoretically sound roadmap to leveraging data analytics and data science. The vast amount of data generated about us and our world is useless without plans and strategies to cope with its size and complexity, and which enable organisations to leverage the information to create value in marketing.

Recenzijos

"More than ever before, managers are held accountable for the return on their marketing investments. Data science has thus become a centerpiece in the skills arsenal of todays manager. This comprehensive, yet eminently readable book is a must read for anyone seriously interested in using data science to create firm value."

Jan-Benedict Steenkamp, C. Knox Massey Distinguished Professor of Marketing, University of North Carolina at Chapel Hill, USA

"Data science and big data analytics are in vogue these days but none of the current books on these subjects combine an understanding of them along with how they interface with marketing metrics. This is precisely what Peter Verhoef, Edwin Kooge, Natasha Walk and Jaap Wieringa have accomplished! The book is literally a treasure trove for marketing practitioners, academics and students, to not only understand the specifics of the data science techniques and methodologies but also to develop an appreciation of how they can all be put together to inform managers and drive decision making!"

P.K. Kannan, Associate Dean of Strategic Initiatives at University of Maryland, Robert H. Smith School of Business, USA

"In the age of big data, understanding data analytic in marketing is crucial. This edition highlights how data can bring value to the organization and brand while addressing the need for customer privacy and data security. This book helps the reader understand the what story the data is saying as well as create a data-driven culture. A must read for anyone wanting a better understanding of how data can bring value in marketing."

Joyce Costello, Marketing Management, Cardiff Metropolitan University, UK

"This superb book brilliantly synthesizes knowledge that every researcher and professional working with marketing analytics should have. The new edition of this much-valued book is even better!"

Polymeros Chrysochou, Professor at Department of Management & MAPP Centre, Aarhus University, Denmark

"This book offers an amazing overview of what data analytics entails, and how any marketer can create value through (big) data. It offers many great examples and is written in a no nonsense manner combining the best of academic and practitioner knowledge. If you are serious about data analytics, this book is a must-read."

Arne De Keyser, Associate Professor of Marketing, EDHEC Business School, France

"This revised edition is a practical book introducing readers to big data analytics in marketing in details. It is packed with great examples and engaging case studies. The book is well structured and is easy to understand. A great book for students, scholars and practitioners to start their journey in exploring big data analytics in marketing."

Mingming Cheng, Senior Lecturer in Digital Marketing, School of Management and Marketing, Curtin University, Australia

List of figures
xiv
List of tables
xx
Preface xxi
1 Data science and big data
1(5)
1.1 Introduction
1(1)
1.2 Explosion of data
1(2)
1.3 Data science becomes the norm
3(1)
1.4 Objectives
3(1)
1.5 Our approach
4(1)
1.6 Overview of chapters
4(2)
2 Creating value with data science
6(14)
2.1 Introduction
6(1)
2.2 Data science value creation model
6(1)
2.3 Value creation objectives
7(3)
2.3.1 Balance between V2F and V2C
7(1)
2.3.2 V2S: Extending value creation
8(1)
2.3.3 Metrics for V2F and V2C
9(1)
2.4 Data assets
10(1)
2.5 Data analytics
11(2)
2.5.1 The power of visualization and storytelling
12(1)
2.6 Value creation
13(1)
2.7 Data analytics capabilities
13(2)
2.7.1 The role of culture
15(1)
2.8 Data science value creation model as guidance for this book
15(2)
2.9 Conclusions
17(3)
Assignment 2.1 V2C and V2F company classification
17(3)
3 Value objectives and metrics
20(40)
3.1 Introduction
20(1)
3.2 V2C metrics
21(2)
3.2.1 Market metrics
21(1)
3.2.2 New big data market metrics
21(2)
3.3 Brand metrics
23(6)
3.3.1 Brand-Asset Valuator®
25(1)
3.3.2 Do brand metrics matter?
25(2)
3.3.3 New big data brand metrics
27(1)
3.3.4 Digital brand association networks
27(1)
3.3.5 Digital brand metrics
27(1)
3.3.6 Social media brand metrics
28(1)
3.4 Customer metrics
29(5)
3.4.1 Is there a silver metric?
31(1)
3.4.2 Other theoretical relationship metrics
32(1)
3.4.3 Customer equity drivers
32(1)
3.4.4 New big data customer metrics
33(1)
3.5 V2S metrics
34(1)
3.6 Should firms collect all V2C metrics?
34(1)
3.7 Value to firm metrics
35(1)
3.8 Market metrics
36(4)
3.8.1 Market attractiveness metrics
36(1)
3.8.2 New product sales metrics
37(1)
3.8.3 Brand market performance metrics
37(2)
3.8.4 Brand evaluation metrics
39(1)
3.9 Customer metrics
40(11)
3.9.1 Customer acquisition metrics
41(1)
3.9.2 Customer development metrics
41(2)
3.9.3 Customer value metrics
43(3)
Case 3.1 Case CLV at energy company
46(3)
3.9.4 Customer equity
49(1)
3.9.5 New big data metrics
49(2)
3.10 Marketing ROI
51(1)
3.11 Conclusions
52(8)
Assignment 3.1 CLV Health insurance company
53(1)
Assignment 3.2 Metrics Dutch supermarkets
53(7)
4 Data assets
60(17)
4.1 Introduction
60(1)
4.2 Data sources and the different types of data
61(9)
4.2.1 External data sources versus internal data sources
62(1)
4.2.2 Structured versus unstructured data
63(1)
4.2.3 Market data
64(2)
4.2.4 Big data influence on market data
66(1)
4.2.5 Brand data
67(1)
4.2.6 Big data influence on brand data
68(1)
4.2.7 Customer data
68(2)
4.2.8 Big data influence on customer data
70(1)
4.3 Using the different data sources in the era of big data
70(2)
4.4 Data quality and data cleansing
72(2)
4.4.1 Data quality
72(1)
4.4.2 Data cleansing
73(1)
4.4.3 Missing values and data fusion
74(1)
4.5 Conclusions
74(3)
5 Data storing and integration
77(22)
5.1 Introduction
77(1)
5.2 Storing and integrating data sources in data warehouses
78(4)
5.2.1 Storing data in the data warehouse
78(1)
5.2.2 The data model in a data warehouse
79(2)
5.2.3 Data integration into the data warehouse
81(1)
5.3 Storing and integrating data sources in data lakes
82(3)
5.3.1 Data
83(1)
5.3.2 Data model
83(1)
5.3.3 Users
83(1)
5.3.4 Analytics
84(1)
5.3.5 Price/quality
84(1)
5.3.6 Flexibility
84(1)
5.3.7 Technology
84(1)
5.4 Challenges of data integration in the era of big data
85(11)
5.4.1 The technical challenges of integrated data
86(2)
5.4.2 The analytical challenges of integrated data
88(1)
5.4.3 The business challenges of integrated data
88(5)
Case 5.1 Data integration for an insurance company
93(3)
5.5 Conclusions
96(3)
Assignment (Chapters 4 and 5): Superstore
97(2)
6 Customer privacy and data security
99(20)
6.1 Introduction
99(1)
6.2 Why is privacy a big issue?
100(1)
6.3 What is privacy?
101(1)
6.4 Customers' privacy concern
101(2)
6.5 Privacy paradox and privacy calculus
103(1)
6.6 Governments and privacy legislation
103(6)
6.6.1 Steps to comply with GDPR
107(2)
6.6.2 Going beyond legislation
109(1)
6.7 Privacy and ethics
109(1)
6.8 Privacy policies
110(2)
6.9 Privacy and internal data analytics
112(2)
6.9.1 Model based solutions for privacy
113(1)
6.10 Data security
114(1)
6.10.1 People
114(1)
6.10.2 Systems
114(1)
6.10.3 Processes
115(1)
6.11 Conclusions
115(4)
Assignment 6.1 Curani pet care
115(4)
7 Data analytics
119(19)
7.1 Introduction
119(1)
7.2 The power of analytics
120(1)
7.3 Strategies for analyzing data
121(4)
7.3.1 Problem solving
121(1)
7.3.2 Data exploitation
122(1)
7.3.3 Data mining
123(1)
7.3.4 Collateral catch
124(1)
7.4 Types of data analytics
125(4)
7.4.1 Descriptive analytics
126(1)
7.4.2 Diagnostic analytics
126(2)
7.4.3 Predictive analytics
128(1)
7.4.4 Prescriptive analytics
128(1)
7.5 How big data and AI change analytics
129(6)
7.5.1 Definitions
129(2)
7.5.2 Important changes in the analytical working field
131(4)
7.6 Analytical methods and techniques
135(1)
7.7 Conclusions
136(2)
8 Data exploration
138(36)
8.1 Introduction
138(1)
8.2 Descriptive analyses---reporting
139(3)
8.3 Descriptive analyses---investigating one-to-one relationships
142(8)
8.3.1 KPI categorical, driver categorical
143(1)
8.3.2 KPI numerical, driver categorical
144(2)
8.3.3 KPI categorical, driver numerical
146(2)
8.3.4 KPI numerical, driver numerical
148(2)
8.4 Special cases of one-to-one exploratory analyses
150(4)
8.4.1 Profiling and customer crossings
150(1)
8.4.2 Decile analysis
151(1)
8.4.3 External profiling
152(1)
8.4.4 Zip code analysis
152(2)
8.5 Dynamic analyses
154(5)
8.5.1 Trend analysis
154(2)
8.5.2 Migration analysis
156(1)
8.5.3 Like-4-like analysis
157(2)
8.6 Identifying structure in the data---unsupervised learning
159(9)
8.6.1 Cluster analysis
161(3)
8.6.2 Principal components analysis
164(4)
8.7 Conclusions
168(6)
Assignment
168(6)
9 Data modeling
174(47)
9.1 Introduction to data modeling
174(3)
9.2 Theory-driven models
177(6)
9.2.1 Model specification
178(3)
9.2.2 Estimation
181(1)
9.2.3 Validation
181(2)
9.3 Linear regression
183(9)
9.4 Logistic regression
192(7)
9.4.1 Hit rate
196(1)
9.4.2 Top-decile lift
197(1)
9.4.3 Gini coefficient
198(1)
9.5 Data-driven modeling and machine learning
199(1)
9.6 Decision trees
200(6)
9.7 Ensemble learning models: bagging, random forests, boosting
206(4)
9.7.1 Bagging decision trees
206(1)
9.7.2 Random forests
207(1)
9.7.3 Boosting decision trees
208(2)
9.8 Naive Bayes
210(1)
9.9 Support vector machines (SVM)
210(2)
9.10 Neural networks
212(2)
9.11 Reinforcement learning
214(1)
9.12 Conclusions
215(6)
Assignments
216(5)
10 Creating impact with storytelling and visualization
221(25)
10.1 Introduction
221(2)
10.2 Failure factors for creating impact
223(1)
10.3 Storytelling
224(4)
10.3.1 Checklist for a clear storyline
226(2)
10.4 Visualization
228(13)
10.4.1 Choosing the chart type
230(4)
10.4.2 Graph design
234(2)
10.4.3 Misleading graphs and other problems
236(5)
10.5 Trends in visualization
241(1)
10.6 Conclusions
242(4)
Assignments
242(1)
Assignment 10.1 Vodafone press release
242(2)
Assignment 10.2 Remove the errors from the graph
244(2)
11 Creating value with data science
246(29)
11.1 Introduction
246(1)
11.2 Data science value creation
247(1)
11.3 Value creation at marketing level
247(10)
11.3.1 Insights delivery
247(1)
11.3.2 Marketing performance measurement
248(1)
Case 11.1 Marketing performance measurement at an insurance company
249(5)
Case 11.2 Attribution modeling at an online retailer
254(2)
11.3.3 Customer level
256(1)
11.4 Value creation at customer-firm interface level
257(9)
11.4.1 Recommendation systems
258(1)
Case 11.3 Implementation of big data analytics for relevant personalization at an online retailer
259(5)
11.4.2 Personalization
264(2)
11.4.3 Dark sides of personalization
266(1)
11.5 Data science as business model
266(1)
11.6 Opportunity finding as a methodology to create more value
267(3)
11.6.1 Step 1: The business challenge
268(1)
11.6.2 Step 2: The sub questions
268(1)
11.6.3 Step 3: The factors
268(1)
11.6.4 Step 4: Hypotheses
269(1)
11.6.5 Step 5: Insights
269(1)
11.6.6 Step 6: Initiatives
269(1)
11.6.7 Step 7: Impact
269(1)
11.7 Conclusion
270(5)
Assignment 11.1 OpportunityfindingforSure.com
270(5)
12 Building successful data analytics capabilities
275(30)
12.1 Introduction
275(2)
12.2 Transformation to create successful analytical competence
277(2)
12.2.1 Changing roles
277(1)
12.2.2 Changing focus
277(2)
12.3 Building block 1: process
279(4)
12.3.1 Define and structure the business challenge
280(1)
12.3.2 Collect and manipulate data
280(1)
12.3.3 Perform data analysis
281(1)
12.3.4 Presenting opportunities and solutions
281(1)
12.3.5 Implementation of results
282(1)
12.4 Building block 2: people
283(5)
12.4.1 Analyst profile
283(2)
12.4.2 Team approach
285(1)
12.4.3 Acquiring new talent
286(1)
12.4.4 Talent retention
287(1)
12.4.5 Scalable analytics
288(1)
12.5 Building block 3: systems
288(6)
12.5.1 Data sources
289(2)
12.5.2 Data storage
291(1)
12.5.3 Analytical data platform
291(1)
12.5.4 Analytical applications
292(2)
12.6 Building block 4: organization
294(4)
12.6.1 Centralization or decentralization
294(1)
12.6.2 Cooperation with other functions
295(1)
12.6.3 Establishing a data-driven culture
296(2)
12.7 Conclusions
298(7)
Assignments
299(1)
Assignment 12.1 The multidisciplinary skills of the modern data analyst
299(1)
Assignment 12.2 Data analytics function NL Insurance
300(5)
Index 305
Peter C. Verhoef is the Dean of the Faculty of Economics and Business and Professor of Marketing at the University of Groningen, the Netherlands.

Edwin Kooge is a co-founder of BAYZ, a consulting agency focusing on teaching companies to master analytics. He is a data science expert, results-focused business advisor and entrepreneur with more than 25 years experience in analytics.

Natasha Walk is a co-founder of BAYZ, a consulting agency focusing on teaching companies to master analytics. She is a pragmatic data science expert focusing on talent development in data science with more than 25 years experience in analytics.

Jaap E. Wieringa is a Professor of Research Methods in Business at the Department of Marketing at the University of Groningen, the Netherlands, and is a Research Director of the Customer Insights Center.