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Contents





Chapter 1: Big Data Challenges

  • Introduction
  • Explosion of Data
  • Big Data Becomes the Norm, but…
  • Our Objectives
  • Our Approach
  • Reading Guide



Chapter 2: Creating Value using Big Data Analytics

  • Introduction
  • Big Data Value Creation Model
  • Big data assets
  • Big data capabilities
  • The Role of Culture
  • Big Data Analytics
  • Strategies for Analyzing Big Data
  • Big data is changing analytics?
  • The power of visualization
  • From Big Data Analytics to Value Creation
  • Value creation concepts
  • Balance between V2F and V2C
  • V2S: Extending value creation
  • Metrics for V2F and V2C
  • Value Creation Model as Guidance for Book
  • Conclusions


Chapter 2.1 Value to Customer Metrics

  • Introduction
  • Market Metrics
  • New Big Data Market Metrics
  • Brand Metrics
  • Brand-Asset Valuator®
  • Do Brand Metrics Matter?
  • What about Brand Equity?
  • New Big Data Brand Metrics
  • Digital brand association networks
  • Digital summary indices
  • Social media brand metrics
  • Customer Metrics
  • Is There a Silver Metric?
  • Other theoretical relationship metrics
  • Customer equity drivers
  • New Big Data Customer Metrics
  • Internal data sources
  • Online sources
  • V2S Metrics
  • Corporate social responsibility
  • Corporate reputation
  • Should Firms Collect all V2C Metrics?
  • Conclusions


Chapter 2.2: Value to Firm Metrics

  • Introduction
  • Market Metrics
  • Market Attractiveness Metrics
  • New Product Sales Metrics
  • New Big Data Metrics
  • Brand Metrics
  • Brand Market Performance Metrics
  • Brand evaluation metrics
  • Customer Metrics
  • Customer Acquisition Metrics
  • Customer Development Metrics
  • Customer Value Metrics
  • Customer Lifetime Value
  • CLV and its Components
  • Calculating CLV
  • Getting Started with CLV: Be Pragmatic
  • Customer Equity
  • New Big Data Metrics
  • Customer Engagement
  • Customer Journey Metrics: Path to Purchase
  • Marketing ROI
  • Conclusions


Chapter 3: Data, Data Everywhere

  • Introduction
  • Data Sources and Data Types
  • External data sources versus internal data sources
  • Structured versus unstructured data
  • Market data
  • Big data influence on market data
  • Brand data
  • Big data influence on brand data
  • Customer data
  • Big data influence on customer data
  • Using the Different Data Sources in the Era of Big Data
  • Data Warehouse
  • Database Structures
  • Data Quality
  • Missing Values and Data Fusion
  • Conclusions


Chapter 3.1: Data integration

  • Introduction
  • Integrating Data Sources for use in the Commercial Data Environment
  • Extraction
  • Transformation
  • Load
  • Dealing with Different Data Types in the Commercial Data Environment
  • Declared data: Customer descriptors
  • Appended data
  • Overlaid data
  • Implied data
  • Data Integration in the Commercial Data Environment in the Era of Big Data
  • The technical challenges of integrated data
  • The analytical challenges of integrated data
  • The business challenges of integrated data
  • Conclusions


Chapter 3.2: Customer Privacy and Data Security

  • Introduction
  • Why is Privacy a Big Issue?
  • What is Privacy?
  • Customers and Privacy
  • Governments and Privacy Legislation
  • Privacy and Ethics
  • Privacy policies
  • Privacy and Internal Data Analytics
  • Data Security
  • People
  • Systems
  • Processes
  • Conclusions


Chapter 4: How Big Data is Changing Analytics

  • Introduction
  • The Power of Analytics
  • Different Sophistication Levels
  • General Types of Marketing Analysis
  • Strategies for Analysing Big Data
  • Problem solving
  • Data modelling
  • Data mining
  • Collateral catch
  • How Big Data Changes Analytics
  • Market level changes
  • Brand- and product changes
  • Customer level changes
  • Generic Big Data Changes in Analytics
  • From analysing samples to analysing the full population
  • From significance to substantive and size effects
  • From ad-hoc data collection to continuous data collection
  • From standard to computer science models
  • From ad hoc models to real time models
  • Conclusions


Chapter 5: Building Successful Big Data Capabilities

  • Introduction
  • Transformation to Create Successful Analytical Competence
  • Changing roles
  • Changing focus
  • Building Block 1: Process
  • Starting point of the analysis
  • Support during the analysis process
  • Building Block 2: People
  • Analist profile
  • Team approach
  • Acquiring good people
  • Talent retention
  • Building Block 3: Systems
  • Data sources
  • Data storage
  • Analytical big data platform
  • Analytical applications
  • Building Block 4: Organization
  • Centralization or decentralization
  • Cooperation with other functions
  • Conclusions


Chapter 6: Every Business Has (Big) Data, Let’s Use It

  • Introduction

Case 1: CLV Calculation for Energy Company

  • Situation
  • Complication
  • Key-message
  • Data and model used
  • Results
  • Additional insights
  • Success factors

Case 2: Holistic Marketing Approach by Big Data integration at Insurance Company

  • Situation
  • Complication
  • Key message
  • Results
  • Model used
  • Insights
  • Success factors

Case 3: Implementation of Big Data Analytics for Relevant Personalization at Online Retailer

  • Situation
  • Complication
  • Key-message
  • Approach
  • Model used
  • Results
  • Success factors

Case 4: Attribution Modelling at an Online Retailer

  • Situation
  • Complication
  • Key message
  • Results
  • Model used
  • Insights
  • Additional insights
  • Success factors

Case 5: Initial Social Network Analytics at a Telecom Provider

  • Situation
  • Complication
  • Key-message
  • Data & model used
  • Insights
  • Success factors
  • Conclusions


Chapter 7: Concluding Thoughts and Key-Learnings

  • Key-learning Points