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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