Chapter
1 This changes everything
The breadth and depth of datafication
Datafication of sentiment/emotions
Datafication of interactions/relationships
Datafication of what is traditionally seen
as offline activity
Part One Current thinking
2 Is there a view from nowhere?
Sources of bias in samples
Bigger samples are not always better
Differences between online and offline worlds
The perils of vanity metrics
Thinking about thinking: defining the questions
Frameworks to help select metrics
The Donald Rumsfeld model
The data type vs business objective model
Balanced scorecards and dashboards
From good data to good decisions
Dangers of reading data: the pitfalls of correlations
Dangers of reading data: the frailties of human judgement
The pitfalls of storytelling
Mixing up narrative and causality
Theory is there whether we like it or not
Big data can be misleading without theory
5 The power of prediction
The growth of data available for prediction
How good is our ability to predict?
Understanding the limitations of prediction
Why some things are easier to predict than others: complex vs simple systems
The influence of social effects on system complexity
Building models to make predictions
Learning to live with uncertainty: the strategy paradox
6 The advertisers’ dilemma
Online advertising metrics
Teasing apart cause and effect
Targeting problems when measuring effectiveness
Psychology of online advertising
The value of linking data sets
Understanding who we are from our
digital exhaust
The evolution of segmentation
Why making choices can be so difficult
Simplifying decision-making
The role of influence and ‘influencers’
Identifying network effects
The implications of networks for marketing
Exploring the importance of social relationships
Seeing the world in new ways
Deconstructing cultural trends
Exploring the lifecycle of ideas through cultural analytics
From verbal to visual: the importance of images
Analysing cultural trends from images
So what do we need to do?
Centralization vs decentralization
Developing organization-wide networks of experts
Limitations to using networks
Part Three Consumer thinking
How people think about data sharing
Limits to data-mediated relationships
A model for thinking about data-mediated relationships
Overstepping data-based relationships
A changing personal data landscape
The relationship between data ownership and empowerment
The pitfalls of personal analytics
The psychology of inertia
Potential solutions for empowerment
The psychology of decision-making
Technology-based decision support
The pros and cons of data disclosure
The behavioural economics of privacy
Trust frameworks and transparency
The trend towards transparency
But does transparency work?
So what should brands do?
Just how useful is big data to marketers?
Driving insights from data
Understanding the consumer experience
Humanizing Big Data
：Marketing at the Meeting of Data, Social Science and Consumer Insight
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