Big data raises more questions than it answers, particularly for those organizations struggling to deal with what has become an overwhelming deluge of data. It can offer marketers more than simple tactical predictive analytics, but organizations need a bigger picture, one that generates some real insight into human behaviour, to drive consumer strategy rather than just better targeting techniques. Humanizing Big Data guides marketing managers, brand managers, strategists and senior executives on how to use big data strategically to redefine customer relationships for better customer engagement…mehr
Big data raises more questions than it answers, particularly for those organizations struggling to deal with what has become an overwhelming deluge of data. It can offer marketers more than simple tactical predictive analytics, but organizations need a bigger picture, one that generates some real insight into human behaviour, to drive consumer strategy rather than just better targeting techniques. Humanizing Big Data guides marketing managers, brand managers, strategists and senior executives on how to use big data strategically to redefine customer relationships for better customer engagement and an improved bottom line. Humanizing Big Data provides a detailed understanding of the way to approach and think about the challenges and opportunities of big data, enabling any brand to realize the value of their current and future data assets. First it explores the 'nuts and bolts' of data analytics and the way in which the current big data agenda is in danger of losing credibility by paying insufficient attention to what are often fundamental tenets in any form of analysis. Next it sets out a manifesto for a smart data approach, drawing on an intelligent and big picture view of data analytics that addresses the strategic business challenges that businesses face. Finally it explores the way in which datafication is changing the nature of the relationship between brands and consumers and why this calls for new forms of analytics to support rapidly emerging new business models. After reading this book, any brand should be in a position to make a step change in the value they derive from their data assets.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Colin Strong is a leading consumer researcher who has worked with a wide range of global brands to help shape their consumer strategies. He uses consumer data to drive insight that was once the preserve of surveys, and to advise on ways to shape new consumer brand relationships. Behavioral science runs throughout his research practice, not only to design experimental approaches, but also to guide data analytics. Currently, he is Managing Director of Verve Ventures, a research consulting firm. He is also a regular speaker at conferences and a contributor to publications including The Huffington Post, Wired, Medialine, AdMap and Market Leader.
Inhaltsangabe
Preface Acknowledgements 01 This changes everything The breadth and depth of datafication What is data? Defining big data Qualities of big data This book Notes Part One Current thinking 02 Is there a view from nowhere? Who are you talking to? Sources of bias in samples The upsides of sampling Bigger samples are not always better Big data and sampling Concluding thoughts Notes 03 Choose your weapons The perils of vanity metrics Thinking about thinking: defining the questions Frameworks to help select metrics Tracking your metrics From good data to good decisions Concluding thoughts Notes 04 Perils and pitfalls 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 Is theory important? Concluding thoughts Notes 05 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 other: 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 Concluding thoughts Notes 06 The advertisers' dilemma Online advertising metrics Psychology of online advertising Concluding thoughts Notes Part Two Smart thinking 07 Reading minds The value of linking data sets Knowing your customers Understanding who we are from our digital exhaust The evolution of segmentation Concluding thoughts Notes 08 The ties that bind 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 Concluding thoughts Notes 09 Culture shift 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 Concluding thoughts Notes 10 Bright ideas So what do we need to do? Centralization vs decentralization Developing organization-wide networks of experts Using external networks Limitations to using networks Nurturing ideas Concluding thoughts Notes Part Three Consumer thinking 11 Off limits? How people think about data sharing Limits to data-mediated relationships A model for thinking about data-mediated relationships Overstepping data-based relationships Looking beyond the data Concluding thoughts Notes 12 Getting personal History of self-tracking A changing personal data landscape The relationship between data ownership and empowerment The pitfalls of personal analytics Potential solutions for empowerment Concluding thoughts Notes 13 Privacy paradox Teenagers and privacy The pros and cons of data disclosure The behavioural economics of privacy Brand challenges Trust frameworks and transparency The trend towards transparency But does transparency work? So what should brands do? Concluding thoughts Notes Final thoughts Index
Preface Acknowledgements 01 This changes everything The breadth and depth of datafication What is data? Defining big data Qualities of big data This book Notes Part One Current thinking 02 Is there a view from nowhere? Who are you talking to? Sources of bias in samples The upsides of sampling Bigger samples are not always better Big data and sampling Concluding thoughts Notes 03 Choose your weapons The perils of vanity metrics Thinking about thinking: defining the questions Frameworks to help select metrics Tracking your metrics From good data to good decisions Concluding thoughts Notes 04 Perils and pitfalls 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 Is theory important? Concluding thoughts Notes 05 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 other: 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 Concluding thoughts Notes 06 The advertisers' dilemma Online advertising metrics Psychology of online advertising Concluding thoughts Notes Part Two Smart thinking 07 Reading minds The value of linking data sets Knowing your customers Understanding who we are from our digital exhaust The evolution of segmentation Concluding thoughts Notes 08 The ties that bind 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 Concluding thoughts Notes 09 Culture shift 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 Concluding thoughts Notes 10 Bright ideas So what do we need to do? Centralization vs decentralization Developing organization-wide networks of experts Using external networks Limitations to using networks Nurturing ideas Concluding thoughts Notes Part Three Consumer thinking 11 Off limits? How people think about data sharing Limits to data-mediated relationships A model for thinking about data-mediated relationships Overstepping data-based relationships Looking beyond the data Concluding thoughts Notes 12 Getting personal History of self-tracking A changing personal data landscape The relationship between data ownership and empowerment The pitfalls of personal analytics Potential solutions for empowerment Concluding thoughts Notes 13 Privacy paradox Teenagers and privacy The pros and cons of data disclosure The behavioural economics of privacy Brand challenges Trust frameworks and transparency The trend towards transparency But does transparency work? So what should brands do? Concluding thoughts Notes Final thoughts Index
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