000			02576nam a22002537a 4500
999			_c4496 _d4496
005			20230117110058.0
008			230117b ||||| |||| 00| 0 eng d
020			_a9780367819798
082			_a658.83 _bVER
100			_aVerhoef, Peter C. _910499
245			_aCreating value with data analytics in marketing: _bmastering data science
250			_a2nd
260			_bRoutledge _aLondon _c2022
300			_axxii, 314 p.
365			_aGBP _b39.99
504			_aTable of Contents 1 Data science and big data 2 Creating value with data science 3 Value objectives and metrics 4 Data assets 5 Data storing and integration 6 Customer privacy and data security 7 Data analytics 8 Data exploration 9 Data modeling 10 Creating impact with storytelling and visualization 11 Creating value with data science 12 Building successful data analytics capabilities
520			_aThis 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 organizations 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. Tying data and analytics to specific goals and processes for implementation makes this 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.
650			_aMarketing--Data processing _911355
650			_aBig data _9212
650			_aConsumer profiling _910472
700			_aKooge, Edwin _911356
700			_aWalk, Natasha _911357
942			_2ddc _cBK