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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203202010 ISBN 13: 9786203202014
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Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203202010 ISBN 13: 9786203202014
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Añadir al carritoTaschenbuch. Condición: Neu. Machine Learning in Marketing | Customer data growth, new methods, and myriad of applications | José M. Ramos-Henriquez | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203202014 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203202010 ISBN 13: 9786203202014
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Publicado por LAP LAMBERT Academic Publishing Jan 2021, 2021
ISBN 10: 6203202010 ISBN 13: 9786203202014
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Marketing paradigm is becoming customer centric. Every day billions of interactions and transactions occur, generating huge quantities of data. Customers are analysed though the lenses of marketing, with the data that those organizations record, integrate and manage. Today analytical tools include new statistical learning developments, such as machine learning, to generate new models gaining customer insights and supporting marketing decisions. A review of recent research in marketing showed a wide variety of case studies using machine learning methods from customer acquisition analysis, to customer development and customer retention issues. Machine learning models are able to process large data sets, including structured and unstructured data, for prediction and feature extraction models. In an environment where Artificial Intelligence is taking on a large part of the analytical work, companies should consider integrating these methodologies and tools into their marketing teams. 96 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Jan 2021, 2021
ISBN 10: 6203202010 ISBN 13: 9786203202014
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Marketing paradigm is becoming customer centric. Every day billions of interactions and transactions occur, generating huge quantities of data. Customers are analysed though the lenses of marketing, with the data that those organizations record, integrate and manage. Today analytical tools include new statistical learning developments, such as machine learning, to generate new models gaining customer insights and supporting marketing decisions. A review of recent research in marketing showed a wide variety of case studies using machine learning methods from customer acquisition analysis, to customer development and customer retention issues. Machine learning models are able to process large data sets, including structured and unstructured data, for prediction and feature extraction models. In an environment where Artificial Intelligence is taking on a large part of the analytical work, companies should consider integrating these methodologies and tools into their marketing teams.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.
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Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203202010 ISBN 13: 9786203202014
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Marketing paradigm is becoming customer centric. Every day billions of interactions and transactions occur, generating huge quantities of data. Customers are analysed though the lenses of marketing, with the data that those organizations record, integrate and manage. Today analytical tools include new statistical learning developments, such as machine learning, to generate new models gaining customer insights and supporting marketing decisions. A review of recent research in marketing showed a wide variety of case studies using machine learning methods from customer acquisition analysis, to customer development and customer retention issues. Machine learning models are able to process large data sets, including structured and unstructured data, for prediction and feature extraction models. In an environment where Artificial Intelligence is taking on a large part of the analytical work, companies should consider integrating these methodologies and tools into their marketing teams.