Inhaltsangabe:
Machine learning, now a central part of artificial intelligence, would be a driving force to change the current world to a more autonomous society. This impact of machine learning appears in many fields, for example, science, engineering, finance, agriculture, to name a few. Marketing is rather behind this trend, while marketing has a lot of potential applications for machine learning. In other words, marketing may change into more autonomous scientific work by using data and also proper formulation of each application into a machine learning problem. This book focuses on two major, traditional paradigms of marketing: target marketing and relationship marketing. Then it is revealed that each of numerous aspects of the two marketing paradigms can be formulated into a machine learning problem. That is, for each problem, a machine learning model can be built and parameters of the model can be estimated/optimized from given data. For example, an important objective of target marketing can be interpreted as a problem of finding a customer segment, which has a plenty of customers but no competitors. This problem can be formulated into a machine learning problem for which a model is built and model parameters can be estimated from given data. This book, for each machine learning problem setting, always builds a simpler (probably simplest) model, so that readers can understand the idea and assumption of the model easily. This book would be useful for both sides of marketing and machine learning. That is, marketers would be able to study the way of formulating a problem of marketing into a machine learning problem/function in which parameters are estimated from given data. On the other hand, machine learners would be able to study applications of marketing and also essential and intuitive ideas behind marketing through numerous applications in this book.
Reseña del editor:
Machine learning, now a central part of artificial intelligence, would be a driving force to change the current world to a more autonomous society. This impact of machine learning appears in many fields, for example, science, engineering, finance, agriculture, to name a few. Marketing is rather behind this trend, while marketing has a lot of potential applications for machine learning. In other words, marketing may change into more autonomous scientific work by using data and also proper formulation of each application into a machine learning problem. This book focuses on two major, traditional paradigms of marketing: target marketing and relationship marketing. Then it is revealed that each of numerous aspects of the two marketing paradigms can be formulated into a machine learning problem. That is, for each problem, a machine learning model can be built and parameters of the model can be estimated/optimized from given data. For example, an important objective of target marketing can be interpreted as a problem of finding a customer segment, which has a plenty of customers but no competitors. This problem can be formulated into a machine learning problem for which a model is built and model parameters can be estimated from given data. This book, for each machine learning problem setting, always builds a simpler (probably simplest) model, so that readers can understand the idea and assumption of the model easily. This book would be useful for both sides of marketing and machine learning. That is, marketers would be able to study the way of formulating a problem of marketing into a machine learning problem/function in which parameters are estimated from given data. On the other hand, machine learners would be able to study applications of marketing and also essential and intuitive ideas behind marketing through numerous applications in this book.
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