A. Mansurali – författare

With businesses becoming ever more competitive, marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume, Marketing Analytics: A Machine Learning Approach, enlightens readers on the application of analytics in marketing and the process of analytics, providing a foundation on the concepts and algorithms of machine learning and statistics. The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow, giving them to tools to make better business decisions.

This volume gives a comprehensive overview of marketing analytics, incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics, including segmentation and targeting analysis, statistics for marketing, marketing metrics, consumer buying behavior, neuromarketing techniques for consumer analytics, new product development, forecasting sales and price, web and social media analytics, and much more.

This well-organized and straight-forward volume will be valuable for marketers, managers, decision makers, and research scholars, and faculty in business marketing and information technology and would also be suitable for classroom use.

With businesses becoming ever more competitive, marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume, Marketing Analytics: A Machine Learning Approach, enlightens readers on the application of analytics in marketing and the process of analytics, providing a foundation on the concepts and algorithms of machine learning and statistics. The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow, giving them to tools to make better business decisions.

This volume gives a comprehensive overview of marketing analytics, incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics, including segmentation and targeting analysis, statistics for marketing, marketing metrics, consumer buying behavior, neuromarketing techniques for consumer analytics, new product development, forecasting sales and price, web and social media analytics, and much more.

This well-organized and straight-forward volume will be valuable for marketers, managers, decision makers, and research scholars, and faculty in business marketing and information technology and would also be suitable for classroom use.

In "Sports Analytics: Data-Driven Sports and Decision Intelligence," embark on a journey through the exhilarating world of sports enhanced by the power of data-driven insights. From the nail-biting moments on the field to the strategic decisions behind the scenes, this comprehensive guide unveils the secrets that propel teams to victory and champions to greatness.It explores the cutting-edge techniques and methodologies that revolutionize the way we understand and analyze sports performance. From player evaluations to game strategies, injury prevention to fan engagement, this book equips you with the tools to gain a competitive edge in any sport. Whether you''re a coach, player, analyst, or simply a passionate fan, this book will change the way you see the game.

This book details how to use analytics and machine learning to highlight key performance indicators (KPIs) of sports for analysis. The authors show how to apply various statistical techniques, machine learning and data mining algorithms for on-field and off-field analysis. They go on to show how analytical algorithms are used in the sports ecosystem to derive solutions for the team and leadership, helping team managers and coaches to monitor games and player information through dashboards. The book then shows how to deploy machine learning algorithms for validating and improving teams and players performance. The book is relevant to professionals and academics working in machine learning and data analysis related to sports.