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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.
This book presents a framework for developing an analytics strategy
that includes a range of activities, from problem definition and
data collection to data warehousing, analysis, and decision making.
The authors examine best practices in team analytics strategies
such as player evaluation, game strategy, and training and
performance. They also explore the way in which organizations can
use analytics to drive additional revenue and operate more
efficiently. The authors provide keys to building and organizing a
decision intelligence analytics that delivers insights into all
parts of an organization. The book examines the criteria and tools
for evaluating and selecting decision intelligence analytics
technologies and the applicability of strategies for fostering a
culture that prioritizes data-driven decision making. Each chapter
is carefully segmented to enable the reader to gain knowledge in
business intelligence, decision making and artificial intelligence
in a strategic management context.
This book presents a framework for developing an analytics strategy
that includes a range of activities, from problem definition and
data collection to data warehousing, analysis, and decision making.
The authors examine best practices in team analytics strategies
such as player evaluation, game strategy, and training and
performance. They also explore the way in which organizations can
use analytics to drive additional revenue and operate more
efficiently. The authors provide keys to building and organizing a
decision intelligence analytics that delivers insights into all
parts of an organization. The book examines the criteria and tools
for evaluating and selecting decision intelligence analytics
technologies and the applicability of strategies for fostering a
culture that prioritizes data-driven decision making. Each chapter
is carefully segmented to enable the reader to gain knowledge in
business intelligence, decision making and artificial intelligence
in a strategic management context.
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