Data analytics in marketing, entrepreneurship, and innovation
著者
書誌事項
Data analytics in marketing, entrepreneurship, and innovation
(Data analytics applications)(An Auerbach book)
CRC Press, 2021
1st ed
- : hbk
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at:
Business analytics
Applying predictive analytics
Using discrete choice analysis for decision-making
Marketing and customer analytics
Developing new products
Technopreneurship
Disruptive versus incremental innovation
The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.
目次
1 Business Analytics: Through SIoT and SIoV. 2 Innovation Analytics. 3 Business Predictive Analytics: Tools and Technologies. 4 Hospitality Analytics: Use of Discrete Choice Analysis for Decision Support. 5 Data Analytics in Marketing and Customer Analytics. 6 Marketing Analytics. 7 Big Data Analytics. 8 New Product Development and Entrepreneurship Analytics. 9 Predictive Learning Analytics in Higher Education.
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