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 of developing new products and services as well as improving 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.
Author(s): Mounir Kehal, Shahira El Alfy
Series: Data Analytics Applications
Publisher: CRC Press
Year: 2021
Language: English
Pages: 208
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editors
Contributors
1 Business Analytics: Through SIoT and SIoV
Introduction
Background
Benefits and Advantages
Safety Management
Traffic Control and Convenience
Productivity
Commercialization
Issues, Controversies, Problems
Information Management in SIoVs
Solutions and Recommendations
Future Research Directions
Conclusion
References
2 Innovation Analytics
Introduction
Scope of Innovation Analytics
Managers and Analytics Applications
Diffusion of Innovation Analysis—Creating the Environment
The Cases of Transformation for the Digital Future
Netflix Innovation Analytics
Emirates Airlines Innovation Analytics (Marketing Techniques)
Amazon and Souq.com Innovation Analytics (Customers Database)
Airbnb Innovation Analytics (New Product Development)
Alibaba Strategy (Investing in People Knowledge)
References
3 Business Predictive Analytics: Tools and Technologies
Introduction
Learning Outcomes
Business Intelligence (BI) Software
Functionality
Weaknesses
Intended Audience
Open-Source Analytics Tools
Functionality
Weaknesses
Intended Audience
Proprietary Analytics Tools
Functionality
Weaknesses
Intended Audience
The Right Tool for the Job
Case Study: Microsoft Power BI and Football Attendance
Getting Started with Football Attendance Data
Introducing Microsoft Power BI
Importing Data
Creating a Visual
Creating a Filter
Club Performance and Attendance
Linking Data
Looking for Relationships.
Debriefing
Problem Set
Bibliography
4 Hospitality Analytics: Use of Discrete Choice Analysis for Decision Support
Introduction
Literature Review
Foundations of Consumer Research: Cognitive Approach
Behavioural Decision Theory
Theories of Choice
The Alternatives
Decision Rules
Past Research on Restaurant Attributes
Ascertaining Attribute Importance
Challenges for Ascertaining Importance
Conjoint Analysis or Discrete Choice Analysis
Conjoint Analysis in Restaurant Attributes Research
Research Design
Preliminary Considerations
Discrete Choice Experiments
Sampling Strategy
Recruitment of Participants, Pilot Study and Final Sample
The Research Instrument
Screening Section
Choice Tournament
Counting Analysis for ACBC
HB Analysis: Calculation of Utilities and Importances
HB with Covariates
Results and Discussion
An Outline of the Different Tasks (Sections)
Fixed Attributes
Optional Attributes
Average Importances
HB Analysis with Covariates
Difference in Levels of Attributes for Every Occasion
Conclusions
Implications for the Restaurant Industry
Reflections on Limitations of This Research
References
5 Data Analytics in Marketing and Customer Analytics
Introduction
Chapter Objectives and Learning Outcomes
Definitions of Data Analytics, Business Analytics, Marketing and Marketing Management, Marketing Analytics, Customers and Consumers, Customer Analytics
Data Analytics
Business Analytics
Marketing and Marketing Management
Customer and Consumer
Marketing Analytics and Its Significance
Customer Analytics and Its Role
The Marketing Management Tasks and Process
Tasks of Marketing Management
Process of Marketing Management
Marketing Research
Segmentation, Targeting, Differentiation, and Positioning (STD&P)
Marketing Mix (4Ps and 7Ps) Including the Updated
Marketing Implementation
Marketing Evaluation and Control
Data Analytics in Marketing
Data Preprocessing
Data Modeling
Customer Analytics
Chapter Conclusions and Implications
Acknowledgments
References
6 Marketing Analytics
Introduction
Insights from a Survey of Small and Medium Enterprises (SMEs)from the UK's East Midlands Region
The Paradox of the Perceived Impact of Marketing Analytics vs. Funding
Need for an Overarching Strategy for Marketing Analytics
The Historical Use of Metrics and Analytics in Marketing
Availability of Marketing Analytic Skills Specifically and Analytics Skills in General
Prevalence of Marketing Analytics Curricula in Business and Management Education
Data Privacy vs. Analytics
Data Availability vs. Data Quality
Accessibility of Paid Professional Analytics
The Future of Marketing Analytics
The Typology of Marketing Analytics – Laying the Foundation
Overarching Strategy/Vision/Leadership
Resources/Competency/Capacity/Tools
Data Availability vs. Data Quality
Context
Meaning and Marketing Intelligence
Conclusion
References
7 Big Data Analytics
Characteristics of Big Data
Big data in Fighting COVID-19
Big Data in Artificial Intelligence
Big Data in Social Media and Internet of Things
Big Data in Customer Interactions
Big Data in Data Science
References
8 New Product Development and Entrepreneurship Analytics
Introduction
The Concepts of New Product and New Product Development
Classification of New Products
New-to-the-World Products
New Product Lines
Additions to Existing Lines
Improvements and Revisions to Existing Products
Cost Reductions
Repositioning
New Product Development Process
Idea Generation Stage
Idea Screening Stage
Concept Development and Testing Stage
Concept Development
Concept Testing
Marketing Strategy Development Stage.
Business Analysis Stage
Product Development Stage
Test Marketing Stage
Commercialization Stage
Product Development Analytics.
Predictive Analytics in Product Development
Entrepreneurship Analytics
Analytics for Start-up Entrepreneurs
Choose the Right Analytics Team
Collect the Right Data
Make Key Technology Decisions Early
Measure Your Results
Find the Supportive Investors
Growth Hacking for Start-ups
Conclusion
References
9 Predictive Learning Analytics in Higher Education
Section 1: Introduction
Section 2: Prospects and Challenges of Predictive Analytics
Prospects
Challenges
Section 3: Ethical Framework and Considerations for Predictive Analytics in Higher Education
Section 4: The Application of Predictive Analytics in Higher Education
Student Academic Advising
Adaptive Learning
Mini-Case Study: Intellipath at Colorado Technical University
Management of Student Enrolment
Student Academic Performance
Predictive Analytics for Curriculum Internationalization
Section 5: Case Studies of Predictive Learning Analytics in Higher Education Management
Introduction
Predictive Analytics in Georgia State and Kennesaw State Universities
Predictive Analytics at Mount St Mary's University
References
Index
