Business Analytics: Solving Business Problems with R offers a practical, hands-on introduction to analytical methods, including machine learning in real-world business scenarios. Connecting business decisions and analytical methods across multiple fields, this book guides readers through a wide range of business problems and their fitting analytical solutions, offering examples and implementation using R.
Business Analytics: Solving Business Problems with R offers a practical, hands-on introduction to analytical methods, including machine learning in real-world business scenarios. Connecting business decisions and analytical methods across multiple fields, this book guides readers through a wide range of business problems and their fitting analytical solutions, offering examples and implementation using R.
Arul Mishra is the Emma Eccles Jones Presidential Chair Professor of Marketing and Adjunct Professor, School of Computing at the University of Utah. Her research, on a broader level, uses machine learning methods to understand customer decisions and guide firm strategies. Specifically, she derives theoretical and practical insights from data using computational algorithms to understand customer engagement in digital markets, customer preference and choice, financial decisions, online advertising, and creativity. Currently her research involves leveraging language and generative models for business applications. She also examines the ethical consequences of using algorithms. Can algorithms exacerbate or reduce the impact of social biases and inequities? How can algorithms help firms make better decisions? Methodologically, she uses Natural Language Processing, generative language models, image processing, and field studies to test social phenomena and theories. Arul's research has been published in the Journal of Marketing Research, Journal of Consumer Research, Journal of Marketing, Marketing Science, Management Science, Journal of Personality and Social Psychology, Organizational Behavior and Human Decision Processes, Psychological Science , and American Psychologist®. Popular accounts of her work have appeared in Scientific American, Los Angeles Times, The Wall Street Journal, Chicago Tribune, MSN Money, The Financial Express, and Shape. Arul teaches or has taught several courses at the Eccles School of Business including Algorithms for Business Decisions for Master students, Consumer Analytics for undergraduate students, and doctoral courses on research theory and methods.
Inhaltsangabe
Part 1. Business Environment Analytics Chapter 1: The external environment of a business Chapter 2: Monitoring the Macroeconomic Environment Chapter 3: Monitoring the Competitive Environment using Principal Component Analysis Chapter 4: Monitoring the Social Environment using Text Analysis Part 2. Marketing Analytics Chapter 5: Market Segmentation using Clustering Algorithms Chapter 6: Predicting Price with Neural Nets Chapter 7: Advertising and Branding with A/B Testing Chapter 8: Customer Analytics using Neural Nets Part 3. Financial and Accounting Analytics Chapter 9: Loan Charge-off Prediction using an Explainable Model Chapter 10: Analyzing Financial Performance with LASSO Chapter 11: Forensic Accounting using Outlier Detection Algorithms Part 4. Operations and Supply Chain Analytics Chapter 12: Predicting Decision Uncertainty using Random Forests Chapter 13: Predicting Employee Satisfaction using Boosted Decision Trees Chapter 14: New Product Development with A/B Testing Part 5. Business Ethics and Analytics Chapter 15: Fairness in Business Analytics Part 6. Technical Appendix
Part 1. Business Environment Analytics Chapter 1: The external environment of a business Chapter 2: Monitoring the Macroeconomic Environment Chapter 3: Monitoring the Competitive Environment using Principal Component Analysis Chapter 4: Monitoring the Social Environment using Text Analysis Part 2. Marketing Analytics Chapter 5: Market Segmentation using Clustering Algorithms Chapter 6: Predicting Price with Neural Nets Chapter 7: Advertising and Branding with A/B Testing Chapter 8: Customer Analytics using Neural Nets Part 3. Financial and Accounting Analytics Chapter 9: Loan Charge-off Prediction using an Explainable Model Chapter 10: Analyzing Financial Performance with LASSO Chapter 11: Forensic Accounting using Outlier Detection Algorithms Part 4. Operations and Supply Chain Analytics Chapter 12: Predicting Decision Uncertainty using Random Forests Chapter 13: Predicting Employee Satisfaction using Boosted Decision Trees Chapter 14: New Product Development with A/B Testing Part 5. Business Ethics and Analytics Chapter 15: Fairness in Business Analytics Part 6. Technical Appendix
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