Business Analytics

Auteur
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.

Himanshu Mishra serves as the David Eccles Professor at the Eccles School of Business and as an Adjunct Professor in the Kahlert School of Computing at the University of Utah. He earned his Ph.D. in marketing from the University of Iowa. Himanshu uses machine learning methods to analyze human decisions in social and marketplace settings. He often collaborates with firms to apply the insights he gathers from research. The findings of his research inform consumer decision-making, AIs role in fair decisions, risk assessment strategies, and overall human well-being.

With over 20 years in academia, Himanshu has taught across undergraduate, graduate, and Ph.D. levels. His recent courses involve using machine learning applications to improve business decisions and the importance of algorithmic fairness. His extensive research contributions can be found in top journals and conferences spanning marketing, business, computer science, and psychologyincluding the Journal of Marketing Research, IEEE International Conference on Big Data, Psychological Science, and others. Moreover, media outlets like MSNBC, The Wall Street Journal, National Public Radio, and The New York Times have featured his work.

Texte du rabat

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.

Contenu

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