Business Analytics: Solving Business Problems With R

Businesses typically encounter problems first and then seek out analytical methods to help in decision making. Business Analytics: Solving Business Problems with R by Arul Mishra and Himanshu Mishra offers practical, data-driven solutions for today′s dynamic business environment. This text helps students see the real-world potential of analytical methods to help meet their business challenges by demonstrating the application of crucial methods. These methods are cutting edge, including neural nets, natural language processing, and boosted decision trees. Applications throughout the book, including pricing models, social sentiment analysis, and branding show students how to use these analytical methods in real business settings, including Frito-Lay, Netflix, and Zappos. Step-by-step R code with commentary gives readers the tools to adapt each method to their business settings. The book offers comprehensive coverage across diverse business domains, including finance, marketing, human resources, operations, and accounting. Finally, an entire chapter explores equity and fairness in analytical methods, as well as the techniques that can be used to mitigate biases and enhance equity in the results. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don’t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.

Table of Contents:
Part 1. Business Environment Analytics Chapter 1: The external environment of a business What Is a Business? Internal and External Environment of a Business Using Analytics to Understand the Business Environment Chapter 2: Monitoring the Macroeconomic Environment Defining the Macroeconomic Environment Impact of Macroeconomic Factors on Business Outcomes Regression for Prediction Application of Linear Regression for Prediction A Few Things to Remember Implementation Using R: Predicting Units Ordered for MedDiagnostics Understanding the Chapter Chapter 3: Monitoring the Competitive Environment using Principal Component Analysis The Competitive Environment of a Business Visualization Using Principal Component Analysis Application of PCA for Competitor Analysis Other Uses of PCA Implementation Using R: Competitor Analysis Appendix: Technical Details of PCA Understanding the Chapter Chapter 4: Monitoring the Social Environment using Text Analysis Understanding the Social Environment Defining Text Data Converting Qualitative Text Data to a Quantifiable Form Analyzing Text Data Choice of Meat Versus Meatless Options: A Reflection of the Social Environment Other Text Analysis Methods Implementation Using R: Choice of Meat Versus Meatless Options Understanding the Chapter Part 2. Marketing Analytics Chapter 5: Market Segmentation using Clustering Algorithms Segmenting Customers Targeting Potential Customers Positioning the Product in Customers’ Minds Data-Driven Segmentation Clustering Algorithms for Segmentation Implementation Using R: Segmentation Using k-means and k-medoid Understanding the Chapter Chapter 6: Predicting Price with Neural Nets Understanding Product Pricing The Power of Pricing Role of Analytics in Price Prediction The Architecture of Neural Networks A Deep Dive Into Neural Nets Predicting House Prices Using Neural Nets Implementation Using R: Predicting House Prices Understanding the Chapter Chapter 7: Advertising and Branding with A/B Testing Advertising: Spreading the Message Causal vs. Correlational A/B Testing for Advertising Effectiveness Steps in A/B Testing Experimental Design to Test for Effective Advertisement Machine-Learning-Based A/B Testing for Finding Effective Advertisements Implementation Using R: A/B Testing for Advertising Effectiveness Understanding the Chapter Chapter 8: Customer Analytics using Neural Nets Retaining Existing Customers Rationale for a Defensive Strategy Monitoring Satisfaction Past Behavior as a Predictor of Churn Predicting Customer Drop-Off Using Neural Nets Implementation Using R: Predicting Customer Churn Understanding the Chapter Part 3. Financial and Accounting Analytics Chapter 9: Loan Charge-off Prediction using an Explainable Model Using Analytics for Financial Decisions Risk Assessment: External Versus Internal Factors Credit Underwriting: Protecting Against Risk Logistic Regression Using Logistic Regression for Charge-Off Prediction Implementation Using R: Loan Approval Understanding the Chapter Chapter 10: Analyzing Financial Performance with LASSO Financial Health of a Business Importance of Forecasting Financial Health of the Business Importance of Knowing Financial Health for Lenders Importance of Knowing a Business’s Financial Health for Investors Forecasting Financial Health Multicollinearity Using Penalized Regression for Evaluating Financial Health Implementation Using R: Evaluating the Health of a Business Appendix: Glossary of Financial Terms Understanding the Chapter Chapter 11: Forensic Accounting using Outlier Detection Algorithms Machine Learning for Accounting Forensic Accounting Machine Learning for Forensic Accounting Understanding Outliers Detecting Fraudulent Transactions Using Loop Business Insights and Conclusion Implementation Using R: Outlier Detection for Identifying Fraudulent Transactions Appendix: Glossary of Accounting Terms Understanding the Chapter Part 4. Operations and Supply Chain Analytics Chapter 12: Predicting Decision Uncertainty using Random Forests Decision-Making Under Uncertainty Features of Decision Uncertainty Backorder and Its Implications Machine-Learning Options to Aid in Decision-Making Under Uncertainty Random Forest Backorder Prediction Using Random Forests Business Insights and Summary Implementation Using R: Backorder Prediction Understanding the Chapter Chapter 13: Predicting Employee Satisfaction using Boosted Decision Trees Employee Satisfaction Drives Customer Satisfaction Measuring Employee Satisfaction Gradient-Boosted Trees Using Boosted Decision Trees to Understand What Impacts Job Satisfaction Business Insights and Summary Implementation Using R: Employee Satisfaction Understanding the Chapter Chapter 14: New Product Development with A/B Testing Innovations in the Marketplace New Product Development Stages The Importance of Testing and Market Research The Intricacies of A/B Testing Using A/B Testing to Test Gaming Prototypes Using the A/B Test in New Product Development Implementation Using R: The A/B Test Understanding the Chapter Part 5. Business Ethics and Analytics Chapter 15: Fairness in Business Analytics Introduction What Are the Causes Behind Algorithmic Unfairness? Mitigating Unfairness Implementation Using Python: Debiasing an Algorithm Understanding the Chapter Part 6. Technical Appendix

About the Author :
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, AI′s 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 psychology—including 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.   

Review :
A thorough and in-depth overview of data analysis with a focus of practical usage using industry-focused examples and accurate use cases. The book provides a business-specific, applied introduction to business analytics. It incorporates multiple business disciplines and perspectives so that students can understand ways that algorithms can be applied in business practice. The chapters are organized by application so that students can see multiple implementations of data science concepts. This is an advanced textbook that provides a practical approach to data analytics, algorithms, and modeling techniques in a business setting. One of the greatest strengths of this book is that it focuses on R through a lens of business problems rather than code. The book provides good explanation about the underlying issues, such as loan charge-off, risk analysis, and more. A unique approach to Business Analytics with a focus on different application domains from External Environment Analytics to Supply Chain Analytics. This text would provide for the opportunity to expand the skills of students and offer one a way to broaden the content covered in an advanced undergraduate course or first year graduate course. I think that the coverage of PCA and Text Analysis is particularly good and is becoming more and more mainstream. Thus, these are topics that need to be covered even at the undergraduate level but are difficult to fit into a single course. This text could provide the opportunity deal with that problem. Good data analytics text using R that you can customize for program needs based upon discipline focus. This book is well-grounded in practical business decision making and includes straightforward discussion and interpretation of statistical output. The content of this book is thorough, with each chapter including a case study and R code example.