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Business Analytics
Solving Business Problems With R

  • Arul Mishra - Eccles School of Business, University of Utah, USA
  • Himanshu Mishra - University of Utah, USA, Eccles School of Business, University of Utah, USA


March 2024 | 344 pages | SAGE Publications, Inc
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.

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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

A thorough and in-depth overview of data analysis with a focus of practical usage using industry-focused examples and accurate use cases.

Brad D. Messner
Seton Hill University

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.

Thomas A. Hanson
Butler University

This is an advanced textbook that provides a practical approach to data analytics, algorithms, and modeling techniques in a business setting.

Aeron Zentner
Coastline College

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.

Yavuz Keceli
Alfred University

A unique approach to Business Analytics with a focus on different application domains from External Environment Analytics to Supply Chain Analytics.

Anita Lee-Post
University of Kentucky

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.

Joel Kincaid
Suny Geneseo

Good data analytics text using R that you can customize for program needs based upon discipline focus.

Kevin S. Walker
Eastern Oregon University

This book is well-grounded in practical business decision making and includes straightforward discussion and interpretation of statistical output.

John L. Sparco
Wilmington University

The content of this book is thorough, with each chapter including a case study and R code example.

Yue Han
Le Moyne College

I am recommending (but not requiring) this book in my course. It is interesting and useful, but I have some questions about the depth of the coverage of R versus the coverage of Business Analytics.

Dr Jose Mendoza
Marketing, New York University
May 22, 2024

Arul Mishra

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... More About Author

Himanshu Mishra

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... More About Author

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ISBN: 9781071815236
$156.00