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Business Analytics

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

January 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
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

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
Key features
  • This text connects business problems to the analytical methods for solving them.
  • Methods are all contextualized in business settings so students can immediately see their value and applications.
  • R code throughout, with datasets online, offers students hands-on learning opportunities and chances to engage with business data.
  • A variety of business domains, including finance, marketing, human resources, operations, accounting, and supply chain management give students a wide sample of contexts where these methods can apply.
  • A unique chapter on Fairness in Business Analytics addresses ethics and potential sources of bias in critical aspects of daily life to ensure results are equitable for all groups involved.
  • Contemporary examples featuring companies such as MoviePass and Frito-Lay make analytics methods relevant for business students.
  • End-of-chapter questions guide students to view the bigger picture of data in business contexts.

Sage College Publishing

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