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January, 2024 | SAGE Publications, Inc

Business Analytics

Solving Business Problems with R


Arul Mishra - Eccles School of Business, University of Utah, USA
Himanshu Mishra - University of Utah, USA
288 pages | January, 2024 | SAGE Publications, Inc
Paperback
ISBN: 9781071815236

Available on March 05,2024

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Paperback
ISBN: 9781071815236

Available on March 05,2024

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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:

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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

Instructor Resources

  • 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.