Business Analytics

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

Other Titles in:
Decision Analysis | Management Sciences (General) | Quantitative Techniques for Business & Management Research

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

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

Supplements

Instructor Resources

Student Resources

I could not access the book. It is shown in the cart, but I cannot do anything about it. So, no chance to review the book. Your system seems to have a problem.

Professor Dohoon Kim

Management, Kyung Hee University - Yongin

January 16, 2025

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Sample Materials & Chapters

Ch01_The External Environment of a Business

Ch02_Monitoring the Macroeconomic Environment

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