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Full Description
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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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
