What This Document Is
This document contains lecture notes from BUSI 604: International Business at Liberty University, focusing on quantitative methods used in marketing. It explores techniques for understanding consumer preferences and making data-driven marketing decisions. The notes cover conjoint analysis, cluster analysis, and forecasting methods, providing a foundational overview of these analytical tools.
Why This Document Matters
These notes are valuable for students in International Business programs, particularly those specializing in marketing or market research. They are useful during coursework, as a study aid for understanding complex analytical concepts, and as a reference point for applying these methods to real-world business scenarios. Understanding these quantitative methods is crucial for professionals who need to interpret market data and develop effective marketing strategies.
Common Limitations or Challenges
This document provides a theoretical overview and examples of these methods. It does *not* offer in-depth training on how to *execute* these analyses using specific software packages (like SPSS or Excel). It also doesn’t include case studies or detailed applications to international business contexts. Users will still need textbooks, software tutorials, and practical experience to fully master these techniques.
What This Document Provides
The full document includes:
* An explanation of conjoint analysis, including formulas for calculating relative importance and utility values.
* Examples illustrating conjoint analysis calculations for attributes like airline features (nonstop flights, price).
* An overview of cluster analysis, including examples of agglomeration schedules and coefficient tables.
* A discussion of forecasting errors, including formulas for MAD, MSE, and MAPE.
* Example calculations for exponential smoothing forecasting.
* Definitions of key terms related to cluster analysis.
This preview *does not* include detailed step-by-step instructions for performing these analyses, access to datasets, or comprehensive case studies. It is intended to provide a high-level understanding of the topics covered.
