ANALYTICS FOR BUSINESS DECISIONS
An Introduction to Modern Theory, Tools and Techniques
ABOUT THE AUTHORS
Jeff Dotson is an Associate Professor of Marketing at Brigham Young University where he teaches coursework in Pricing Strategies, Marketing Research, and Marketing Analytics.
Prior to joining the faculty at BYU he worked as an Assistant Professor in the Owen Graduate School of Management at Vanderbilt University. He holds a PhD in Marketing from The Ohio State University and master’s degrees in Business Administration (MBA) and Statistics (MS) from the University of Utah.
His research focuses on the development and application of Bayesian statistical methods to a variety of marketing problems. His work has been published in several outlets including the Journal of Marketing, Quantitative Marketing and Economics, Marketing Science, Strategic Management Journal, Journal of Retailing, and the Journal of Interactive Marketing.
In his free time he enjoys skiing, music, and spending time with his wife and four children.
Brady Leavitt is an alumnus of Brigham Young University’s Marriott School of Management MBA program, where he was designated a “Hawes Scholar” (the Marriott School’s highest distinction) and graduated with an emphasis in marketing analytics.
His undergraduate degree was from the University of Utah in Journalism with a business minor.
His career has been spent at the intersection of business and technology. He currently works for Microsoft, helping Fortune 500 companies in Houston, TX leverage the power of data and analytics to transform their businesses.
Previous work experience includes companies such as Amazon, Ancestry.com, and the global technical organization that supports the Church of Jesus Christ of Latter-day Saints.
When he’s not working or nerding around on side projects, Brady enjoys running, swimming and cycling and the occasional round of golf. Mostly he just loves spending time with his wife and two kids.
TABLE OF CONTENTS
a. Course structure and flow
b. A primer on data product development
c. Course tools
d. What is the role of a business student in the world of data and analytics?
2. Getting Started with Power BI and Microsoft Azure ML
a. Tools for data visualization, statistical computing and machine learning
b. Intro to Power BI
c. Intro to Azure ML
d. Putting it all together: Visualizing and pricing the used car market
3. Data Visualization
a. Communicating with data
b. Principles of data visualization
c. Putting it all together: Visualizing company KPI’s and dash boarding in Power BI
4. Statistical Inference and Hypothesis Testing
a. Inference vs. Prediction
b. Sampling, confidence intervals and hypothesis testing
c. Putting it all together: A/B testing for new product development
5. Modeling Continuous Variables
a. Building and interpreting Regression Models
b. Putting it all together: Identifying key drivers of customer satisfaction
6. Modeling Binary Variables
a. Decision Trees
b. Random Forests
c. Putting it all together: Sales lead scoring
7. Cluster Analysis
a. Supervised vs. Unsupervised Machine Learning
b. Techniques for cluster analysis
c. Putting it all together: Customer segmentation
8. Text Analytics
a. Bringing structure to unstructured data
b. Prepping text for analysis
c. Modeling text
d. Putting it all together: Competitive intelligence using twitter data
9. Recommendation Agents
a. Types of recommendation agents
b. Putting it all together: Recommending products for cross-sell and up-sell
ABOUT THE TEXTBOOK
The analytics world evolves at a rapid pace and the ability to keep up with emerging technologies, platforms, tools, and strategies are vital for any instructor and their students.
The Motivation Behind the Textbook
The motivation behind this textbook comes from Jeff’s 10 years of teaching business analytics and the constant need to re-prep his course multiple times over and over again. Jeff wanted to create a textbook that would live in perpetuity and include industry-leading tools.
Analytics for Business Decisions: An Introduction to Modern Theory, Tools and Techniques uses the industry-leading software within the Microsoft family.
These tools consist of:
- Microsoft Excel
- Power BI (Microsoft’s data visualization tool)
- Microsoft Azure Machine Learning Studio (software that allows for complex modeling)
Each of these tools (except Excel) are web-based, innovative, and easily accessible.
The Perfect Mix
Being able to bridge the gap between industry and academia is an important part of this text. Co-Author Brady Leavitt works for Microsoft and is an expert in their tools and in the concrete application of mentioned tools.
Brady focuses on the student outlook and how the content and application will benefit the student going forward in their career. Jeff focuses on the instructor with best ways to teach the content while making it accessible to all types of instructors.
This author partnership provides the perfect mix of expert industry experience and academic theory and principles.