This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.
With the first module, we will measure and identify satisfied customers to adjust product or service accordingly. To measure the customer satisfaction we will measure expectations, performance and disconfirmation of the offered product or service. We will also provide an overview of marketing analytics used to gauge the effectiveness of different marketing activities. Finally, we will go through measurement and scaling techniques.
We will explore the marketing world through process of A/B testing, design of experiments, data analysis, and hypothesis testing. Next, we study Analysis of Variance (ANOVA) which is used to determine significant differences between two or more categorical groups. We will study ANOVA’s assumptions, test inference and different types of ANOVA. We also spend some time in designing experiments.
We will learn about the Binary Outcome model using Logit function. The Logistic regression is sued when the dependent variable has a binary outcome. Next, we cover Multidimensional Scaling (MDS). MDS is used in marketing to understand the pair-wise similarity of the individual cases of data-set. This technique maps the individual cases onto a 2-dimensional Cartesian graph for visual analysis.
We will learn about Conjoint Analysis to understand how individuals combine and weigh different attributes. We will introduce the concept of conjoint analysis and part-worth utilities. We will survey different approaches before focusing our attention to the classical conjoint analysis. We will reinforce our understanding by studying an example in detail before running examples of the analysis in R