Applying Data Analytics in Marketing

This course introduces students to marketing analytics through a wide range of analytical tools and approaches. We will discuss causal analysis, survey analysis using regression, textual analysis (sentiment analysis), and network analysis. This course aims to provide the foundation required to make better marketing decisions by analyzing multiple types of data related to customer satisfaction.

• Course Introduction and Module 1: Causal Analysis
• In the first module, we will discuss analytics in marketing and dive into causal analysis, an important tool for analytics. We will start with a broad overview of why analytics is important for marketers, what are the various types of data, the process of applying analytics in marketing, and the different types of analytics. We will then delve deeper into causal analysis.
• Module 2: Survey Analysis
• In the second module, we will focus on the analysis of survey data using regression. Surveys are one of the key tools used by organizations to measure important constructs like customer satisfaction. We will start with a broad understanding of the concept of customer satisfaction and various ways to measure it. Next, we will discuss the tools to analyze survey data. We will specifically focus on two regression methods—linear and logistic regressions. Finally, we will conclude the module with a hands-on logistic regression demonstration using an airline customer satisfaction survey dataset with R.
• Module 3: Text Analysis
• We will learn about the various methods of text analysis. We will first introduce you to sentiment analysis—the most prevalent means of analyzing customer satisfaction with textual data. We will demonstrate the sentiment analysis steps via R Studio. Then, we will shift our focus to text summarization techniques. We begin by listing the pre-processing steps required to bring the text to an analyzable form. Next, we look at how the frequency counts of multi-word phrases of pre-processed text can reveal the common terms being discussed. Building on top of the n-grams, we move onto a more intelligent method to automatically detect quality phrases. We will also discuss the LDA Topic Modeling - a very popular way to detect topics in a body of texts. We will wrap up this module with a highlight on supervised machine learning and an example of its application.
• Module 4: Network Analysis
• We will introduce a method to analyze customer satisfaction influence using social media data. Social networks are the perfect dataset to utilize network analysis to understand how people are interacting with other people and forming networks. Identifying a pattern in social media relationships can be useful when making marketing decisions. We will also review influencer brand personality analysis that can be used as a method for brands to find influencers similar in personality to themselves.