Applying Data Analytics in Marketing
- Course Introduction and Module 1: Customer Satisfaction
- With the first module, we begin by looking at some definitions of customer satisfaction. Then, we explore some major issues we have to consider. These issues include the psychological constructs of customer satisfaction, proper measurement of those constructs, varying targets of satisfaction, differences in the impact of individuals' expressions, and changing satisfaction over time. We will then introduce you to a new tool that you can use to conduct various data science methods on social media data. We conclude the module with a short primer on R and RStudio.
- Module 2: Customer Satisfaction Analysis
- We will begin our second module with a discussion on different types of data for customer satisfaction analysis. We first focus on survey data and look at different ways to analyze them. Next, we will provide a simple primer on linear and logistic regression. We will wrap up this module with a guided demo of utilizing sentiment analysis on tweets using the Social Media Macroscope.
- Module 3: Customer Satisfaction Influence 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.
- Module 4: Text Summarization
- We will learn about the various methods of text summarization. We begin by discussing 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.