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.
This course is part of the Business Analytics Specialization
Offered By
About this Course
A basic familiarity with R is recommended.
Skills you will gain
- Marketing
- regression
- Analytics
- Data Analysis
- Marketing Analytics
A basic familiarity with R is recommended.
Offered by
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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Syllabus - What you will learn from this course
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 both the Social Media Macroscope and R Studio.Â
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.Â
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TOP REVIEWS FROM APPLYING DATA ANALYTICS IN MARKETING
This course is really insightful. Explanation done very well, quizzes is related and challenging. Although I suggest you have a statistical background before taking this course
If the peer reviews were done faster it would be better
it was a perfect course , which gave me the full picture of how to make a marketing testing and evaluation
Very informative. Good beginning to start the journey into analytics for marketers.
About the Business Analytics Specialization
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.
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