University of Illinois at Urbana-Champaign
Applying Data Analytics in Marketing
University of Illinois at Urbana-Champaign

Applying Data Analytics in Marketing

This course is part of Business Analytics Specialization

Taught in English

Some content may not be translated

Unnati Narang
Joseph T. Yun

Instructors: Unnati Narang

21,018 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(143 reviews)

Intermediate level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(143 reviews)

Intermediate level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

See how employees at top companies are mastering in-demand skills

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This course is part of the Business Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

19 videos7 readings2 quizzes1 discussion prompt1 plugin

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.

What's included

7 videos2 readings1 quiz

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.

What's included

7 videos2 readings1 quiz1 peer review

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. 

What's included

6 videos4 readings1 quiz1 plugin

Instructors

Instructor ratings
4.7 (40 ratings)
Unnati Narang
University of Illinois at Urbana-Champaign
2 Courses60,326 learners

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Recommended if you're interested in Marketing

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4.5

143 reviews

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KK
5

Reviewed on Aug 25, 2020

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Reviewed on Jan 3, 2021

YY
5

Reviewed on Nov 2, 2019

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