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.
Applying Data Analytics in Marketing
oferecido por
Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign
Programa - O que você aprenderá com este curso
Semana
16 horas para concluir
Course Introduction
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.
20 vídeos (Total 132 mín.), 6 leituras, 5 testes
20 videos
Interview with Monica Penagos – MOOC intro8min
Tour of R and RStudio5min
Projects3min
Math Function4min
Scalar Variables6min
Column Vectors9min
Data Frame6min
Data Frame Import2min
Help and Cheat Sheets2min
Interview with Monica Penagos – Intro to Customer Satisfaction1min
Lesson 1-1.1 Introduction to Marketing Analytics and Customer Satisfaction5min
Lesson 1-1.2 Marketing Analtyics – A Brief History12min
Lesson 1-2 Customer Satisfaction12min
Lesson 1-3.1 Measurements and Scaling Techniques – Introduction3min
Lesson 1-3.2 Measurements and Scaling Techniques – Primary Scales of Measurement11min
Lesson 1-3.3 Measurements and Scaling Techniques – Comparative Scaling9min
Lesson 1-3.4 Measurements and Scaling Techniques – Non-Comparative Scaling9min
Lesson 1-4.1 Experiment Design: Key Concepts9min
Lesson 1-4.2 Experiment Design: Controlling for Experimental Errors8min
6 leituras
Syllabus30min
About the Discussion Forums10min
Glossary10min
Update Your Profile10min
Module 1 Overview20min
Module 1 Readings1h 30min
5 exercícios práticos
Orientation Quiz10min
Lesson 1-1 Practice Quiz4min
Lesson 1-2 Practice Quiz4min
Lesson 1-3 Practice Quiz10min
Module 1 Graded Quiz30min
Semana
24 horas para concluir
Module 2
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.
8 vídeos (Total 78 mín.), 2 leituras, 4 testes
8 videos
Lesson 2-1.1 A/B Testing: Introduction11min
Lecture 2-1.2 A/B Testing: Types of Tests7min
Lesson 2-1.3 A/B Testing: R Example10min
Lesson 2-2.1 ANOVA – Introduction14min
Lesson 2-2.2 One-Way ANOVA – Insect Spray Example8min
Lesson 2-2.3 One-Way ANOVA – Insect Spray Example in R10min
Lesson 2-2.4 Two-Way ANOVA – Tooth Growth Example11min
2 leituras
Module 2 Overview20min
Module 2 Readings7min
3 exercícios práticos
Lesson 2-1 Practice Quiz8min
Lesson 2-2 Practice Quiz16min
Module 2 Graded Quiz30min
Semana
34 horas para concluir
Module 3
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.
7 vídeos (Total 63 mín.), 2 leituras, 4 testes
7 videos
Lesson 3-1.1 Binary Outcome Model – Logit Model12min
Lesson 3-1.2 Binary Outcome Model – Logit Model Example 111min
Lecture 3-1.3 Binary Outcome Model – Logit Model Example 27min
Lesson 3-2.1 Multi-Dimensional Scaling Introduction10min
Lesson 3-2.2 Conducting an MDS Analysis9min
Lesson 3-2.3 Multi-Dimensional Scaling – R Example9min
2 leituras
Module 3 Overview20min
Module 3 Readings30min
3 exercícios práticos
Lesson 3-1 Practice Quiz4min
Lesson 3-2 Practice Quiz5min
Module 3 Graded Quiz30min
Semana
44 horas para concluir
Module 4
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
11 vídeos (Total 70 mín.), 2 leituras, 3 testes
11 videos
Lecture 4-1.1 Conjoint Analysis – Introduction7min
Lesson 4-1.2 Conjoint Analysis – Data Collection Methods9min
Lesson 4-1.3 Conjoint Analysis – Types of Conjoint Analysis2min
Lesson 4-1.4 Conjoint Analysis – Part-Worth Utilities3min
Lesson 4-1.5 Conjoint Analysis Example – Ferry Fares10min
Lesson 4-1.6 Conjoint Analysis – Attributes10min
Lesson 4-1.7a Conjoint Analysis R Example – Setup5min
Lesson 4-1.7b Conjoint Analysis R Example – Part 110min
Lesson 4-1.7c Conjoint Analysis R Example – Part 25min
Course Summary: Applying Data Analytics in Marketing1min
2 leituras
Module 4 Overview20min
Module 4 Readings1h
2 exercícios práticos
Lesson 4-1 Practice Quiz24min
Module 4 Graded Quiz30min
Instrutores
Sung Won Kim
Associate ProfessorBusiness Administration
Sobre Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em 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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