Master 2020/2021

Customer analyst

Category 'Best Course for Career Development'

Type: Elective course (SMART-Marketing: Data, Analysis, Insight)

Area of studies: Management

Delivered by: School of Management

Where: Faculty of Economics, Management, and Business Informatics

When: 1 year, 4 module

Mode of studies: distance learning

Instructors: Aleksandra Sazhina

Master’s programme: Smart-Marketing: Data, Analysis, Insight

Language: English

ECTS credits: 5

Contact hours: 2

Course Syllabus

Abstract

In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. Blended format, based on https://www.coursera.org/learn/wharton-customer-analytics

Learning Objectives

• This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics.

Expected Learning Outcomes

• Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions
• Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool
• Communicate key ideas about customer analytics and how the field informs business decisions
• Communicate the history of customer analytics and latest best practices at top firms

Course Contents

• Introduction to Customer Analytics
  What is Customer Analytics? How is this course structured? What will I learn in this course? What will I learn in the Business Analytics Specialization?
• Descriptive Analytics
  In this module, you’ll learn what data can and can’t describe about customer behavior as well as the most effective methods for collecting data and deciding what it means. You’ll understand the critical difference between data which describes a causal relationship and data which describes a correlative one as you explore the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. You’ll also learn how data is used to explore a problem or question, and how to use that data to create products, marketing campaigns, and other strategies. By the end of this module, you’ll have a solid understanding of effective data collection and interpretation so that you can use the right data to make the right decision for your company or business.
• Predictive Analytics
  Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes.
• Prescriptive Analytics
  How do you turn data into action? In this module, you’ll learn how prescriptive analytics provide recommendations for actions you can take to achieve your business goals. First, you’ll explore how to ask the right questions, how to define your objectives, and how to optimize for success. You’ll also examine critical examples of prescriptive models, including how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising.

Assessment Elements

• online course results
  The online course grade is defined according to the rules online courses.
• in-class examination results
  The exam is in the form of a computer test. The exam is conducted on the online course platform. You must connect to the exam according to the schedule of the online course exam. The student's computer must meet the requirements: Internet availability, online course support. To participate in the exam, the student must pass the test at https://www.coursera.org/learn/wharton-customer-analytics. Use of additional materials is prohibited. A short-term communication disruption during the exam is considered a communication disruption of less than a minute. Long-term communication disruption during the exam is considered a violation of a minute or more. In case of a long-term communication disruption, the student cannot continue to participate in the exam. The transfer procedure is similar to the surrender procedure.

Interim Assessment

• Interim assessment (4 module)
  0.4 * in-class examination results + 0.6 * online course results

Bibliography

Recommended Core Bibliography

• Consumer behaviour in a changing world : food, culture and society. 2016. Ed. by Fabio Verneau and Christopher Griffith Emerald Group Publishing Limited https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4514345.
• Hester van Herk, Carlos J. Torelli. Cross cultural issues in consumer science and consumer psychology. Springer, 2017. Доступ через электронную библиотеку НИУ ВШЭ, для перехода по ссылке нужна авторизация в системе удаленного доступа ресурса. http://link.springer.com

Recommended Additional Bibliography

• Mauro Cavallone. Marketing and Customer Loyalty. – Springer, 2017. Доступ через электронную библиотеку НИУ ВШЭ, для перехода по ссылке нужна авторизация в системе удаленного до-ступа ресурса. http://link.springer.com