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Regular version of the site

Analytical Tools Expert (Google IQ, Yandex Metrica Expert)

2022/2023
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Compulsory course
When:
1 year, 3 module

Instructor


Мищенко Вероника Алексеевна

Course Syllabus

Abstract

This course is aimed to make an understanding of Web and Digital Marketing Analytics. This is a beginner level course but even experienced analysts have found it to be effective in reminding them of the process they need to follow. Students will learn the Web Analytics and Digital Analytics process using different tools such as Google Analytics, Yandex Metrica, Google Trends, Yandex Wordstat and so on.
Learning Objectives

Learning Objectives

  • Students master their skills in web and marketing analysis
  • Learn the most important marketing metrics and how to apply them to data
  • Understand the fundamentals and process for web analytics to make their digital marketing better
  • Understand what are some of the most common measurement platforms, from web and app analytics to email marketing measurement
  • Demystify the common definitions in Web Analytics
  • Understand the difference between Web Analytics and Digital Analytics
  • Learn how to go full circle with Digital and Web Analytics
  • Become confident in using Digital and Web Analytics skills
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to identify cases when it is possible to use Digital and Web Analytical Tools
  • The understanding of what a fundamental concepts in marketing and the measurement of marketing
  • Be able to analyse online behavior of customers
  • Be able to select the right metrics based on your business objective
  • The understanding of how to use different Digital Analytical Tools
Course Contents

Course Contents

  • Marketing Analytics Strategy
  • Web, Social Media and Digital Advertising Analytics
  • Customer Analytics
Assessment Elements

Assessment Elements

  • non-blocking Team Project
  • non-blocking Personal Project
  • non-blocking Examination Test
  • non-blocking Seminar Activity
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.2 * Examination Test + 0.4 * Team Project + 0.3 * Seminar Activity + 0.1 * Personal Project
Bibliography

Bibliography

Recommended Core Bibliography

  • Linoff, G., & Berry, M. J. A. (2011). Data Mining Techniques : For Marketing, Sales, and Customer Relationship Management (Vol. 3rd ed). Indianapolis, Ind: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=520245

Recommended Additional Bibliography

  • Provost, F., & Fawcett, T. (2013). Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking (Vol. 1st ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=619895

Authors

  • BUDKO VIKTORIYA ALEKSANDROVNA