• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Master 2020/2021

Data-driven Culture

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Compulsory course (Marketing)
Area of studies: Management
Delivered by: Department of Marketing (Nizhny Novgorod)
When: 1 year, 3 module
Mode of studies: offline
Instructors: Sergey Alexandrovskiy, Любчанская Елена Александровна
Master’s programme: Marketing
Language: English
ECTS credits: 6
Contact hours: 40

Course Syllabus

Abstract

The course helps students to learn how to set analytic goals, work with data in a team, select data analysis methods, and make decisions based on data. If students know basic statistics and use Python, R, or SPSS for analysis, it helps students to better understand the course topics.
Learning Objectives

Learning Objectives

  • Help students to understand what it means for an organization to be data-driven and how does an organization get there.
Expected Learning Outcomes

Expected Learning Outcomes

  • Build a data-driven culture in an organization
  • Make data-driven decisions
  • Create and tell stories with data
Course Contents

Course Contents

  • Introduction to Data-driven culture
    Who the analyst is. Competencies and skills. Labor market. Junior interview case.
  • Strategy based on analytics
    Metric development, the choice of a system for structuring metrics and KPI in a company.
  • Client analytics
    Client base analysis tools in CRM. System selection (criteria, review). Contractor Selection. TR for revision. Pipeline construction. Marketing Strategy: Customer Segmentation. JTBD. CJM.
  • Visualization as an analytics tool
    Rules for building a presentation, methods of data visualization.
  • Teamwork with data
    Google docs, tables, slides. Teams or Slack. Data storage. Reports and visualization.
  • Goal and data sources
    How to understand a goal (a client). Online data sources. Panel data. Offline data sources.
  • Feedback
    How to collect and analyze feedback data.
  • Must-have tools for analysis
    Forecasting. Cohort analysis. Comparing segments.
  • Financial analytics
    Financial metrics and Unit analysis.
Assessment Elements

Assessment Elements

  • non-blocking Homework
  • non-blocking Homework
  • non-blocking Homework
  • non-blocking Homework
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.25 * Homework + 0.25 * Homework + 0.25 * Homework + 0.25 * Homework
Bibliography

Bibliography

Recommended Core Bibliography

  • Anderson, C. (2015). Creating a Data-Driven Organization : Practical Advice From the Trenches (Vol. First edition). Beijing: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1045097

Recommended Additional Bibliography

  • Ariely, Dan. The Upside of Irrationality: The Unexpected Benefits of Defying Logic at Work and at Home [Электронный ресурс] / Dan Ariely; БД books24х7. – HarperCol-lins, 2010. – 160 pages. – ISBN 978-0061995040. – Режим доступа: http://common.books24x7.com/toc.aspx?bookid=48980. – Загл. с экрана.
  • Phillips, Tim. Data-Driven Business: Use Real-Life Numbers to Improve Your Business by 352% [Электронный ресурс] / Tim Phillips; БД books24х7. – Infinite Ideas, 2016. – 160 pages. – ISBN 978-1908984609. –Режим доступа: http://common.books24x7.com/toc.aspx?bookid=130361. – Загл. с экрана.