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Data Science and Qualitative Methods for Marketing Analytics

This course unlocks a huge range of qualitative and quantitative instruments to expand your experience in marketing and advertising! 

Course Description

This course introduces hands-on approaches to solving marketing problems using quantitative and qualitative data. Multiple specific problems/cases will be showcased from various fields of marketing analytics: marketing research, customer analytics, social network analysis, advertising, etc. Both quantitative (statistical analysis of substantial volumes of sales/survey/customer data) and qualitative (in-depth interviews, content analysis) methods will be considered.

The course is taught by Evgeny A. Antipov, PhD, Associate Professor, Department of Management, HSE – St. Petersburg; Elena B. Pokryshevskaya, Associate Professor, Department of Management, HSE – St. Petersburg; and Anna O. Daviy, Lecturer, Department of Management, HSE – St. Petersburg. 

Rapid changes in the business world are creating new decision-pressing situations that demand better skills for the acquisition and the use of management and marketing information

Anna O. Daviy
Course Lecturer

The course features a unique set of computer exercises using R, Excel and other packages that will give our students skills that are immediately applicable in the industry

Evgeny A. Antipov
Course Lecturer

Why Choose This Course?

  • Hands-on: students will use Excel and R during most classes
  • Templates for solving real-world problems will be provided
  • Lecturers are open for future research collaborations leading to peer-reviewed journal publications
  • Students will be granted free access to DataCamp.com platform for learning data science for the period of the course and several months after the course


  • Advanced Excel functions useful for marketing analysis
  • Optimization modeling in marketing
  • Exploratory analysis of marketing data
  • Data processing of customer data
  • Customer segmentations
  • Introduction to predictive modeling and its applications: modeling customer lifetime value, churn, etc.
  • Sales modeling
  • Conjoint Analysis
  • Introduction to text mining for marketing
  • Qualitative research methods. In-depth interviews. Rationale for using qualitative research. Questioning techniques in qualitative research. Steps in developing the guide and conducting an in-depth interview. Focus group interviews
  • Qualitative data analysis. Content analysis. Steps of content analysis. Software for content analysis

Skills and Competence

Upon completion of this course, students will be able: 

  • Use Excel and R for solving some of the key marketing problems
  • Extract maximum insight from various data sources available to a company
  • Work out main errors in data collection tools (e.g. in-depth interview guide) and generating ideas on how to resolve them
  • Design data collection forms for the qualitative research that is conducted during the course
  • Interpret the result of data analysis, followed by reporting and communicating the results

Teaching Methods

The quantitative part of the course will contain tutorial sessions where students will conduct analysis along with the lecturers followed by a session where they will solve similar problems on their own to check their understanding. The qualitative part of the course will contain lectures, workshops, practical training and group projects.


  • Mandatory: basic knowledge of Microsoft Excel
  • Some training in general management is recommended, but participants with other backgrounds are also welcomed
  • Introductory knowledge of R programming is preferable but not mandatory. We recommend at least studying A (very) short introduction to R

Final Assessment 

Quantitative part: final problem set solved in class (time limit: 80 minutes). Qualitative part: presentation. 

Final Grade Background

In order to complete the course students have to attend no less than 70% of lectures and seminars. The final grade will be computed according to the following scheme: 0.6*Grade for the Quantitative part + 0.4*Grade for the Qualitative part. 

Recommended Reading List

Hodeghatta, U. R., & Nayak, U. (2016). Business Analytics Using R-A Practical Approach. Apress.

Chapman, C., & Feit, E. M. (2015). R for marketing research and analytics. New York, NY: Springer.

Hair, J. F., Bush, R. P., & Ortinau, D. J. (2008). Marketing research. McGraw-Hill Higher Education.

Malhotra, N. K., & Birks, D. F. (2000). EIS Inc. Marketing research: an applied approach.

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