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Data-driven Decision-making

2017/2018
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 3, 4 модуль

Преподаватель

Course Syllabus

Abstract

In addition to having conceptual skills, modern managers must increasingly master techniques of data-driven decision modeling to do strategic planning based on information from corporate information systems as well as external data sources. This course teaches how to apply statistical, machine learning and optimization techniques to business contexts, especially marketing problems. The course consists of 4 modules: Module 1. Optimization models Module 2. Analytical CRM Module 3. Marketing Mix and Demand Modeling Module 4. Foundations of Text Analytics
Learning Objectives

Learning Objectives

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical business research using modern analytic software tools
  • Develop and apply new research methods
  • Solve managerial problems using best practices of data analysis using modern computational tools
Expected Learning Outcomes

Expected Learning Outcomes

  •  Choose methods adequately corresponding to the objectives of a research project
  •  Collect, store, process and analyze data according to high standards
  •  Conduct empirical business research using modern analytic software tools
  •  Develop and apply new research methods (ОПК-2)
  •  Solve managerial problems using best practices of data analysis using modern computational tools
Course Contents

Course Contents

  • Introduction to business analytics
  • Optimization models-1: Linear programming. Advertising budget optimization
  • Optimization models-2: Integer programming. Shelf space optimization
  • Introduction to CRM analytics. Database management and segmentation in R.
  • Customer acquisition models
  • Customer retention models-1. Predicting customer activity and future best customers. Probabilistic Models for Assessing and Predicting your Customer Base.
  • Customer retention models-2. Machine Learning methods for Assessing and Predicting your Customer Base.
  • Balancing acquisition and retention
  • Customer churn
  • Customer win-back
  • Product recommendation. Cross-selling. Market Basket Analysis.
  • Implementing CRM models. The future of CRM analytics.
  • Product and concept testing
  • Site selection problems
  • Promotional analytics
  • Causal impact analysis
  • Multichannel attribution models. Analysis of clickstream data.
  • New product design and pricing research. Conjoint analysis. Market shares simulation.
  • Text Analytics based on Data Camp's Text Mining: Bag of Words
Assessment Elements

Assessment Elements

  • non-blocking Test
  • non-blocking DataCamp
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.28 * DataCamp + 0.3 * Exam + 0.42 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737

Recommended Additional Bibliography

  • Artun, O., & Levin, D. (2015). Predictive Marketing : Easy Ways Every Marketer Can Use Customer Analytics and Big Data. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1050355