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Бакалаврская программа «Социология и социальная информатика»

Topics in Business Applications of Data Analysis

2018/2019
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
3-й курс, 1, 2 модуль

Преподаватели

Course Syllabus

Abstract

This course is blended with a MOOC where we will go through 4 techniques and practical cases where business analytical tasks are solved with research instruments and data analysis approaches. We will discuss these cases, practise methods that can apply, and look at the business context of each case. The course is targeted at those aiming at business-oriented jobs in marketing, sales and service analytics, etc., and requires a large amount of independent study. https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling
Learning Objectives

Learning Objectives

  • help students to appropriate the methods and concepts they have learned in data analysis, research methods, and other courses as applied in marketing
Expected Learning Outcomes

Expected Learning Outcomes

  • be able to apply data analysis techniques in R in estimating the customer lifetime value, churn prevention, modelling time to reorder, and reducing data complexity
Course Contents

Course Contents

  • Predictive analytics. Modelling Customer Lifetime Value with Linear Regression
    Basic concepts of consumer behavior and client analytics. Classification of consumer behaviour models. Generic marketing strategies. Types of business models. Statistical methods for client analytics. Predicting client’s life-time value (LTV) with linear regression in R.
  • Churn Prevention Analysis
    How to predict customer churn? How to detect and prevent customer churn? Push-pull-mooring paradigm for churn and service switching, measurement of latent variables: satisfaction and expectation disconfirmation. Models of satisfaction, expectation, performance, disconfirmation. Predicting client’s churn with logistic regression in R.
  • Time to Reorder and Lifetime Values with Survival Models
    CPH model interpretation, calculation of customer lifetime value. Addressing churn using segmentation and advertisement. Description of task and data for the final project. Predicting time till next purchase with survival analysis in R.
  • Reducing Dimensionality with PCA
    Understanding the way customers interact with websites, dimension reduction and segmentation, Markov chain models and Sequential association rules. Clickstream Analysis in client analytics. Applications of principal component analysis in customer relationship management (CRM) in R.
Assessment Elements

Assessment Elements

  • non-blocking MOOC results
    MOOC grade includes successful in-time completion of the four chapters of the online-course assigned by instructors (exercises and theory from all the chapters should be completed within the time assigned). Late completion is penalised.
  • non-blocking In-class activity
  • non-blocking Case-based project 1
    Project. Students create teams of 2-3 and work together on their projects. The first project focuses on reworking the programming code in R in order to calculate customer life-time value. Project details are available in LMS.
  • non-blocking Case-based project 2
    Project. Students create teams of 2-3 and work together on their projects. The second project is due towards the end of the course, and it reproduces the whole cycle of customer analytics, from data screening, to analysis, to creating a report targeted at the company’s management. The second project is reported as individual scripts of team members, so that individual contribution is transparent, and the final script that is meant for the client. Project details are available in LMS.
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.1 * Case-based project 1 + 0.3 * Case-based project 2 + 0.4 * In-class activity + 0.2 * MOOC results
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

  • De Vries, A., & Meys, J. (2015). R For Dummies (Vol. 2nd edition). Hoboken, New Jersey: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1017482