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Course Syllabus

Bachelor 2022/2023

Customer Analytics

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Marketing and Market Analytics)
Area of studies: Management
Delivered by: Department of Marketing
Where: Graduate School of Business
When: 4 year, 1, 2 module
Mode of studies: offline
Open to: students of one campus
Instructors: Alexandr Vladimirovich Krasnikov, Aleksandr Rozhkov
Language: English
ECTS credits: 5
Contact hours: 50

Course Syllabus

Full SyllabusAsk Question

Abstract

In this course the students will study how to use data analytics to learn about customer needs and improve targeting individual consumers. The course will encourage students to apply scientific methods and models to predict and respond to customer choices. This is the key part of learning Big Data. The term Big Data is viewed in the broad sense as it relates to various aspects of the consumer behavior, which may be captured, measured, and transformed to the digital form.Through applications of statistical models to the analysis of the real-world databases, the students will learn how firms may use customer data to serve customers better. The course is based on the analytic process model that represents the complex analytic process as a sequence of steps starting with the problem identification, selection and preparing of the data sources, analyzing data and preparing report for the decision makers. This process model defines the structure of this course and also illustrates how the information can be used in the different business settings and for different business purposes.
Learning Objectives

Learning Objectives

  • The students will learn value-centric approach towards analytics use in the business decision making and understanding of the advanced analytic approaches. The course takes hands-on approach with predictive analytics and equips students with skills that are relevant for business projects.
Expected Learning Outcomes

Expected Learning Outcomes

  • * Get value out of Big Data by using a 5-step process to structure your analysis.
  • Implement relevant tools and methods for customer data analysis
  • Implement and present analytic projects based on RFM, A/B testing, CRM tools
  • Identify tools and methods of data analysis and presentation required to select particular customer segments
  • Assess and develop infographics for customer data analysis communication
  • Identify customer privacy and ethics issues in customer data analysis projects
Course Contents

Course Contents

  • 1 Course Introduction. What is Customer Analytics?
  • 2. RFM Analysis
  • 3. CRM
  • 4. STP framework
  • 5. Cohort analysis
  • 6. Infographics
  • 7. A/B Testing in Marketing
  • 8. NLP
  • 9. Privacy in analytics
Assessment Elements

Assessment Elements

  • non-blocking RFM assignment
    In this assignment you will provided a dataset on customer activity. You will need to conduct analysis according to the RFM framework guidelines discussed in class, deliver the results and provide your comments and conclusions. A more detailed guide, grading criteria and report template will be delivered in class.
  • non-blocking CRM assignment
    In this assignment you will provided a dataset on customer transactions. You will specific tools and methods to identify and target customer segments to maximise company profits. A more detailed guide, grading criteria and report template will be delivered in class.
  • non-blocking Infographics project
  • non-blocking Cohort analysis
  • blocking Exam
    The exam will consist of written assignment that includes test questions and data analysis assignments.
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.2 * Infographics project + 0.15 * CRM assignment + 0.1 * Cohort analysis + 0.4 * Exam + 0.15 * RFM assignment
Bibliography

Bibliography

Recommended Core Bibliography

  • Baesens, B. (2014). Analytics in a Big Data World : The Essential Guide to Data Science and Its Applications. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=761032
  • Krum, R. (2014). Cool Infographics : Effective Communication with Data Visualization and Design. Indianapolis, Indiana: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=654832
  • Nataraj Venkataramanan, & Ashwin Shriram. (2017). Data Privacy : Principles and Practice. Chapman and Hall/CRC.

Recommended Additional Bibliography

  • Abbott, D. (2014). Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst. Indianapolis, Indiana: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=752690
  • Derya Birant. (2011). Data Mining Using RFM Analysis. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.B99B7782
  • Murphy, T., & SAS Institute. (2018). Infographics Powered by SAS : Data Visualization Techniques for Business Reporting / C Travis Murphy. Cary, NC: SAS Institute. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1805001

Authors

  • ROZHKOV ALEKSANDR GENNADEVICH