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Regular version of the site
Bachelor 2019/2020

Customer Analytics

Category 'Best Course for Career Development'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Marketing and Market Analysis)
Area of studies: Management
Delivered by: Department of Strategic Marketing
When: 3 year, 1, 2 module
Mode of studies: distance learning
Instructors: Aleksandr Rozhkov
Language: English
ECTS credits: 4

Course Syllabus


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.SAS is a programing and analytical environment that is widely used by industry professionals and has capabilities of advanced statistical modeling. 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

  • Provide overview of major models used to classify and describe customers
  • Learn appropriate analytical methods for collecting, analyzing and interpreting numerical customer information and apply these inputs in business decision-making
  • Develop specific skills, competencies and points of view needed by analytics professionals in the field.
Expected Learning Outcomes

Expected Learning Outcomes

  • Learning of basic SAS features and tools, registration and login procedures.
  • Stages of analytics process in a company
  • Knowledge of Types of variables
  • Learning of SAS interface and basic operations in the program
  • Learning CRM concepts and algorithms
  • Learning segmentation, targeting and and positioning concepts
  • Learning about market segmentation concept and basics of cluster analysis
  • RFM analysis elements and quality metrics (Lifts & Gains)
  • Features of Logistic regression, logistic regression implementation in SAS
  • Concept and composition of neural networks
  • Confusion Matrix: concept and composition, related metrics: Precision and Recall
  • Decision trees algorithm and features
  • SAS Viya key features discussion - registration process
  • Textual data analysis task and issues
  • Prediction algorithms based on Text analysis
Course Contents

Course Contents

  • Introduction to SAS-on-Demand
  • Value-Driven Analytics Process
  • Types of Variables. Associations between Variables
  • CRM - Managing Customer Relationships for Profit
  • SAS Practicum: Descriptive Stats, Association, Regression
  • Market Segmentation - Cluster Analysis
  • STP - Segmentation, Targeting, and Positioning
  • Prospecting & Targeting Right Customer - RFM Lifts and Gains. Model Assessment I
  • Predicting Response with Logits
  • Predicting Customer Response with Neural Networks
  • Model Assessment II. Confusion Matrix.
  • Decision Trees and Ensemble Models
  • SAS Viya: Practicum for Supervised ML (Banking case)
  • Predicting Responses using Textual Analytics
  • Analysis of Unstructured Data. Textual Analytics
Assessment Elements

Assessment Elements

  • non-blocking Register and access course at SAS on-demand site (Assignment)
  • non-blocking SAS practicum
  • non-blocking RFM (Home Assignment)
  • non-blocking Neural networks (Assignment)
  • non-blocking Decision Trees (assignment)
  • non-blocking Text analysis (assignment)
  • non-blocking In class discussion
  • blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.1 * Decision Trees (assignment) + 0.4 * Exam + 0.2 * In class discussion + 0.1 * Neural networks (Assignment) + 0.1 * RFM (Home Assignment) + 0.1 * Text analysis (assignment)


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

Recommended Additional Bibliography

  • Samaddar, S., & Nargundkar, S. (2019). Data Analytics : Effective Methods for Presenting Results. Boca Raton, FL: Auerbach Publications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2026397