Анализ потребителей

Бакалавриат 2020/2021

Статус: Курс обязательный (Маркетинг и рыночная аналитика)

Направление: 38.03.02. Менеджмент

Кто читает: Департамент маркетинга

Где читается: Высшая школа бизнеса

Когда читается: 3-й курс, 1, 2 модуль

Формат изучения: без онлайн-курса

Преподаватели: Рожков Александр Геннадьевич

Язык: английский

Кредиты: 4

Контактные часы: 40

Full Syllabus Ask 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.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

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

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

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

Register and access course at SAS on-demand site (Assignment)
SAS practicum part 1
RFM (Home Assignment)
Neural networks (Assignment)
Decision Trees (assignment)
Text analysis (assignment)
In class discussion
Exam
Оценки, входящие в формулу результирующей оценки за дисциплину, при ее расчете не округляются. Способ округления результирующей оценки по дисциплине: арифметический. Автоматов по данной дисциплине нет. // The grades included in the resulting grade for the discipline are not rounded up during the calculation. Method of rounding the resulting grade by discipline: arithmetic. There are no exam waivers for this discipline.
SAS Practicum Part 2
Analytics and CX ( case analysis)
This assignment is based on the SAS-Forrester case webinar, students are expected to provide analysis/ reflection of the situation presented.
Midterm Exam

Interim Assessment

Interim assessment (1 module)
0.15 * Analytics and CX ( case analysis) + 0.5 * Midterm Exam + 0.05 * Register and access course at SAS on-demand site (Assignment) + 0.15 * SAS practicum part 1 + 0.15 * SAS Practicum Part 2
Interim assessment (2 module)
0.1 * Decision Trees (assignment) + 0.5 * Exam + 0.1 * In class discussion + 0.1 * Neural networks (Assignment) + 0.1 * RFM (Home Assignment) + 0.1 * Text analysis (assignment)

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

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

Course Syllabus