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Обычная версия сайта
Бакалавриат 2024/2025

Маркетинговая аналитика

Статус: Курс по выбору (Прикладной анализ данных)
Когда читается: 4-й курс, 1 модуль
Охват аудитории: для своего кампуса
Язык: английский

Course Syllabus

Abstract

Marketing Analytics: Collect, organize, and analyze marketing data, solve marketing problems using the most appropriate technique. Within Marketing Analytics, we aim at designing a decision architecture to aid better decision making. This course is about generating marketing insights from empirical data in such areas as segmentation, targeting and positioning, satisfaction management, customer lifetime analysis, customer choice, and product and price decisions using conjoint analysis. This will be a hands-on course based on KNIME workbench, and a spreadsheet software.
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

  • Implement relevant tools and methods for customer data analysis
  • 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
  • Use key marketing metrics and the basics of customer analytics
  • Define the marketing analytics problem
  • Implement and present analytic projects based on RFM, STP process, and smartsheets
  • Discuss alternative solutions effectively
  • Determine/set the best course of action
  • Demonstrate understanding of data-driven decision modeling
  • Be able to communicate the marketing analytics project with stakeholders
Course Contents

Course Contents

  • Introduction to Analytics in Marketing
  • New product design using conjoint analysis
  • Segmentation, Targeting and Positioning (STP) process
  • RFM, Response models
Assessment Elements

Assessment Elements

  • non-blocking In-class exercises
  • non-blocking HW1
  • non-blocking HW2
  • non-blocking HW3
  • non-blocking HW4
  • non-blocking Online exam
Interim Assessment

Interim Assessment

  • 2024/2025 1st module
    0.1 * HW1 + 0.1 * HW2 + 0.1 * HW3 + 0.2 * HW4 + 0.2 * In-class exercises + 0.3 * Online exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Marketing analytics : a practitioner's guide to marketing analytics and research methods, Charan, A., 2015

Recommended Additional Bibliography

  • Rackley, J. (2015). Marketing Analytics Roadmap : Methods, Metrics, and Tools. [Berkley]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1000698

Authors

  • Абдулхакимов Мухиддин Мураджанович