Insegnamento a.a. 2023-2024

30420 - MARKETING ANALYTICS

Department of Marketing

Course taught in English
Go to class group/s: 25
BEMACS (8 credits - I sem. - OB  |  SECS-P/08)
Course Director:
JESSICA JUMEE KIM

Classes: 25 (I sem.)
Instructors:
Class 25: JESSICA JUMEE KIM


Suggested background knowledge

Background knowledge on statistics, economics, and econometrics would be strongly recommended. In addition, data analysis skills would be assets.

Mission & Content Summary

MISSION

In today’s information economy companies have access to data about markets, products, customers, and much more. This course provides you with the tools and methods to leverage data and analytics to make business recommendations and shape a marketing strategy.

CONTENT SUMMARY

Course topics:

This list is tentative - some topics may be modified or added on the syllabus.

 

  1. Market and Consumer-level analysis. We cover:
    • Consumer choice model.
    • Marketing Mix and Market Response Model.
  2. Advertising, Brand, and Customer Analytics
    • Advertising metrics
    • Brand management and brand valuation
    • Two sides of customer value: value to customers and values to firms
    • Customer acquisition and retention
    • Valuing customers and customer metrics
  3. Social Media and Marketing:  
    • Marketing stategy in online environment
    • Online marketing metrics
    • Social media and social networks

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Understand and interpret marketing and consumer data. 
  • Learn the marketing metrics and tools for analyzing customers and firm decisions.
  • Learn the principle of customer management in terms of acquisition and retention.
  • Communicate a story using data and data visualization.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Conduct data analysis to interpret customer and/or firm decisions
  • Understand different metrics to assess customer profitability and brand equity.
  • Develop a marketing strategy - prioritize customers and select appropriate actions across different segments.
  • Analyze firm marketing decisions (e.g., advertising) and measure its performance.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Group assignments

DETAILS

All methods other than face-to-face lectures are used to provide exercises and examples of the application of theoretical concepts and models.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
    x
  • Group assignment (report, exercise, presentation, project work etc.)
x    
  • Active class participation (virtual, attendance)
x    

ATTENDING STUDENTS

With the purpose of measuring the acquisition of the above-mentioned learning outcomes, the students’ assessment is based on the following main components:

  • Group assignments aimed to test the students’ ability to identify a marketing problem, analyze and interpret customer decisions or firm marketing decisions using panel data, and find possible solutions. Students gain experience by applying some of the methods and tools learned in the course to solving real-world marketing analytics problems.
  • Written exam. The written exam consists of open/closed answers questions aimed to assess students’ understanding of the mechanisms of consumer data analytics and principles of customer management as well as students’ ability to apply the methods learned in the course in various marketing analytics tasks.
  • Class Participation.

NOT ATTENDING STUDENTS

Written exam. The written exam consists of open/closed answers questions that covers all chapters of the text book. The exam aims to assess students’ understanding of the mechanisms of consumer data analytics as well as principles of customer and marketing management.


Teaching materials


ATTENDING STUDENTS

  • Lecture notes
  • Hand-outs
  • Software: Stata or R 

 

Recommended Reference

  • Peter C. Verhoef, Edwin Kooge, and Natasha Walk (2016), Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Routledge; ISBN-10: 9781138837973; ISBN-13: 978-1138837973

NOT ATTENDING STUDENTS

 

Textbook for non-attending students:

Peter C. Verhoef, Edwin Kooge, and Natasha Walk (2016), Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Routledge; ISBN-10: 9781138837973; ISBN-13: 978-1138837973

Last change 04/06/2023 15:53