Smart Data Driven Marketing
Objectives
In
this curricular unit, students acquire the skills to make smart and impactful
marketing decisions informed by the
right data. Marketing
data isbecoming ubiquitous, and
big international
companies
are increasingly investing
in data analytics;
however, the irony
of having too
much data is that it is often hard to distinguish the insights from the
clutter, and managers end up with too much data and too little information.
Thankfully,
there are common ingredients of good data analysis and marketing decisions that
can enhance decision-making confidence. Students
will acquire the
right mindset and
analytic approach to transform data into insight; they will learn how to
find the right data sources/typesto
inform marketing decision-making, and how
to interpret these
data to make
effective marketing decisions.
General characterization
Code
14223
Credits
2
Responsible teacher
Irene Consiglio
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
Available soon
Bibliography
•Boll,
Jack B., Katherine L. Milkman, and John W. Payne (2015). Outsmart Your Own
Biases.Harvard Business Review.
•Davenport,
Thomas H. (2006). Competing on Analytics. Harvard Business Review.
•Levitt
Steven D. and Stephen J. Dubner (2014). Think Like a Freak: How to Think
Smarter about Almost Everything. London, England: Allen Lane.
•MacGarvie, Megan
and Kristina McElheran
(2018). Data Analytics: From
Bias to Better Decisions.Harvard Business Review.
•Thomke, Stephan
H. (2020). Experimentation Works:
The Surprising Power
of Business Experiments. Boston,
Massachusetts: Harvard Business Review Press.
Teaching method
Through a combination of readings, lectures, exercises and class discussion, this curricular unit provides the latest tools, techniques and cutting-edge thinking to put Students at the forefront of making intelligent marketing decisions and realising the full potential of marketing data and analytics.
Evaluation method
This curricular unit will be assessed through an examination (50%), group work and presentation (25%) and class participation (25%).
Subject matter
GOOD
DATA, BAD DATA
–Asking
the right questions
Marketing
problem definition
Thinking
big vs. thinking small
Changing
perspectives
–Finding
right data & ignoring useless data
Matching
marketing problems with their data
Causality
vs. observation
Finding
the right data
Ignoring
bad data
Workshop
1: Application of theory part 1 and 2
In-class
mini-cases, exercises, and class discussion
Good
questions & bad questions
Good
data & bad data
GOOD
DECISIONS, BAD DECISIONS
–Smart
data analysis
Developing
critical thinking
Identifying
Assumptions/Confounds
Establishing
the right analysis
Experimentation
assignments
–Smart
decisions
Actionable
insights
Deriving
insights from data
Workshop
2: Application of theory part 3 and 4
In-class
mini-cases, exercises, and class discussion
Good
analysis & bad analysis
Good
decisions & bad decisions