This document outlines the course introduction for the MITB - B.11 Marketing Analytics and Applications course. The instructor, Masao Kakihara, introduces himself and provides an overview of the course objectives, topics, structure and evaluation. Key topics to be covered include trends in marketing analytics, macro/micro environments, and challenges of data abundance. The course will help students understand marketing analytics landscapes, integrate various data methodologies and translate data into actionable strategies.

1. Session 1:
Course Introduction
Instructor:
Masao Kakihara, Ph.D.
MITB - B.11 Marketing Analytics and Applications
AY2016-17 Term 1

2. All rights reserved © Masao Kakihara
Today’s Agenda
2
● Introduction of you & me
● Course objectives, topics, and structure
● Evaluation
● Introductory discussions
○ Key trends in marketing analytics
○ Macro/micro environment of marketing analytics
○ Marketing challenges in the era of ‘data abundance’

3. All rights reserved © Masao Kakihara
About me…
3
● A basketball kid with a PC in Kobe, Japan
● An Economics student playing hockey
● Joined a small consulting firm in Tokyo [4 y]
● Postgraduate study in London, earned Ph.D. in Information Systems [4 y]
● Accidentally a university professor [5 y]
● Back to industry, joined Yahoo! Japan Research [3.5 y]
● Joined Google Japan, working in Market Insights team [1.5 y]
● Moved to Singapore, doing market research for Southeast Asia [3.5 y + ?]

4. All rights reserved © Masao Kakihara
Rapidly changing business
environments, largely driven by
digital technologies
Data abundance in marketing
analytics
The lack of knowledge of
translating data to insights and
strategies
Course Objectives
4
Backgrounds Course objectives
Understand an overall landscape
of data analytics for marketing
decision making in a dynamic
business environment
Learn a framework to integrate
various data analytics
methodologies and practices
Acquire a capability to translate
data analytics into actionable
marketing strategies and
influence stakeholders
1.
2.
3.
Rapidly changing business
environments, largely driven by
digital technologies
‘Data abundance’ in marketing
decision making
The lack of knowledge of
translating data into insights and
strategies

5. All rights reserved © Masao Kakihara
Class Schedule (1/3)
5
Session Topic Key contents (3 hours per session) Pre-session readings
1
25/Aug
Introduction ● A course overview
● Key trends in marketing analytics
● Macro/micro environment of
marketing analytics
● “Big Data: The Management Revolution” HBR,
Oct 2012.
● “Beyond the Hype: The Hard Work Behind
Analytics Success”, MIT SMR, Mar 2016.
2
1/Sep
Ecosystem of Marketing
Metrics
● Systems and structures of marketing
metrics
● Marektging funnels
● Data landscape for marketing decision
making
● “Marketing Metrics” (Main Ref.), Chapter 1.
3
8/Sep
Analytics for Marketing
Planning - 1
● Macro trend analysis
● Competitive landscape analysis
● “How Smart, Connected Products Are
Transforming Competition”, HBR, Nov 2014.
● “The Definitive Guide To (8) Competitive
Intelligence Data Sources”, A. Kaushik, 2010.
4
15/Sep
Analytics for Marketing
Planning - 2
● Market share
● Consumer funnels and journey
* Due for the 1st assignment
● “Marketing Metrics” (Main Ref.), Chapter 2.
● “The consumer decision journey”, McKinsey
Quarterly, Jun 2009.

6. All rights reserved © Masao Kakihara
Class Schedule (2/3)
6
Session Topic Key contents (3 hours per session) Pre-session readings
5
22/Sep
Analytics for Marketing
Execution - 1
● Revenue, cost, profit
● Customer value and profitability
● Sales force and channel management
● “Marketing Metrics” (Main Ref.), Chapter 3-6.
6
29/Sep
Analytics for Marketing
Execution - 2
● Brand equity
● Pricing
● Promotion
* Due for the 2nd assignment
● “Marketing Metrics” (Main Ref.), Chapter 7-8.
No class on 6 & 13/Oct
7
20/Oct
Analytics for Marketing
Execution - 3
● Advertising
● Marketing effectiveness
● “Marketing Metrics” (Main Ref.), Chapter 9.
8
27/Oct
Analytics for Marketing
Measurement - 1
● Measurement frameworks
● Resource allocation planning
● ROI
● “Marketing Metrics” (Main Ref.), Chapter 11-13.
● “Current industry approaches towards
Marketing ROI an Empirical study”, European J.
of Bus. Mgmt, Vol 3, No.6, 2011.

7. All rights reserved © Masao Kakihara
Class Schedule (3/3)
7
Session Topic Key contents (3 hours per session) Pre-session readings
9
3/Nov
Analytics for Marketing
Measurement - 2
● Cross-media attribution
● Marketing Mix Modeling
* Due for the 3rd assignment
● “Cross-Channel Attribution Is Needed to Drive
Marketing Effectiveness”, Forrester, 2014.
● “Measure What Matters Most: A Marketer's Guide”,
Think with Google,
10
10/Nov
Digital Marketing ● Digital marketing metrics
● Mobile and social metrics
● Online advertising
● “Marketing Metrics” (Main Ref.), Chapter 10.
● “A Comparison of Approaches to Advertising
Measurement”, White paper by Kellogg/Facebook,
2016.
11
17/Nov
Teams and organizations ● Organizational issues for marketing
analytics
● How to build an effective analytics team
● “Mobilizing your C-suite for big-data analytics”,
McKinsey Quarterly, Nov 2013.
● “How Smart, Connected Products Are
Transforming Companies”, HBR, Oct 2015.
12
24/Nov
Project presentation ● Team project final presentation
13
1/Dec
Final wrap-up ● Future of marketing analytics
● Big data, AI, IoT
● Impact of automation
● “Beyond Automation”, HBR, June 2015.
● “The coming era of ‘on-demand’ marketing”,
McKinsey Quarterly, Apr 2013.

8. All rights reserved © Masao Kakihara
Readings
8
● Main reference book
○ “Marketing Metrics: The Manager's Guide to
Measuring Marketing Performance” (3rd Edition),
by Paul Farris, Neil Bendle, Phillip E. Pfeifer, David
J. Reibstein. Pearson FT Press, 2015.
● Supplementary materials
○ “Data-Driven Marketing: The 15 Metrics Everyone in Marketing
Should Know”, by Mark Jeffery. Wiley, 2010.
○ “Marketing Analytics: Data-Driven Techniques with Microsoft
Excel”, by Wayne L. Winston. Wiley, 2014.
○ “Business and Competitive Analysis: Effective Application of
New and Classic Methods” (2nd Edition), by Craig S. Fleisher,
Babette E. Bensoussan, Pearson FT Press, 2015.
● Various online articles and papers provided by
students on Online Shared Note.

9. All rights reserved © Masao Kakihara
Evaluation
9
1. In-class contribution : 20%
○ Contributions to class discussion to be assessed in both quantity
and quality
2. Material Sharing : 20%
○ Sharing relevant and useful materials for each course topic via
Online Discussion Forum on eLearn
3. Individual assignments (3 Assignments) : 10% x 3 = 30%
○ Analytical case studies to be provided, solved in 2 weeks and
submitted
4. Final team project : 30%
○ A team of 5-6 members to be formed, solving a marketing analytics
problem with real data sets
○ One team report (doc) and one class presentation (10-15 mins per
team) to be done on Session 12 (24th Nov)

10. All rights reserved © Masao Kakihara
Misc. Matters
10
● No training for stat techniques and tools to be offered
○ Preferred courses prior to this course
■ B.2: Data Analytics Lab
■ B.3: Customer Analytics and Applications
○ Assignments will not be assessed solely on model/analysis
sophistication, but more on practical implications and insights
● Course material folder (eLearn / Google Drive)
○ All course materials to be uploaded before each class

11. All rights reserved © Masao Kakihara
A Material for Today’s Discussion
11
Access to this article and have a quick read.
“Beyond the Hype: The Hard Work Behind
Analytics Success”, MIT SMR, Mar 2016,

12. All rights reserved © Masao Kakihara
‘Definition’ Matters
12
● Data, Information, Knowledge
○ What’s the difference?

13. All rights reserved © Masao Kakihara
‘Definition’ Matters (cont’d)
13
● Marketing
○ “The activity, set of institutions, and processes for creating,
communicating, delivering, and exchanging offerings that have
value for customers, clients, partners, and society at large”
(American Marketing Association, 2013)
○ Agree? Make sense?

14. All rights reserved © Masao Kakihara
Increasing Interest in ‘How to Deal with Massive Data’
14

15. All rights reserved © Masao Kakihara
‘Big Data” Hype?
15MIT SMR Research Report “Beyond the Hype: The Hard Work Behind Analytics Success”, March 2016
http://sloanreview.mit.edu/projects/the-hard-work-behind-data-analytics-strategy/

16. All rights reserved © Masao Kakihara
Struggling with Translating Data into Insights
16MIT SMR Research Report “Beyond the Hype: The Hard Work Behind Analytics Success”, March 2016
http://sloanreview.mit.edu/projects/the-hard-work-behind-data-analytics-strategy/

17. All rights reserved © Masao Kakihara
Capturing and aggregating data is still a big issue
17

18. All rights reserved © Masao Kakihara
Next Session…
18
2
1/Sep
Ecosystem of Marketing
Metrics
● Systems and structures of marketing
metrics
● Key concepts and frameworks for
marketing decision making process
“Marketing Metrics” (Main Ref.), Chapter 1.
● An overview of marketing metrics
● Key concepts and frameworks for marketing
○ Consumer journey
○ Marketing funnels
○ Plan, Do, See
○ Segmentation, Targeting, Positioning etc.