"Marketing Analytics and Applications": Course Introduction
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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.
![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 + ?]](https://image.slidesharecdn.com/mitb2016t1courseintro-161125003109/85/Marketing-Analytics-and-Applications-Course-Introduction-3-320.jpg)
"Marketing Analytics and Applications": Course Introduction
- 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.