MAN 5146 Applied Marketing Analytics

MAN 5146 Applied Marketing Analytics

Course code: 
MAN 5146
Department: 
Marketing
Credits: 
15
Course coordinator: 
Matilda Dorotic
Product category: 
Executive
Portfolio: 
Master of Management
Semester: 
2019 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

We teach managers how to find and extract the important marketing data from the noise of the abundance of data surrounding you! We teach you to create and measure metrics that truly allow you to explore what is working in marketing in your firm and what isn’t! And we teach you how to employ analytics in practice in a simple way, using just Excel!

In the fast-paced market conditions, where competition is fierce, rate of technology-change wild and consumers empowered and unpredictable, it has become more crucial than ever to understand the trade-offs between elements that are driving business performance. While data availability is overwhelming, managers are struggling in analyzing the increasing amount of information they have. Never before did managers have this much information on customers, partners and competition at disposal to make informed, smarter decisions; yet they are more than ever criticized about the lack of actionable insights that derive from it. McKinsey Consulting predicted that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. Even for managers who have IT specialists and computer scientists in their team, the understanding of which data inputs are required and what outputs could and should be expected makes the essence of decision-making. The quest to find sustainable advantages to satisfy customers better than competition requires an understanding of how different aspects of marketing investment can be tight together, how to evaluate the current potential and future contribution of each customer and how to reap the advantage from customer relationships and social media investments.

This course aims to teach basic skills in marketing decision-making based on a systematic analytical approach to harnessing data that help managers to increase the effectiveness of marketing decisions. The course is tailored towards managers who are not trained as computer engineers but rather as business professionals who have to use data to improve marketing decision-making in their everyday tasks (e.g. deciding which customer segments should be targeted, how to evaluate the potential costs versus return-on-investing in different marketing activities e.g. from online or offline campaigns, or testing how to develop a new product and improve the existing one). We use an “Explain-Show-Do-Practice” approach to teaching that encompasses explanations in the lectures followed up by a combination of class discussion, case study analysis and practical hands-on exercises with practice datasets in Excel. We do not go deeply into the statistics and mathematics behind the methods used in the academic models behind the tools, but rather provide you with an understanding of what the model could be used for, intuitively how it works and which data do you need to have as an input and how to evaluate the output that you would get from the software. We use an add-on module in Excel to make this course highly relevant and applicable to manager’s actual decision-making.

INTENDED AUDIENCE:

This executive course is designed for:

  • Executives who are in traditional leadership/management positions - of business units, products and functions - who need to understand how to better leverage data  to improve business performance.
  • Executives who already have expertise in analytics, but whose roles and projects are becoming increasingly strategic so they need to develop further strategic skills.

    Our classroom experience shows it is beneficial to team up the two groups of executives above to teach them how to bridge the gap between analytics and startegy. Therefore, the course can both to managers without extensive marketing background as well as to those who work on marketing issues on a daily base. The highest impact of learning is achieved when concepts are discussed and practiced across the whole company in interdepartmental teams consisting of managers with diverse background: analytics, finance, accounting, operations and marketing, and the class would benefit from interactions and contributions from different backgrounds.

    This course would be suited for students who have basic skills and knowledge in business administration. No advanced understanding of mathematics or econometrics is necessary for this course, but the basic understanding of statistics (at an undergraduate business studies level) is beneficial for following the course. Basic knowledge and use of Microsoft Excel program is preferable, because most excercises are linked to Excel. Advanced data science and computer programing skills are not needed for this course (albeit we provide insights for more advanced students in supplemental materials which are linked to programming in R and machine learning algorithms). 

Learning outcomes - Knowledge

Participants in this course will:

  • Obtain advanced knowledge on how to access business opportunities using systematic analytical approach to marketing decision-making based on data.
  • Obtain an in-depth understanding of the benefits and costs of alternative marketing options and actions by understanding their trade-offs.
  • Enable identification of data sources needed to effectively solve some common marketing problems and calibration of the opportunity costs associated with each options.
  • Be able to use the knowledge to understand how to optimize resource allocation across different marketing instruments, customer segments, products and channels.
  • Be able to understand and analyze the value that market segments offer and seek from the firm (i.e. value-of-the-firm’s offer versus value-to-the-firm)
  • Develop advanced knowledge about key requirements and insights necessary to design successful new product(s) by measuring, analyzing, and predicting customers' responses to new products and/or to new features of existing products
  • Develop in-depth understanding why some products perform better than others by understanding the choices that drive individual customer’s decisions in the market.
  • Understand and demonstrate what is required to measure and evaluate the potential versus actual performance of marketing instruments like customer relationship management initiatives or digital marketing and social media campaigns.
Learning outcomes - Skills

This course aims to develop participant’s strategic thinking and analytical skills through a hands-on learn-by-doing approach which should allow participants to obtain the following skills upon the completion of this course:

  • An ability to execute basic analytical models in marketing based on a systematic approach to harness data and knowledge in order to increase the effectiveness of marketing decisions.
  • Demonstrate an ability to segment markets and identify target segments based on the available primary and secondary data
  • Analyze customer preferences for new products or changes in existing products and use those preferences for developing new products and forecasting sales of the new product
  • Critically evaluate the impact of different drivers (marketing and other investments) on firm performance and customer choice of brands
  • Analyze and calculate value/worth of different customer segments for the long-term profitability using Customer Lifetime Value (CLV) metric, for online and/or offline customer segments
  • Utilize firm data to evaluate how customer purchases and loyalty are affected with mobile- and social media investments. For example, participants will learn how to analyze data to evaluate how customer adoption of digital channels, mobile apps or social media affects customers’ purchases.
  • The participants will learn to critically evaluate the conversion rate from online channels to actual sales, how effective online advertising versus TV advertising is, how much to invest in search engine bidding or how to link social media metrics to final sales improvements?
General Competence

Through the teaching approach employed in this course, the participants will be able to reflect upon:

  • the business and marketing phenomena that are seen as a current “hype” to distinguish between the popular “buzz” and the actual evidence
  • the existing data environment and a need for sustainable, ethical evaluation of the opportunities
    the culture of collaborative thinking and teamwork, since the tasks and discussions in the course are encouraging team-working
Course content

Topics:

Day 1:

  • Introduction to the course and background for software use
  • Segmentation (classification of customers), targeting and positioning (perceptual-maps creation) tools (Cluster analysis, Latent-classes, Perceptional Mapping) – explanations, hands-on exercise in Excel

    Day 2:

  • Designing and developing customer-centric new products and services and forecasting the sales of new products/services (Conjoint analysis, Diffusion models) - explanations, hands-on exercise in Excel

    Day 3:

  • Understanding individual choices customers make and their main drivers (Choice models: single choice to buy or not, choosing between several brands) - explanations, hands-on exercise in Excel

    Day 4:

  • Understanding customer value through Customer Lifetime Value (CLV) metrics - explanations, hands-on exercise in Excel

    Day 5:

  • Measuring impact of digital marketing and social media investments on business performance -  explanations, hands-on exercise in Excel

    Day 6:

  • Optimization of resource allocation across different marketing instruments, customer segments, products and channels – explanations, hands-on exercise in Excel
Teaching and learning activities

This course is conducted through a combination of campus and online learning process. The campus module will consist of 3 sessions x 2 days. The online module will combine online content, videos and excercises to support the learning, both individually and in groups. The combination of campus and online learning process equals 75 lecturing hours over one semester. In each session, students will have an opportunity to learn hands-on different tools in Excel software with add on modules. In-class and online exercises will include working on available datasets and case study datasets. In general, students should estimate the overall workload for the course to 400 hours.

Please note that while attendance is not compulsory in all courses, it is the student's own responsibility to obtain any information provided in class that is not included on the course homepage or other course materials.

The students are evaluated through an individual home assignment and a (final) term paper. The term paper accounts for 60% of the total grade, and may be written individually or in groups of maximum three persons. The individual 72 hour home assignment accounts for 40% of the total grade. All evaluations must be passed to obtain a certificate for the course. Both the term paper and home assignment are based on the application of the concepts and tools learned in the course and provides an opportunity to implement the learned skills on your firm’s data in the final project.

The term paper is included in the Executive Master of Management degree’s independent work (cf. national regulation on requirements for master’s degree)and it is equivalent to 9 ECTS credits per course. For the Executive Master of Management degree, the independent work of degree represents the sum of term papers from all the taken courses/programmes.

Term paper supervision/guidance differ in each Executive Master of Management course. It will consist of individual and class supervision/guidance. Generally, the students may expect consulting not evaluation supervision/guidance. Supervision/guidance is offered up to 2 hours per term paper.

All evaluations must be passed to obtain a certificate for the course. Both types of evaluations are based on the application of the concepts and tools learned in the course. The final project report provides an opportunity to implement the learned skills on student's own (firm’s) data.

The course will use the concepts and marketing analytics software from Marketing Engineering for Excel v2.1 by Decision Pro, Inc. (a widely used instructional material and software adds-on for Excel users). Student's individual licence for the software is required (see the literature and course requirements sections).

 

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

Participating in the course will require access to a pc/laptop with Microsoft Excel installed.

PLEASE NOTE THAT THE LICENCE FOR THE ADD-IN TO THE WINDOWS VERSION OF MICROSOFT EXCEL IS REQUIRED FOR THIS COURSE!  This add-in to Microsoft Excel adds statistical analysis features to basic Microsoft Excel functionality. The costs of a student's academic subscription to the software and business cases for six months is approximately 45 US dollars (around 370 NOK, subject to exchange rate fluctuations).

The individual licences for the software are paid by the students!

Interested students can find more details about the software at http://www.decisionpro.biz/students. Note: Mac users can obtain the same functionalityas Windows users by using the Windows virtual machine software.

In addition, the course may use selected Harvard business cases for which participants can obtain a student access from Harvard Business Publishing (HBP). Up to four cases could be used in the course and students are required to pay for the access to the case studies from HBP (the average price per case for students is around 4.5 US dollars (around 35 NOK per case)).

Qualifications

Bachelor degree, corresponding to 180 credits from an accredited university, university college or similar educational institution
The applicant must be at least 25 years of age
At least four years of work experience. For applicants who have already completed a master’s degree, three years of work experience are required.

Required prerequisite knowledge

This course would be suited for managers who have basic skills and knowledge in business administration. No advanced understanding of mathematics or econometrics is necessary for this course, albeit basic understanding of statistics (at an undergraduate business studies level) is beneficial for following the course.

Basic knowledge and use of Microsoft Excel program is preferable.

Advanced data science and computer programing skills are not needed for this course.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
MAN 51461
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40No72 Hour(s)Individual Individual 72 hours home exam counting 40% of the total grade.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
MAN 51462
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
60No1 Semester(s)Group/Individual (1 - 3)Term paper, counting 60% of the total grade.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Individual
Duration:72 Hour(s)
Comment:Individual 72 hours home exam counting 40% of the total grade.
Exam code:MAN 51461
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:60
Invigilation:No
Grouping (size):Group/Individual (1-3)
Duration:1 Semester(s)
Comment:Term paper, counting 60% of the total grade.
Exam code: MAN 51462
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Workload activityDurationType of durationComment student effort
Teaching48Hour(s) On-campus teaching
Online teaching27Hour(s)Webinars and online teaching
Self study325Hour(s)Student's self-study of online/offline materials, preparation for the class and term paper + in-home paper preparation
Expected student effort:
Workload activity:Teaching
Duration:48 Hour(s)
Comment: On-campus teaching
Workload activity:Online teaching
Duration:27 Hour(s)
Comment:Webinars and online teaching
Workload activity:Self study
Duration:325 Hour(s)
Comment:Student's self-study of online/offline materials, preparation for the class and term paper + in-home paper preparation
Sum workload: 
400

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 15 ECTS credit corresponds to a workload of at least 400 hours.