MAN 5146 Customer and Market Analytics

MAN 5146 Customer and Market Analytics

Course code: 
MAN 5146
Course coordinator: 
Matilda Dorotic
Course name in Norwegian: 
Customer and Market Analytics
Product category: 
Executive Master of Management
2023 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Are you using the data your business has to your best advantage? This course teaches you (without lots of math and statistics) how to understand and employ analytics to improve your business results.

Should you blindly trust analytic reports served to you by analysts or statistical software? Be careful if you do!

In the fast-paced market conditions, where competition is fierce, rate of technological 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 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 data to make effective business decisions. Even for managers who have IT specialists and computer scientists in their team, the understanding of how to avoid “garbage in-garbage out” situations is essential. Practice have shown that business/strategic decisions cannot be left to technologies alone because competitive advantage is still in the decisions that managers make about their investments in customers and markets. 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.

 In this course you learn how to increase the effectiveness of strategic decisions. We use an “Explain-Show-Do-Practice” approach to teaching that encompasses explanations in the lectures followed by a combination of class discussion, case study analysis and practical hands-on exercises in Excel (cloud-based solution). We do not go deeply into the statistics and mathematics behind the methods used in the academic models behind the tools. In this course, we always start from an identification of a strategic business problem (e.g. “Should I invest in option A or option B”). We will then summarize what we know from years of practice and research on what works and what does not. We follow up these insights with an understanding of what data would I need to solve the problem and intuitively how a model/analytics work to solve this issue. We teach you hands-on, based on actual case studies and their datasets how could one analyze the data to get insights. We use a cloud-based solution Enginius (that can use Excel spreadsheets) to make this course highly relevant and applicable to manager’s actual decision-making. Strategically, we then focus our attention on how to evaluate the output that you would get from the software or analytics experts in the firm to make more effective business decisions.


  • To those reading analytic reports:
  1. Executives in leadership positions who want to understand how analytics could be used to improve business performance rather than blindly trust into outputs of analysts
  2. For business development professionals and management consultants who need to understand how analytics can create value for their clients
  • To those who do analytics:
  1. for product, brand and marketing managers who need to understand how to better leverage data and run analytic models in intuitive, simple way
  2. professionals who already have background in analytics or computer science, but whose roles and projects are becoming increasingly strategic, so they need to develop further strategic skills to bridge the gap between analytics and business strategy.

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 strategy. 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. Basic understanding of statistics (at an undergraduate business studies level) is beneficial for following the course, although some basic concepts that we need will be explained in class and in online materials and exercises. Basic knowledge and use of Microsoft Excel program is preferable, because most exercises are linked to a managerially oriented cloud-solution Enginius. 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 decision-making based on data.
  • Obtain an in-depth understanding of the benefits and costs of alternative strategic 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 return on firm's investment in customers and products, identifying opportunities in market
  • 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 and potential business problems that could be addressed:


What data you put in

What you get

Analytical Tool


  • Better understand the market I serve and my customers.
  • Identify different segments in a market.
  • Choose attractive customer segments for targeting its marketing programs.
  • Profile my customers


  • Customers' importance ratings for each measure of value for offerings in a product class
  • Customer descriptors (demographic or firmographic variables)
  • Number, size, and profile of needs-based market segments
  • Identification of factors that differentiate segments, both in terms of needs and descriptors
  • Classification tool to allocate any potential customer to a segment based on customer descriptors.

Segmentation, & Targeting Analysis


Cluster analysis

Discriminant analysis

Units 2-3-4

  • Understand how customers view  product(s) relative to competitive products
  • Customers' rating of focal brand and key competitors on dimensions of merit
  • Individual customer preference ratings of all competitors
  • Perceptual map, showing which brands are closest to one another.
  • Attributes that differentiate brands
  • Locations of individual customer preferences
  • Projected market share associated with current and new positions on the map

Positioning and perceptual mapping

Units 2-3-4

  • Measure, analyze, and predict customers' responses to new products and to new features of existing products (e.g. which price to charge)
  • Design new products that maximize customer utility.
  • Forecast sales/market share of alternative product bundles.
  • Identify market segments for which a given product concept has high value.
  • Identify the "best" product concept for a target segment.
  • Customer ratings of a set of real or potential product offerings, defined by their key attributes
  • Market share of existing products
  • New product profiles


  • Customers' preferences and responses to new products
  • Relative worth of product attributes
  • Optimal product design
  • Market share estimates for alternative products
  • Customers' willingness to pay for product attributes
  • Potential incremental revenue from new offerings/features

Conjoint analysis


Units 5-6-7-8

  • Analyze and explain the choices individual customers make in the market.
  • Understand which elements drive customer decision to buy your product or not?
  • Estimate customer’s willingness to buy at different price points (and to interpolate between these price points).
  • Customer's choice data for alternative offerings
  • Customer ratings of alternative offerings on their key attributes
  • Buying intention for a product at a number of different price points
  • Purchase probabilities, predicted and observed choices of customers
  • Factors influencing customer choice, including brand as well as performance attributes
  • Aggregate demand level estimate for any price level

Customer choice model


Units 5-6-7-8

  • Calculate customer's value to the organization over the entire history of the relationship
  • Understand who are my most valuable customers
  • Observed churn rates
  • Customer acquisition cost
  • Number of customers/segments
  • Gross margins by segment
  • Customer transition probabilities across segments
  • Value of current customer base
  • Time required to recoup customer investments
  • ROI on customer/segment investments
  • Size and profitability of customer segments over time; sensitivity to marketing investment plan

Customer Lifetime Value Analysis

Units 9-10-11

  • Optimize resource sizing and resource allocations across segments, products, channels, etc.
  • How much should we spend in total during a given planning horizon?
  • How should that spending get allocated to each product or market segment? To each marketing mix element? How much of our budget should be spent on advertising and other forms of impersonal marketing communications? On sales promotions? On the sales force?
  • How should budgets given to an individual (e.g., salesperson, manager of department) be allocated? To customers? To geographies? To sub-elements of the marketing communications mix? Over time?


  • Analyzing what customers say and feel about your brands on social media
  • Number of market segments, products, geographies or other basis for resource allocation
  • Current level of spending and associated sales
  • Profit margins
  • Response functions - how sales would change if spending were higher or lower than current spending
  • Constraints (minimum / maximum) for each basis unit


  • Optimal level of total spending (across media spending)
  • Optimal allocation of spending across units
  • Profit associated with optimal plan versus current plan
  • Incremental gain or loss associated with changes from current or optimal plan
  • Across media spending (different channels)


Resource allocation analysis











Sentiment analysis and ROI on social media

Units 9-10-11

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 exercises 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 a set of individual home assignments 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 home assignment accounts for 40% of the total grade. It is a collection of case study solutions on which students work throughout 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. Both evaluations must be passed to obtain a certificate for the course. 

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.

The course will use the concepts and marketing analytics software from Enginius (a widely used executive instructional material developed at Penn State University, US). Student's individual licence for the software as well as all the books, case studies and materials are included in the price of the course (see the literature and course requirements sections).

In all BI Executive courses and programs, there is a mutual requirement  
for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.

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

Participating in the course will require access to a pc/laptop.

PLEASE NOTE THAT THE LICENCE FOR ACCESS TO ENGINIUS IS REQUIRED FOR THIS COURSE! The individual licenses for the software is included in the course price advertised on BI's website. The approximate 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). All cases, datasets and online materials are included in the total price of the course. All compulsory readings and the books are included in the online course materials.


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 and at least four years of work experience. For applicants who have already completed a master’s degree, three years of work experience are required.


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this. 

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 category: 
Form of assessment: 
Written submission
1 Semester(s)
The individual home assignment accounts for 40% of the total grade and consists of two parts which are submitted at the same time.

Part I (30%): Solution to at least three (3) case-studies throughout the course: in online exercises throughout the course students are asked to solve case studies and provide 1-2 Power point slides with their proposed solution. The student should select 3 of his/her solutions uploaded to Insendi and include them in a PDF document.

Part II: Self-reflection on learning outcomes (10%): In this part of the individual home assignment, the student will analyze his/her learning from the online activities and reflect on how he/she has applied this learning in practice or how the learning could be applied.

The student will submit both parts in one PDF document within the exam deadline given.
Exam code: 
MAN 51461
Grading scale: 
Examination when next scheduled course
Exam category: 
Form of assessment: 
Written submission
Group/Individual (1 - 3)
1 Semester(s)
Term paper, counting 60% of the total grade. This final project applies at least one method/tool that the students learned in the course to solve actual strategic problem of a firm. Typically, students use data (either collected for this purpose or existing firm data) to solve the identified problem. The students work in groups and they can share one dataset per group (no need for each student to have the separate dataset). the data should be in the anonymized format.
Exam code: 
MAN 51462
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
Student workload
48 Hour(s)
On-campus teaching
Digital resources
  • Interactive video
  • Interactive websites
27 Hour(s)
Webinars and online teaching
Student's own work with learning resources
325 Hour(s)
Student's self-study of online/offline materials, preparation for the class and term paper + in-home paper preparation
Sum workload: 

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.