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Marketing Models

Code: 102351
Credits: 6
2026/2027
Degree programme Type Course
Business Administration and Management OP 4

Contact lecturer

Name :
Jordi Lopez Sintas
Email :
jordi.lopez@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

This course is elective for both students of business administration as well as students of economics, although it is  compulsory for estudents taking the specialization in marketing. dout to this dual profile of students I adopt a perspective of decision making based on marketing models applied to solving marketing problems. 

Students of business administration  are advised of taking a course of industrial economics and market research.

Objectives

Context

This is an elective course of 6 ECTS for students of Business Administration and Management, Economics, and Marketing Studies taught at UAB and is offered during the second semester of the fourth year. Its purpose is to advance the study of applied marketing models: strategic marketing analysis and marketing decision using models, data, and computer support. This is an advanced marketing strategy course.

Specifically, it aims to develop the skills needed to apply marketing models in a wide range of marketing decisions: market segmentation, product choice, positioning, pricing strategies, product policy, and analysis of the interactions between the elements of the marketing mix.

The course presents business management models, analyzes, implements, and evaluates seral models developed in the area of marketing knowledge for making strategic business decisions. Furthermore,  the course presents a set of decision tools and the necessary knowledge to design an effective marketing program.

Objectives

 At the end of the course, the student should be able to:

I Evaluate the role of marketing, particularly marketing strategy on the competitiveness of the company.

II. Understand the various sources of information available for making marketing decisions and their possible usefulness.

III. Use sophisticated tools (software models) for the resolution of marketing problems.

IV. Know the needed  information for using marketing models and data process tools  for  business decision making.

V. Define a business problem, evaluate the different solutions business models suggest, and propose a solution or action plan.

VI. Explain the reality of Spanish companies, their most important business problems, strategic as well as tactics, the usual solutions and its logic.

Learning outcomes

  1. Capacity to adapt to changing environments.
  2. Capacity to continue future learning independently, acquiring further knowledge and exploring new areas of knowledge.
  3. Select and generate the information needed for each problem, analyse it and make decisions based on this information.
  4. Make decisions in situations of uncertainty and show an enterprising and innovative spirit.
  5. A capacity of oral and written communication in Catalan, Spanish and English, which allows them to summarise and present the work conducted both orally and in writing.
  6. Organise work, in terms of good time management and organisation and planning.
  7. Demonstrate initiative and work independently when required.
  8. Work as part of a team and be able to argue own proposals and validate or refuse the arguments of others in a reasonable manner.
  9. Assess the main marketing concepts and tools.
  10. Assess the importance of long-term commercial relationships with clients (relationship marketing).
  11. Identify the differences in the marketing applied to different economic sectors or types of organisations.
  12. Understand the importance of strategic marketing as a source of competitive advantages for the organisation.
  13. Perform an analysis of the market and of competitive structures, and determine a strategic diagnosis for the company.
  14. Formulate and design different strategies of growth and differentiation.
  15. Establish strategies of innovation and development of new products.
  16. Recognise the different directions a company can adopt.
  17. Identify the different elements making up a marketing plan, and draw up a marketing plan.
  18. Translate strategic goals into specific marketing-mix programmes.
  19. Apply the concepts of strategic marketing to achieve market-oriented organisation.
  20. Evaluate the major concepts and tools of communication (offline and online).
  21. Identify the different elements that make up a communication plan and develop a communication plan.
  22. Translating strategic objectives into concrete programs of communication.
  23. Students can apply the knowledge to their own work or vocation in a professional manner and have the powers generally demonstrated by preparing and defending arguments and solving problems within their area of study.

Contents



1. Data-driven models and the evaluation of marketing strategies


1.1 Models in business decision-making


1.2 Technologies and data in business decision making


2. Analysis of consumer behavior: Models to segment markets


2.1 Models based on the benefits that consumers are looking for


2.2 Data pre-processing


2.3 Statistical and numerical models: clustering


2.4 Analysis of cases: Hatko (industrial), Hopsital KFH (consumption), PDA, FLIP (consumption)


3. Analysis of consumer behavior: Targeting


  3.1 Simple choice models: RFM, statistical models


  3.2 Models of choice between multiple brands


  3.3 Analysis of cases: ABB (industrial marketing), Bookbinders (consumer marketing)


4. Competitor Analysis: Models to Evaluate the Positioning of Brands in the Minds of Consumers


  4.1 Models for constructing perception maps


  4.2 Models for building preference maps


  4.3 Models for building joint maps


4.4 Analysis of cases: Infinity G20, PDA-positioning, Case Heineken Spain, Cas Hospital KFH, Can de Bunny Hop? Pacific Brands Case, Case ISBM


5. Business strategies with heterogeneous products: Design of new products and prediction of market shares


5.1 Models for Evaluating a product idea and choosing product features


5.2 Models for Designing the product line


5.3 Analysis of cases: Forte Hotel case (dis, sim), Kirin case (seg, pos, dis, sim), Dürr environmental case (seg, pos), Pharmaceutica Beta case, car design case, case Apple vs. Samsung


6. Commercial strategies with homogeneous and heterogeneous products: product and price decisions


6.1 Models for deciding how much and when to reduce product prices (rebates)


6.2 Models for deciding the prices of complementary and substitute products


6.3 Models for Deciding the price of product packages


6.4Models for setting the price according to the value of the product (industrial markings)


6.4 Analysis of cases: PDA-maximization, inkjet printers, sales management case, Abcor2000 Value in use prices case.



Learning activities and methodology

Title Hours ECTS Learning outcomes
Individual tutoring and small group seminars 9 0.36 3, 4, 5, 6, 7, 8
Bibliographic research 15 0.6 3, 4, 6, 7
Working with large groups of students: learning based on the exposition of the problem and the theoretical approaches, the cooperative work and the resolution of cases with databases 32.5 1.3 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
Group work: Development of professional skills to work in a team of professionals 26 1.04 2, 3, 4, 6, 8
Solving cases and exercises 17 0.68 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
Readings and personal study 40.5 1.62 5, 7

The teaching methodology and evaluation proposed in the guide may undergo some modification depending on the restrictions on attendance imposed by the health authorities.

In the classroom, we will work as follows:

Lectures. Learning activities will be introduced throughout the class to allow greater participation and motivation of students. Specifically de use of mini-cases or newspaper clippings that illustrate the problems being studied in class to fix the lecture on the experience of the student.

The case as a teaching strategy. The cases have the following characteristics: (1) being authentic, from a real experience, (2) unfinished, i.e. recounting a problematic situation, (3) analysis and resolution shall require the selection of specific information related to the lecture, and (4) must be complete, contain all the necessary information, despite having some additional assumptions to be made to proceed with the resolution. Cases are accompanied by databases that students must analyze. Sessions will be held in the computer lab to learn how to use the necessary models to solve cases.

Analysis by mini-cases. We refer to those cases that are short, two to three pages at most, and often come directly from press clippings right to be used in lectures to illustrate, apply, analyze and evaluate the explanatory power of the theories studied.

Cooperative  activities. The purpose of these activities is to help students prepare their conceptual knowledge and develop their analysis of cases.

Report writing strategy. In some cases, students develop and deliver a professional report after data analysis. Every so often, it will be a written report, and in others, it will be a professional presentation.

Personal work: Resolution of cases with the support of computer software, of which at least three will be part of the continuous assessment.

The virtual environment of learning (the Internet): On the \"campus virtual\", students will find various teaching and learning resources: readings, cases, internet resources, etc. Check at least once a week for the latest news.

Long essay on a case to be handed by the end of the Course (optional): This assignment will be conducted throughout the semester, and it will be discussed during the last two weeks of the course

Personal Tutoring: During the tutoring schedule as well as in the virtual environment of learning, we will answer student questions about the content of the course as well as personal or professional issues related to the subject.

Assistant digital: We have prepared a digital chatbot based on generative LLM.

Use of AI

Permitted use: \"In this subject, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of the work, provided that the final result reflects a significant contribution of the student in the analysis and personal reflection. The student will have to clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how they have influenced the process and the final result of the activity. The lack of transparency in the use of AI will be considered a lack of academic honesty and may lead to a penalty in the grade of the activity, or greater penalties in cases of seriousness.\"

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Assessment of at least three formative assessment tasks related to cas discussion 40% 4 0.16 1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
Regularity in the preparation of cases, formative tests and in the attendance and participation in class discussions. 20% 2 0.08 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 23
Assessment of at least three formative assessment tasks conducted in class 40% 4 0.16 3, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22

(Indicate the type of evidence that student learning must be delivered, its weight in the final grade, the evaluation criteria, the definition of "absent or not shown", the review procedure of testing, treatment of any particular cases, etc.).

The teaching methodology and evaluation proposed in the guide may be modified depending on attendance restrictions imposed by the health authorities.

This subject/module does not offer the option for a comprehensive evaluation

Formative assessment:

1) Evaluation of at least 80% of the assigned cases or short assignments (individual or in groups of 2 people maximum). This must be submitted by the established deadline; after that, no work will be accepted. The average of all the works presented will be calculated (40%)

2) Regularity in the preparation of cases, training tests and in attendance and participation in class discussion (20%)

3) Delivery and evaluation of at least 80% of the formative evaluations (individual) throughout the course (40%).

4) Optional submission: Preparation of a work based on the challenge that must be submitted within the set deadline. (up to 2 additional points)

The maximum score will be 10.

Student not graded:

1) will be deemed not presented to the person who has NOT submitted at least 80% of all formative assessment tasks.

Review and evaluation of the work submitted:

1) Short papers: assessed work will be returned one week after it is delivered or completed. During the following week, students will review the work graded.

2) Assessment of short formative assessment tasks conducted in class. After delivering it, the student will have a week to discuss it with the lecturer.

3) Long assignment: After presenting the assignment, the student will have a week to discuss it with the lecturer.

Calendar of evaluation activities

The dates of the different evaluation tests (midterm exams, classroom exercises, assignment submission, ...) will be announced well in advance during the semester.

The date of the subject's final exam is listed in the Faculty's exam calendar. \"The scheduling of the evaluation tests may not be modified unless there is an exceptional and duly justified reason why an evaluation act cannot be carried out.

In this case, the people responsible for the degrees, after consulting the teaching staff and the students affected, will propose a new program within the corresponding academic period. Section 1 of Article 115. Calendar of assessment activities (UAB Academic Regulations)

Students of the Faculty of Economics and Business who, in accordance with the previous paragraph, need to change an evaluation date must submit the request by filling in the document. Request for rescheduling of the test: https://eformularis.uab.cat/group/deganat_feie/reprogramacio-proves

Reassessment:

All students are required to perform the evaluation activities. If the student's grade is 5 or higher, the student passes the course, and it is not subject to further evaluation. If the student's grade is below 3.5, the student will not receive a grade and will have to repeat the course the following year. Students who have obtained a grade of 3.5 or higher but less than 5 can take a second-chance exam. The lecturers will decide on the type of the second-chance exam. When the second exam grade exceeds 5, the final grade will be a PASS with a maximum numerical grade of 5. When the second exam grade is less than 5, the final grade will be a FAIL, with a numerical grade equal to the course grade (not the second-chance exam grade).

A student who does not perform any evaluative task is considered “no evaluable”, therefore, a student who performs a continuous assessment component can no longer be qualified as "not evaluable".

Students of the Faculty of Economics and Business who, in accordance with the preceding paragraph, need to change the date of evaluation must submit the petition by filling in the document request reprogramming:

https://eformularis.uab.cat/group/deganat_feie/reprogramacio-proves


Review the procedure for the qualifications

Coinciding with the final exam, I’ll announce the day and the medium by which the final grades will be published. Besides, I will inform you about the procedure, place, date, and time of the revision of the assessment in accordance with the regulations of the university.


Irregularities in the assessment acts

Notwithstanding other disciplinary measures that are deemed opportune, and in accordance with the current academic regulations, \"If the student performs any irregularity that may lead to a significant variation of the qualification of an act of assessment, the student will be graded with 0 this act of evaluation, regardless of the disciplinary process that can be instructed. If several irregularities occur in the assessment of the same subject, the final grade for the course will be 0 \". (Apartat 10 de l'Article 116. Resultats de l'avaluació. (Normativa Acadèmica UAB).


The completion of assessment activities is subject to the provisions set out in this course guide and in the "Policy of the School of Economics and Business on the Detection of Irregularities during Assessment Activities", which regulates the conditions under which assessment tasks are conducted and the procedures applicable in cases where indications of irregularities are detected. Students are encouraged to consult the policy.


Use of AI

Permitted use: "In this subject, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of the work, provided that the final result reflects a significant contribution of the student in the analysis and personal reflection. The student will have to clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how they have influenced the process and the final result of the activity. The lack of transparency in the use of AI will be considered a lack of academic honesty and may lead to a penalty in the grade of the activity, or greater penalties in cases of seriousness."

Bibliography

Compulsory readings:

López, Jordi (2026) Marketing avanzado con un enfoque de ingeniería comercial. Disponible en https://mgc-uab.netlify.app.

Lilien, Gary L, & Arvind Rangaswamy (2004), Marketing Engineering. Trafford Publishing. Revised Second Edition.

Lilien, Gary L, Arvind Rangaswamy, & Arnaud De Bruyn. Principles of Marketing Engineering and Analytics. State College, PA: DecisionPro, Inc., 2017.

Bruzzone, G. B. B. y F. R. (2020). RStudio para Estadística Descriptiva en Ciencias Sociales. Retrieved 27 June 2021, from https://bookdown.org/gboccardo/manual-ED-UCH/

Franssens, Samuel (2020). R for marketing students. Retrieved 27 June 2021, from https://bookdown.org/content/1340/

Chapman, C. N., & Feit, E. M. (2015). R for Marketing Research and Analytics (2015 edition). Springer. Digital edition

Harris, Jenine K. (2021). Statistics With R. SAGE Publications. https://uk.sagepub.com/en-gb/eur/statistics-with-r/book253567

Supplementary readings:

Andreas Herrmann, Frank Huber, y Christine Braunstein (2000) Market-Driven Product and Service Design: Bridging the Gap Between Customer Needs, Quality Management, and Customer Satisfaction. International Journal of Production Economics, 66:77-96.

ANTON, J. (1996), Customer Relationship Management, Englewood Cliffs, New Jersey: Prentice-Hall, Inc.

Bak A. and Bartlomowicz T. (2012), Conjoint analysis method and its implementation in a conjoint R package, In: Pociecha J., Decker R. (Eds.), Data analysis methods and its applications, C.H. Beck, p. 239-248.

Coghlan, Avril (2013) A Little Book of R for Multivariate Analysis-Release 0.1 (https://buildmedia.readthedocs.org/media/pdf/little-book-of-r-for-multivariate-analysis/latest/little-book-of-r-for-multivariate-analysis.pdf)

DOLAN, R. J. & H. SIMON (1996), Power Pricing, New York: Free Press.

DOLAN, R. K. (1993), Managing the New Product Development Process, Reading, Mass.: Addison- Wesley.

Ehret, M., Kashyap, V., & Wirtz, J. (2013). Business models: Impact on business markets and opportunities for marketing research. Industrial Marketing Management, 42(5), 649-655. doi:10.1016/j.indmarman.2013.06.003

Gensh, D. H. (1984) Targeting the Switchable Industrial Customer.Marketing Science, 3(1), 41-54.

Green, Paul E., Abba M. Krieger y J. Douglas Carrol (1987) Conjoint Analysis and Multidimensional Scaling: A Complementary Approach.Journal of Advertising Research, October/November, 21-27.

Green, Paul E., and Abba M. Krieger. 1988. “Choice Rules and Sensitivity Analysis in Conjoint Simulators.” Journal of the Academy of Marketing Science 16 (1): 114–27. doi:10.1177/009207038801600110.

Green, Paul E. Y Abba M. Krieger (1992) An Application of a Product Positioning Model to Pharmaceutical Products.Marketing Science, 11(2), 117-132.

Guiltinan, J. P. (1987). The Price Bundling of Services: A Normative Framework. Journal of Marketing, 51(2), 74. doi:10.2307/1251130

JAGPAL, S. (1999) Marketing Strategy and Uncertainty. New York: Oxford University Press.

Wedel, M., & Kamakura, W. A. (2000). Market segmentation: conceptual and methodological foundations. Springer.

Lattin, J. M., Carroll, J. D., Green, P. E., & Green, P. E. (2003). Analyzing multivariate data. Pacific Grove, CA: Thomson Brooks/Cole.

LILIEN, G., Ph. KOTLER & K. S. MOORTHY (1992), Marketing Models, Englewood Cliffs, NJ: Prentice-Hall, Inc.

Moorthy, K. S. (1984). Market Segmentation, Self-Selection, and Product Line Design. Marketing Science, 3(4), 288–307.

Palocsay,Susan W., Ina S. Markham, and Steven E. Markham. (2010) “Utilizing and Teaching Data Tools in Excel for Exploratory Analysis.” Journal of Business Research 63, no. 2 (February 2010): 191–206.doi:10.1016/j.jbusres.2009.03.008.

Putler, D. S. (2012). Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R (Chapman & Hall/CRC The R Series).

SIMON, H. (1989), Price Management, Amsterdam (The Netherlands): Elsevier Science Publishers.

Stremersch, S., & Tellis, G. J. (2002). Strategic Bundling of Products and Prices: A New Synthesis for Marketing. Journal of Marketing, 66(January), 55–72.

Tyran, Craig K. (2010) “Designing the Spreadsheet-Based Decision Support Systems Course: An Application of Bloom’s Taxonomy.” Journal of Business Research 63, no. 2 (February 2010): 207–216. doi:10.1016/j.jbusres.2009.03.009.

URBAN, G. L. & J. R. HAUSER (1993), Design and Marketing of New Products, Englewood Cliffs, NJ: Prentice-Hall.

Software

For the application of the models and analysis of the cases the following software will be used:

R Language and Evironment for Data Analysis (r-project.org)

The Rstudio graphical interface (www.rstudio.com), or posit.cloud

The online program rstudio.cloud (rstudio.cloud)

Explratory.io

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 1 Catalan second semester morning-mixed
(PLAB) Practical laboratories 1 Catalan second semester morning-mixed