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2022/2023

Data-Based Marketing and Behaviour-Based Marketing

Code: 43931 ECTS Credits: 10
Degree Type Year Semester
4313148 Marketing OT 0 2

Contact

Name:
María Pilar Lopez Belbeze
Email:
pilar.lopez@uab.cat

Use of Languages

Principal working language:
spanish (spa)

Teachers

María Pilar Lopez Belbeze
Josep Rialp Criado
Antoni Morell Perez
Jose Lopez Vicario

Prerequisites

There are no prerequisites

Objectives and Contextualisation

This module specializes you in an analytical environment with a business focus. You will learn to obtain quality data and the analytical techniques and tools that will allow you to make the best marketing decisions. Analyze markets, segments or consumers through innovative analysis and models that will reveal key insights to optimize marketing policies.

Block I: Data-driven Marketing

Data-driven marketing deals with the study of marketing problems from data, theories and experiments that study consumer behavior. It is an interdisciplinary line of knowledge (marketing sciences, applied microeconomics, industrial organization and statistical computing) that addresses topics such as: research on consumer choices and behavior, evaluation of business decisions based on data, development and application of small-scale and large-scale experiments, methods for using large amounts of data, computational methods for data analysis available on the Internet. The use of automatic learning techniques (Machine Learning) will be one of the pillars of this block of the module.


Block II: Consumer behavior marketing.

Behavioral marketing deals with the study of how individuals behave in relevant domains of consumption through knowledge arising from consumer neuroscience or neuromarketing. This area of marketing is interdisciplinary (psychology, neurology and behavioral economics). The use of biosensors will be one of the pillars of this block of the module. The use of neuroscientific and psychophysiological techniques will make it possible to analyze the effectiveness of marketing actions (advertising, product, packaging, brand, point of sale, web browsing,...).

Competences

  • Address business situations and/or problems that imply an ethical dilemma on the basis of critical reflection.
  • Design and implement marketing plans, applying criteria of effectiveness and efficiency.
  • Design, plan and direct marketing actions in the new scenarios posed by the information society.
  • Develop management and leadership skills.
  • Draft clear, precise reports on commercial problems.
  • Generate innovative, competitive ideas and solutions.
  • Implement emerging techniques in the field of marketing.
  • Provide innovative solutions to commercial problems.
  • Work in interdisciplinary teams.
  • Work with the data sources, methodologies and techniques of scientific research, and the IT tools of marketing.

Learning Outcomes

  1. Address business situations and/or problems that imply an ethical dilemma on the basis of critical reflection.
  2. Analyse survey data using the appropriate statistical software.
  3. Apply cutting-edge marketing planning tools.
  4. Apply the different research methods.
  5. Correctly use IT tools to analyse data.
  6. Define key strategic positions for different product-market situations.
  7. Develop management and leadership skills.
  8. Draft clear, precise reports on commercial problems.
  9. Establish processes of analysis and evaluation of actions by competing businesses.
  10. Evaluate market dynamics.
  11. Formulate realistic and innovative strategies.
  12. Generate innovative, competitive ideas and solutions.
  13. Identify and distinguish the new trends in marketing.
  14. Identify and filter the principal emerging trends and technologies.
  15. Identify the characteristics of the database in order to analyse the data.
  16. Identify the key elements of the principal marketing strategies.
  17. Make detailed proposals for planning or organisation in marketing.
  18. Use advanced models and criteria for forecasting and monitoring.
  19. Use models of strategic diagnosis.
  20. Use resources to present summaries of data and results attractively.
  21. Work in interdisciplinary teams.

Content

Block I: Data-driven Marketing (5 ECTS - A. Morell, J. L. Vicario)

This part of the module is based on the development of mini-projects in the R environment of data analysis. Each mini-project develops a theme based on data marketing, taking into account real data from digital marketing companies (Airbnb, Tripadvisor, Amazon) or social media. They will work on machine learning concepts applied to marketing and will end with an introduction to Deep Learning.

  •     Introduction to machine learning applied to marketing.
  •     Consumer behavior (abandonment prediction).
  •     Advanced Classification Mechanisms (Random Forests, Neural Networks).
  •     Recommendation systems (Basket market analysis, Association rules).
  •     Marketing decision pressure prediction models (regression versus neural network models)
  •     Social media analytics (sentiment analysis, ultrasegmentation, brand engagement).


Block II: Marketing of consumer behavior (5 ECTS - P. López, J. Rialp)

This part of the module is based on the development of a mini-research project in neuromarketing through the use of biosensors based on their experimental design, data capture and analysis.

  •     Neuroscience applied to marketing: Neuromarketing.
  •     Neuromarketing Techniques: Eye Tracking
  •     Neuromarketing techniques: GSR (galvanic skin response) and HR (heat rate)
  •     Neuromarketing research: data analysis.
  •     Marketing applications (advertising, web, social networks, price, brand ...)

Methodology

Teaching methodology

A whole set of teaching methodologies are combined:

  •      Master classes,
  •      Discussion of articles / cases in class,
  •      Internship / case classes,
  •      Preparation and presentation of mini-projects.
  •      Tutorials.
  •      Personal study.


Note: 15 minutes of a class will be reserved, within the calendar established by the center / degree, for the complementation by the students of the Surveys of evaluation of the performance of the teaching staff and of evaluation / module.

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.

Activities

Title Hours ECTS Learning Outcomes
Type: Directed      
Lectures, case discussion and presentation of short essays 75 3 1, 2, 3, 4, 10, 6, 7, 9, 13, 14, 15, 16, 11, 17, 5, 19, 18, 20
Type: Supervised      
Tutorials and follow-up of the essays to be carried out and of the cases of analysis 50 2 4, 9, 15, 17, 5, 20
Type: Autonomous      
Assigned readings, preparation of assignments and practical exercises, study and elaboration of schemes 100 4 2, 4, 8, 12, 15, 21, 5, 20

Assessment

Assessment

  • Participation in class discussions (20%)
  • Deliveries of individual or collective work (40%)
  • Individual assessment through individual examination or delivery (40%)


A. General modification assessment rules

This module is structured in different parts that are in charge of different lectures. The final grade of the module consists of the average of the notes of each subject or part that make up the module.

It is considered that the module has been approved if:

  1. the mark of each part of the module is greater than or equal to 5 (on a scale of 0 to 10) and
  2. the final grade of the module is greater than or equal to 5 (on a scale of 0 to 10)

 

If the module is not approved, the coordination of the master's degree will offer the student the possibility of re-evaluating the parts that make up the module and that have not been passed if the grade is greater than or equal to 3.5, according to the evaluation of the professors. modules and coordination. If the student passes the reassessment, the maximum mark that will be obtained in the re-assessed part will be 5. The re-assessment calendar will be made public along with the list of module marks.

The note of each part of the module

The student will have a mark of Not Assessed if he does not attend at least 80% of the face-to-face classes (a control will be kept with a signature sheet) or if he does not carry out at least 50% of the continuous assessment activities. Each professor will specify in this guide the way in which he will evaluate the students. If not specified in the guide, these evaluation standards will be delivered on the first day of class in writing.

 

 B. Calendar of evaluation activities

The dates of the evaluation activities (midterm exams, exercises in the classroom, assignments, ...) will be announced well in advance during the semester.

The date of the final exam is scheduled in the assessment calendar of the Faculty.

"The dates of evaluation activities cannot be modified, unless there is an exceptional and duly justified reason why an evaluation activity cannot be carried out. In this case, the degree coordinator will contact both the teaching staff and the affected student, and a new date will be scheduled within the same academic period to make up for the missed evaluation activity." Section 1 of Article 115. Calendar of evaluation activities (Academic Regulations UAB). Students of the Faculty of Economics and Business, who in accordance with the previous paragraph need to change an evaluation activity date must process the request by filling out an Application for exams' reschedule https://eformularis.uab.cat/group/deganat_feie/application-for-exams-reschedule

Grade Review Procedure

Coinciding with the final exam, the day and medium in which the final grades will be published will be announced. In the same way, the procedure, place, date and time of the review of exams will be informed in accordance with the regulations of the University.

Recovery Process

“To participate in the recovery process, students must have been previously evaluated in a set of activities that represents a minimum of two thirds of the total grade for the subject or module.” Section 3 of Article 112 ter. Recovery (UAB Academic Regulations). Students must have obtained an average grade for the subject between 3.5 and 4.9.

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 cannot be subject to further evaluation. If the student grade is less than 3.5, the student will have to repeat the course the following year. Students whohave obtained a grade that is equal to or greater than 3.5 and less than 5 can take a second chance exam. The lecturers will decide the type of the second chance exam. When the second exam grade is greater than 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 grade achieved in the course grade (not the second chance exam grade).

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

The date of the retake exam will be posted in the calendar of evaluation activities of the Faculty. Students who take this exam and pass, will get a grade of 5 for the subject. If the student does not pass the retake, the grade will remain unchanged, and hence, student will fail the course. 

 

Irregularities in the assesment process

In spite of other disciplinary measures deemed appropriate, and in accordance with current academic regulations, "in the case that the student makes any irregularity that could lead to a significant variation in the grade of an evaluation activity, it will be graded with a 0, regardless of the disciplinary process that can be instructed. In case of various irregularities occur in the evaluationof the same subject, the final grade of this subject will be 0". Section 10 of Article 116. Results of the evaluation. (UAB Academic Regulations).

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Attendance and participation in class discussions 20% 10 0.4 8, 13, 14, 15, 16, 17, 21, 5
Exercises for individual assessment 40% 3 0.12 10, 6, 12, 13, 14, 15, 16, 11
Individual or group exercises 40% 12 0.48 1, 2, 3, 4, 8, 10, 6, 7, 9, 12, 13, 14, 15, 16, 11, 17, 21, 5, 19, 18, 20

Bibliography

BLOCK I:

 

  • Sharma, T., D. Sarkar, R. Bali (2017)  Learning Social Media Analytics with R: Transform data from social media platforms into actionable business insights.
  • Lilien, G.L. and Rangaswamy, A., (2004) Marketing Engineering: Computer AssistedMarketing Analysis and Planning, Ed. Prentice Hall. 
  • Chapman, N.C., and McDonnell, E., Feit. (2015) R for Marketing Research and Analytics, Springer-Verlag,Switzerland, 2015 
  • Miller, T. W. (2015). Marketing Science: Modeling Techniques in Predictive Analyticswith R and Python (1 edition). OldTappan, New Jersey: Pearson FT Press. (Https://mdsr-book.github.io/exercises.html ) 
  • Grigsby, M. (2015). Marketing Analytics: A practical guide to real marketing science (1 edition). London: Philadelphia: Kogan Page. 
  • Winston, W. L. (2014). Marketing analytics: Data-driven techniques with microsof texcel. ProQuestEbook Central https://ebookcentral.proquest.com Part B: 
  • Arthur, L. (2013). Big data marketing: Engage your customers more effectively and drive value. ProQuestEbook Central https://ebookcentral.proquest.com 
  • Lantz, B. (2015) Machine Learning with R, PacktPublishing. 
  • Chapman, C. and E. McDonnellFeit (2015) R for Marketing Research and Analytics, Springer. 
  • Rocha, A., Reyes, J. L., Peter, M. K., & Bogdanovic, Z. (2020). Marketing and Smart Technologies. In Smart Innovation, Systems and Technologies (Vol. 167). https://doi.org/10.1007/978-981-15-1564-4_6

 

BLOCK II:  

 

  • Casado Aranda, Luis A. (2021): Neurociencia del consumidor, Ediciones Pirámide; 1ª edición (7 octubre 2021) 
  • Cisneros Enríquez, A. (2017). Neuromarketing y neuroeconomía (3a. Ed.). ECOE Ediciones. https://elibro.net/es/ereader/uab/70497?page=1 
  • Dham, S. (2016). TheFoundations of Behavioral Economic Analysis.Oxford: OUP Oxford. 
  • Gentner, F. (2012). Neuromarketing in the BTB sector: Importance, potential and its implicacions for brand management. Pro Quest Ebook Central https://ebookcentral.proquest.com 
  • Kahneman, D. (1990). Experimental Tests of the Endowment Effect and the Coase Theorem. Journal of Political Economy, 98 (6), 1325-1348. 
  • Kahneman, D. (2003). Experienced utility and objective happiness: a time-based approach. In I. Brocas & J. D. Carrillo (Eds.), The Psychology of Economic Decisions. Vol 1: rationality and well-being. Oxford: OxfordUniversity Press. 
  • Kahneman, D. (2012). Thinking, Fast and Slow. London: Penguin. 
  • LOK, J. C. (n.d.). JudgementThedifferencebetweenBehavioralEconomyandPsychologicalMethods To Predict Consumption. 
  • Patzer, G. (1996). Experiment-Research Methodology in Marketing: Types and Applications. Praeger. 
  • Thaler, R. H. (2016). Misbehaving: The Making of Behavioural Economics (01 edition). London: Penguin. Disponible en castellano como: La Psicología Económica. Bilbao: Deusto.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: improving decisions about health, wealth, and happiness. New Haven: Yale University Press. 
  • Wilkinson, N. (2008). An Introduction to Behavioral Economics. Palgrave Macmillan. 

 

Software

R software

R CLOUD

Gazepoint Analysis

Pupil Lab