Learning outcomes

The course mainly develops the following competences: knowledge and reasoning, scientific and systematic approaches and personal development and to a lesser extent the other competences of the competency framework, i.e., corporate citizenship, innovation and entrepreneurship, professional development, teamwork and leadership as well as communication and interpersonal skills.

Goals

The course Methods for Service and Marketing Research (MSMR) has two main objectives.

First of all, it aims at training students in quantitative methods that are useful for solving marketing/service research problems.

The second objective is to confront students with marketing/service problems, real data and statistical softwares in a decision-making perspective.

This course thus deals with concepts, methods, and applications of decision modelling to address current issues faced by marketing managers. It provides students with skills to translate conceptual understanding into specific operational plans.

Through case studies and classroom exercises, students are also trained in using statistical software.

Content

The course starts with a general introduction to the marketing research process with a service perspective as well as to marketing decision models and modelling (the so called “marketing engineering” approach). The methods (and topics) covered are exploratory and confirmatory factor analysis methods (measurement), regression analysis, simulation and optimization techniques (pricing), regression analysis with dummies and analysis of variance techniques (testing relationships between service marketing constructs), cluster and discriminant analysis (segmentation & targeting), judgment-based models (advertising budgeting). More specifically, this course aims at helping students understand by means of various examples and case studies, how analytical techniques and computer decision models can enhance decision making by converting data and information about markets to insights and decisions. It also provides students with software tools (SAS and Excel) that will enable them to apply the methodological approaches taught in the course to real marketing decision problems.

Varying each year, the topics covered are (2-hour session - example of content):

  • Session 1: Introduction to service marketing research and decision-making
  • Session 2: Response models and modelling
  • Session 3: Case study discussion – e.g., advertising budging with judgment-based estimation models
  • Session 4: Linear and nonlinear regression analysis techniques (from estimation to prediction)
  • Session 5: Case study discussion – e.g., solving a pricing decision problem
  • Sessions 6 & 7: Measurement issues – scale development and testing with factor analysis techniques (EFA and CFA, scale reliability and validity testing)
  • Session 8: Case study discussion – e.g., measuring service marketing constructs
  • Session 9: Segmentation and profiling issues with cluster and discriminant analyses
  • Session 10: Case study discussion – e.g., segmenting, targeting and positioning for a new smartphone
  • Sessions 11 to 12: testing relationships between service marketing constructs (moderation and mediation analyses with linear regression and analysis of variance techniques)
  • Session 13: Case study discussion – e.g., from service quality to satisfaction and loyalty

Teaching methods

The course is taught by two university professors: Prof. Dr. Philippe Baecke (associate professor at Vlerick School of Management, researcher in the field of data analytics and customer realtionship management) et Prof. Dr. Pietro Zidda (full professor at UNamur, researcher in the field of retailing, loyalty management and customer relationship marketing). Each concept/method/issue covered in classroom has a software implementation (e.g., SPSS, SAS and/or Excel) with the solving of a case study. The course emphasizes interactions between students and the instructor.

Assessment method

The evaluation of students is made by means of an ongoing assessment and a final written examination:

  • Ongoing evaluation (30 to 40% of the final grade): Each student’s work is evaluated during the case studies (based on case preparation, study steps, class participation, etc.); a personal feedback is given at the end of the class
  • Final exam (60 to 70% of the final grade): Final evaluation is made by means of a written examination covering all the topics discussed in the classroom. It comprises a theoretical part (concepts and theory, NOT mathematical formulas!) and an application part (exercise and/or short case study).

Sources, references and any support material

The course pack (slides) is available before the course on WebCampus platform.

Reference textbooks (non-exhaustive list) are:

  • Hair, J., Black, W.C., Babin, B.J., and Anderson, R.E. (2009). Multivariate data analysis (7th ed.). Upper saddle River, New Jersey: Pearson Education International.
  • Iacobucci, D., and Churchill, G. A. (2015). Marketing Research: Methodological Foundations (11th Edition). The Dryden Press.
  • Lilien, G.L., Rangaswamy, A., and De Bruyn, A. (2007), Principles of marketing engineering, Trafford Publishing
  • SAS documentation

+ journal articles in Journal of Service Research, Journal of Marketing, Journal of Retailing, Recherche et Applications en Marketing (English version), etc.

Language of instruction

English