MARK 612
Marketing Analytics
Fall 2026
Topics
- Course introduction Topic 1. What is Marketing? • Marketing; 4Ps; Personal value equation
- Paper
- University closed in honor of the National Day for Truth and Reconciliation
- Project workshop
- Project work session
- Presentation part 1
- Presentation part 2
Readings
- Salganik, M. J. (2019). Bit by Bit: Social Research in the Digital Age. Princeton University Press
- Principles of Marketing Version 5.0 by Jeff Tanner and Mary Anne Raymond
Fall 2023
Topics
- Focus Groups
- Questionnaire Design
- Observational Research
- Marketing Experiments, A/B Tests, and Test Markets
- Assessed different data collection methods such as surveys, focus groups, observations, and marketing experiments
- Designed a questionnaire
- Calculated, applied and interpreted metrics to improve marketing decisions
Readings
- Hair, J.F., Harrison, D., & H. Ajjan (2022), Essentials of Marketing Analytics, 1st Edition. NY: McGraw Hill
- Palmatier, R.W., Petersen, J.A., & F. Germann (2022), Marketing Analytics Based on First Principles, 1st edition, NY: Bloomsbury
- Bendle, N.T., Farris, P.W., Pfeifer, P.E., & D.J. Reibstein (2016), Marketing Metrics: The Manager’s Guide to Measuring Marketing Performance, 3 rd edition, NJ: Pearson
- McDaniel, C., Gates, R., Sivamakrishnan, S. & K. Main (2013), Marketing Research Essentials, 2nd edition, NJ: Wiley
Sources
- Course catalogue ↗2026–2027
- 2026-Fall-MARK-312-LECTURE-A01 ↗2026
- 2026-Fall-MARK-612-LECTURE-A01 ↗2026
- 2007-Fall-MARK-612-LECTURE-A2 ↗2007
- 2008-Fall-MARK-612-LECTURE-A1 ↗2008
- 2009-Fall-MARK-612-LECTURE-X1 ↗2009
- 2010-Fall-MARK-612-LECTURE-A1 ↗2010
- 2011-Fall-MARK-612-LECTURE-A1 ↗2011
- 2015-Winter-MARK-612-LECTURE-X50 ↗2015
- 2016-Winter-MARK-612-LECTURE-X51 ↗2016
- 2018-Winter-MARK-612-LECTURE-X50 ↗2018
- 2018-Fall-MARK-612-LECTURE-A1 ↗2018
- 2019-Fall-MARK-612-LECTURE-A2 ↗2019
- 2020-Fall-MARK-612-LECTURE-A1 ↗2020
- 2023-Fall-MARK-612-LECTURE-A01 ↗2023
- Partial automated extraction.
- Additional readings are supplied through the course LMS. Partial automated extraction.