Teaching marketing analytics

If you are teaching a full-blown marketing analytics course, these are the teaching resources we recommend for you… Not familiar with Enginius yet? Click here to discover how Enginus can boost your teaching.

Teaching resources

Teaching slides, case studies, reference book, standalone exercises… We have worked very hard to make your life easier.

Marketing models

Enginius includes the most important models and analytics you may need to include in a marketing analytics course, such as segmentation, positioning, conjoint analysis, regression analysis (predictive modeling), resource allocation, etc.

Example syllabus (10 weeks)

Typical course description:

Several forces are transforming the nature, scope, and structure of the marketing profession. Marketers are seeing increasingly faster changes in the marketplace and are barraged with an ever increasing amount of information. While many view traditional marketing as art and some view it as science, today’s marketing increasingly looks like engineering. This course aims at educating and training a new generation of marketing managers – and to expose non-marketing students to this relatively new, yet increasingly important facet of marketing. The goal of this course is to train marketing managers to translate concepts into context-specific operational decisions and actions using analytical, quantitative, and computer modelling techniques.

This course aims at exposing students to the application and presentation of analytical and statistical methods to solve marketing problems, especially as they relate to customer description, segmentation, targeting, lifetime value, customer relationship management, and optimization of marketing actions and tactics, such as pricing or resource allocation.

1.Introduction (week 1)

– Course overview, grading and assignments.
– What is marketing? Marketing management? Marketing analytics?

2.Marketing analytics in today's world (week 2)

– Marketing models
– Success stories
– In-class exercise: Allegro smart sheet
– In-class exercise : Conglom reallocation

3.Segmentation (week 3)

– Segmentation
– Discriminant analysis

4.Scoring and targeting (week 4)

– Segment selection and targeting
– GE McKinsey matrix
– Scoring models

5.Positioning (week 5)

– Case study presentations: HOPE Foundation (predictive modeling)
– Positioning analysis

6.Customer lifetime value (week 6)

– Case study presentation: Heinecken
– Customer lifetime value

7.Conjoint analysis (week 7)

– Conjoint analysis
– In-class demonstration

8.Resource allocation (week 8)

– Case study presentation: Dürr Environmental (conjoint + segmentation) or Kirin (conjoint + segmentation + positioning)
– Salesforce management
– Resource allocation

9.Pricing (week 9)

– Pricing
– Revenue management

10.Wrap-up (week 10)

– Case study presentation: Zach’s garage (pricing)
– Industry outlook and trends
– Course wrap-up

Questions? Need help?

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