IB9JB-15 Marketing and Strategy Analytics
Introductory description
This module will provide students with the ability to use information to generate meaningful insights about the behaviour of their customers.
Module aims
Will provide students with the ability to use information to generate meaningful insights about the behaviour of their customers.
Will provide students with the knowledge of how businesses can exploit opportunities for value creation and improve their financial performance based on insight.
Will challenge students' thinking about the appropriate and inappropriate use of customer data for strategic decision-making and understand the processes of decision making.
Will develop students’ critical and analytical skills through group work
Will provide students with the knowledge and skills to effectively present data/ insights to a variety of audiences from fellow marketers to finance directors or the CEO.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
- Marketing and Strategy Analytics and Business Performance. What is Marketing and Strategy Analytics?
- How can Marketing and Strategy Analytics help to improve business performance?
- Marketing and Strategy Analytics Methods and how to apply them.
- Introduction to the Software R (or another appropriate software package).
- Fundamentals of Customer Data Analysis. Describing data and studying relationships between different types of customer information.
- Advanced Marketing Applications and Using data for decision making.
- How to segment customers (decision trees, regression trees, clustering).
- Sharing your insights with different audiences.
Learning outcomes
By the end of the module, students should be able to:
- Understand the central and essential premises of marketing and strategy analytics, and critically evaluate its role, usefulness and applicability in a business context.
- Understand different marketing and strategy analytics tools such as customer segmentation, linear regression, and the identification of relationships between different types of customer information.
- Demonstrate an ability to evaluate the appropriateness of different marketing and strategy analytics techniques in specific contexts.
- Demonstrate an ability to evaluate the value of market research insights generated by third parties.
- Demonstrate analytical skills through the evaluation of cases.
- Demonstrate the ability to conduct effective research and synthesise logical arguments.
- Critically evaluate data collection practices in businesses from a marketing and strategy analytics perspective.
- Recognise the importance of marketing and strategy analytics for value creation in business-customer relationships.
Indicative reading list
The suggested key texts for this module will be:
Brownlee, J. (2016) Master Machine Learning Algorithms. http://MachineLearningMastery.com
Chapman, C. and Feit, E.M. (2015) R for Marketing Research and Analytics. Springer
Lantz, B. (2015) Machine Learning with R (Second edition). Birmingham: Packt Publishing
Miguel Forte, R. (2015) Mastering Predictive Analytics with R. Birmingham: Packt Publishing
In addition, books that provide an introduction to marketing and business analytics may be useful for students:
Grigsby, M. (2018) Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques. London: KoganPage.
Pauwels, K. (2014) It's Not the Size of the Data - It's How You Use It: Smarter Marketing with Analytics and Dash-boards. New York: American Management Association.
Pinder, J.P., 2016. Introduction to Business Analytics Using Simulation. Academic Press.
In addition, selected articles from leading Marketing Journals such as Journal of Marketing Research, Journal of Marketing, Marketing Science, and International Journal of Research in Marketing will be used to illustrate state of the art applications that go beyond the textbook content. For example:
Germann, F., Lilien, G.L. and Rangaswamy, A. (2013) ‘Performance Implications of Deploying Marketing Analytics’, International Journal of Research in Marketing, 30(2), pp. 114–128.
Lilien, G.L. (2011) ‘Bridging the Academic–Practitioner Divide in Marketing Decision Models’, Journal of Marketing, 75(4), pp. 196–210.
Wedel, M. and Kannan, P.K. (2016) ‘Marketing Analytics for Data-Rich Environments’, Journal of Marketing, 80(6), pp. 97–121.
Subject specific skills
Demonstrate understanding of the application and implementation of a broad range of marketing and strategy analytics methods in the software R (or another appropriate software package).
Demonstrate an ability to view problems in marketing and strategy analytics from a Bayesian perspective.
Demonstrate the use of graphics to communicate marketing and strategy analytics insights effectively to third parties.
Transferable skills
Demonstrate written communication skills.
Demonstrate effective problem solving skills (both theoretically, and when it comes to the programming implementation of a selected method).
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 1 hour 30 minutes (19%) |
| Practical classes | 10 sessions of 1 hour 30 minutes (19%) |
| Private study | 48 hours (62%) |
| Total | 78 hours |
Private study description
Private study to include preparation for lectures and practical classes
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Individual Assignment | 100% | 72 hours | Yes (extension) |
Feedback on assessment
Assignments are graded (%) using standard University Postgraduate Marking Criteria and written feedback is provided, plus an opportunity to discuss the assignment with the module leader on a one-to-one basis.
There is currently no information about the courses for which this module is core or optional.