IB964-15 Customer Analytics
Introductory description
Businesses today have unprecedented access to information about their customers. However, many businesses fail to use this information to generate meaningful insights about the behaviour of their customers. The consequence is that these businesses fail to exploit opportunities for value creation, and improving their financial performance.
Module aims
This module is inspired by the idea that 'It's not the size of the data, it's how you use it'.
Hence, the principal aim of this module is to challenge students' thinking about the appropriate and inappropriate use of customer data for strategic decision-making including ethical issues involved in handling and using data.
Using real-world cases as context for data analyses, we will discuss good and bad practices for how to derive meaningful insights from data. Examples include comparing data for different groups of customers, finding clusters of customers, and how to analyse relationships between different types of customer data.
Students will not only improve their theoretical understanding of empirical methods, but will also learn how to apply these methods in the widely-used software R.
Once these insights have been generated, it is also important to know how to use graphical representations of these insights for story-telling in business presentations. Accordingly, this module will also place great emphasis on good practices in the context of presentations with data.
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.
This topic is a dynamic and expanding field and the syllabus will reflect that dynamism. Typically (but not exclusively) it would cover the following:
Part 1: Customer Analytics and Business Performance
1.1 What is Customer Analytics?
1.2 How can Customer Analytics help to improve business performance?
Part 2: Customer Analytics Methods and how to apply them
2.1 Introduction to the Software R
2.2 Fundamentals of Customer Data Analysis:
a) Describing Data
b) Studying Relationships between different types of customer information
2.3 Advanced Marketing Applications:
a) How to segment customers,
b) choice modelling;
c) structural models;
d) artificial intelligence and machine learning;
Part 3: Sharing your insights with others
3.1 Using graphs for storytelling
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of the central and essential premises of customer analytics, and critically evaluate its role, usefulness and applicability in a business context.
- Demonstrate a comprehensive understanding of the different customer analytics tools such as customer segmentation, choice modelling,the identification of relationships between different types of customer information, and machine learning techniques.
- Demonstrate developed analytical skills through the evaluation of cases.
- Conduct effective research and synthesise logical arguments
- Critically evaluate data collection practices in businesses from a customer analytics perspective
- Demonstrate a comprehensive understanding of the importance of customer analytics for value creation in business-customer relationships
Indicative reading list
Reading lists can be found in Talis
Research element
Students learn how to conduct quantitative analyses in research projects
Interdisciplinary
Statistical knowledge and skills can be applied to other contexts
International
Application of international contexts
Subject specific skills
Apply and implement a broad range of marketing analytics methods in the software R
Deploy graphics to communicate customer analytics insights effectively to third parties
Evaluate the appropriateness of different customer analytics techniques in specific contexts and the value of market research insights generated by third parties
Transferable skills
Written communication skills
Verbal communication skills.
Problem solving skills (both theoretically, and when it comes to the programming implementation of a selected method).
Work within a team to analyse issues and propose solutions.
Study time
| Type | Required |
|---|---|
| Online learning (scheduled sessions) | 9 sessions of 1 hour (6%) |
| Other activity | 21 hours (14%) |
| Private study | 48 hours (32%) |
| Assessment | 72 hours (48%) |
| Total | 150 hours |
Private study description
Self study is pre-reading for lectures and workshops
Other activity description
9 x 2 hrs F2F workshops
3 hrs presentations
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 A4
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Group Presentation | 20% | 15 hours | No |
Reassessment component |
|||
| Individual assignment | Yes (extension) | ||
Assessment component |
|||
| Individual Assignment | 80% | 57 hours | Yes (extension) |
Reassessment component is the same |
|||
Feedback on assessment
Assignments are graded (%) using standard University Postgraduate Marking Criteria and written feedback is provided.
Courses
This module is Optional for:
- Year 1 of TIBS-N1F5 Postgraduate Taught Business and Finance
- Year 1 of TIBS-N1F2 Postgraduate Taught Business with Consulting
- Year 1 of TIBS-N1F3 Postgraduate Taught Business with Marketing
- Year 1 of TIBS-N1QG Postgraduate Taught Business with Operations Management
- Year 1 of TIBS-N1F4 Postgraduate Taught International Business (MINT)
- Year 1 of TIBS-N2N3 Postgraduate Taught Management