24753 Customer Analytics
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Requisite(s): ((24734 Marketing Management OR 24834 Marketing Decision Making) AND 24710 Customer Experience and Behaviour)
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 240753 Customer Analytics
Description
Customer analytics is the process of using customer data to help inform business decisions via segmentation, market basket analysis, and Customer Relationship Management (RCRM) and marketing mix optimization. Customer analytics assists organizations in gaining insight into consumer behaviours to increase the effectiveness of a product or service marketing mix.
Subject learning objectives (SLOs)
| 1. | formulate appropriate analysis or models for identified business problems |
|---|---|
| 2. | apply appropriate analytical methods and techniques to data to analyse a wide range of marketing problems |
| 3. | generate insights from analytical results |
| 4. | communicate and present effectively the findings of analytical results |
Contribution to the development of graduate attributes
It is imperative to create customer values by understanding customer needs and wants. With abundance of customer data and rapid advancement in technology, it enables firm to perform sophisticated empirical analyses of customer behaviours and to identify the ways to create and enhance the sources of customer values. It has also become increasingly important to know how marketing actions translate into revenue and profit growth. This subject develops students’ ability to use quantitative and analytical techniques to draw insights from information about customers, which will be linked to business strategy. This involves investigating customer data, building models of customer behaviours, predicting business related outcomes and providing possible solutions to the problems. It also helps students develop practical and advanced research skills to extract relevant information from large amounts of complex data to make improved business decisions such as market segmentation, customer relationship management and demand forecasting.
Teaching and learning strategies
The subject is based on dynamic and interactive lecture and workshop sessions. It is taught through a combination of lectures and workshops. The lectures present prominent marketing problems and illustrate analytical tools/techniques to solve those problems. The lectures involve critical debate with case studies. The workshops are built around in-class exercises with appropriate computer software including Excel, SPSS and/or R. The lectures and workshops will include time where students will work together to apply analytical methods into marketing problems. These classes are supplemented with both printed and electronic learning materials and resources which students are expected to have read prior to classes. The learning management system will be used to share information and encourage interaction between staff and students.
Feedback throughout the session will be given on a weekly basis, verbally and formatively by the instructors to clarify student’s understandings of key concepts and analytical skills. Further feedback will be provided on assessment items throughout the session.
Content (topics)
- Customer segmentation & profiling
- Market basket analysis
- Customer acquisition & Retention
- Customer Lifetime Value (CLV)
- Customer Relationship Management (CRM)
- Product recommendation
- Digital customer analytics
Assessment
Assessment task 1: Group Project (Group)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3 and 4 |
|---|---|
| Type: | Project |
| Weight: | 30% |
| Length: | Final report should be no more than 15 word pages excluding appendices and references. Relevant tables and figures must be included in the main report. It is not required to attach or submit any intermediate analyses steps, e.g., program coding, unnecesary tables or figures, etc. |
Assessment task 2: In-Class assignments and Homework (Individual)*
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
|---|---|
| Type: | Quiz/test |
| Weight: | 40% |
| Criteria: | *Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 3: Final Exam (Individual)
| Objective(s): | This addresses subject learning objective(s): 2 and 3 |
|---|---|
| Type: | Examination |
| Weight: | 30% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
There is no required textbook for this subject. Readings, lecture notes, and assignments will all be available on UTSOnline. Some materials may require copyright purchase.
References
Mike Grigsby (2016), Advanced Customer Analytics: Targeting, Valuing, Segmenting and Loyalty Techniques, Kogan Page Ltd, London UK.
Chapman, Chris and Elea McDonnell Feit (2019), R for Marketing Research and Analytics, 2nd Edition, Springer.
Malhtra, Naresh K. (2019), Marketing Research: An Applied Orientation, 7th Edition, Pearson Education
Coakes, Sheridan J. and Clara Ong (2012), SPSS: Analysis without Anguish (Version 20 for Windows), Milton, QLD: John Wiley & Sons Australia, Ltd