Description

Market segmentation is a useful marketing strategy that aims to divide a homogeneous customer market into more defined segments, allowing businesses to understand their target customers' dynamics. Market segmentation is crucial for businesses as it enables them to launch targeted marketing campaigns tailored to customers' specific needs. Different data types may be used to segment customers, such as demographic, geographical and behavioural data.

In this project, you will train unsupervised machine learning algorithms to perform customer market segmentation. For example, clustering algorithms (cluster analysis) are commonly used to assign customers into groups so that customers belonging to the same group are more similar than customers in another group. A distance measure determines the similarity of two customers; thus, choosing an appropriate distance measure is a critical step in cluster analysis. There are many different clustering algorithms, such as K-Means clustering, Hierarchical clustering and Gaussian Mixture Model algorithm. Another useful tool is the Principal Component Analysis (PCA) which often used as a dimensionality reduction algorithm.

There are many interesting theoretical and applied topics students may want to work on, where implementation can be done using the statistical package R or Python.

Prerequisites

Corequisites

Statistical Methods III (strongly recommended)

References

• Market segmentation: Wikipedia.
• Cluster analysis: Wikipedia.
• Principal component analysis: Wikipedia.
• Everitt, B., Landau, S., Leese, M. and Stahl, D. Cluster Analysis, 5th ed. Wiley, 2011.
• Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Chapter 14), 2009. [PDF]
• James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning with Applications in R (Chapter 10), 2013. [PDF]
• Jolliffe I.T. and Cadima J. Principal Component Analysis: A Review and Recent Developments. Phil. Trans. R. Soc. A 374: 20150202, 2016. [PDF]
• Provost, F. and Fawcett, T. Data Science for Business. Sebastopol, O'Reilly, 2013.