Market Segmentation

The goal of market segmentation is to replace a one-size-fits-all view of customers with a structured picture of distinct groups. Understanding that some customers are price-sensitive, others are frequent buyers, and others rarely engage helps an organisation decide how to price products, which offers to make, and how to communicate with different groups.

The project starts with selecting which customer data to use: transaction history, demographics, product usage, or a combination. A clustering algorithm is then applied to group customers by similarity, and the resulting clusters are interpreted to describe what each group has in common. Choosing the right number of clusters and validating that they are meaningful is a significant part of the work. The final segments are described in plain terms so that marketing, product, and commercial teams can act on them.

Python and R are the main tools, using libraries like scikit-learn for clustering and pandas for data preparation. Visualisation tools help explore and present clusters. Dimensionality reduction techniques such as PCA are sometimes used before clustering to reduce noise in high-dimensional data.

Market segmentation projects appear in retail, financial services, insurance, and digital platforms. In the Netherlands, they are common at large retailers, banks, and telecoms providers. The project connects to Data Analyst, Data Scientist, and Market Researcher roles, and often feeds into Pricing Analytics or Marketing Analytics work.