Segmentation of Generation Z Spending Habits Using the K-Means Clustering Algorithm: An Empirical Study on Financial Behavior Patterns
DOI:
https://doi.org/10.30871/jaic.v9i6.11506Keywords:
Financial Decision-Making, Generation Z, K-Means Clustering, Segmentation, Spending BehaviorAbstract
Generation Z, born between 1997 and 2012, exhibits unique consumption behaviors shaped by digital technology, modern lifestyles, and evolving financial decision-making patterns. This study segments their financial behavior using the K-Means clustering algorithm applied to the “Generation Z Money Spending” dataset from Kaggle. In addition to K-Means, alternative clustering algorithms—K-Medoids and Hierarchical Clustering—are evaluated to compare their effectiveness in identifying behavioral patterns. The dataset consists of 1,700 individuals with 15 numerical spending attributes, including rent, food, entertainment, education, savings, and investments. All data were normalized using Min-Max Scaling prior to clustering. The analysis identifies six distinct clusters, ranging from highly consumption-oriented groups (with higher spending on entertainment and online shopping) to financially conscious groups prioritizing savings and investments. A quantitative approach was used, incorporating exploratory data analysis, correlation testing, and the Elbow Method to determine the optimal number of clusters. The optimal cluster count of six is supported by a Davies-Bouldin Index (DBI) score of 2.412, indicating acceptable but improvable cluster separation. Each cluster displays unique characteristics: Cluster 0 (average age 20.6) focuses on savings and investments with moderate essential spending; Cluster 1 (average age 23.6) prioritizes education and higher rent expenses; Cluster 2 (average age 20.3) is digitally oriented, spending more on online shopping and entertainment; Cluster 3 (average age 25.2) demonstrates financial stability with balanced expenditures; Cluster 4 (average age 24.9) emphasizes savings and investments with moderate living costs; and Cluster 5 (average age 24.96) combines strong saving habits with balanced essential and leisure spending. Model performance was assessed using the Davies-Bouldin Index, Silhouette Score, and Calinski-Harabasz Index to ensure comprehensive evaluation of cluster quality. The findings highlight the diverse spending behaviors of Generation Z, offering valuable insights for businesses, policymakers, and financial service providers to develop targeted strategies aligned with each segment’s characteristics.
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