Uncovering Customer Segments from Spending and Demographic Data with HDBSCAN : Defining Customer Groups From Spending Data of Bank Customers

Sammanfattning

This thesis explores the application of clustering algorithms to identify consumer segments based on spending behaviors and demographic characteristics. The thesis is conducted in partnership with Svenska Handelsbanken. The bank's current tools for financial overview lack the capability to contextualize and give feedback on customers' spending relative to relevant peers. This study aims to identify data-driven customer groups within the bank’s user base from customer spending data and to evaluate the spending groups against each other and the clustering algorithms utilized, as a pre-study to later be able to create the comparison groups necessary to develop individualized products, such as relativized spending feedback. The study employs two clustering algorithms, k-means and HDBSCAN, to analyze and group customers based on their spending profiles derived from transactional data. The efficacy of these algorithms is compared to identify distinct consumer groups. Logarithmized spending data and dimensionality reduction with t-SNE and PCA is used to prepare the data for clustering. The analysis successfully identified distinct consumer groups with demographic and consumption differences, and the best results were achieved with HDBSCAN based on reduced data via t-SNE. The clusters were validated with new data inputs, ensuring stability and robustness in the customer segments. This study lays the groundwork for banks to offer personalized financial insights and services, with the potential to enhance customer satisfaction and decision-making, as well as targeted initiatives from the bank. Future research can build on these findings to create even more granular clusters or incorporate external demographic data and analyze household-level spending, further refining customer segmentation and personalization efforts.