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Customer segmentation for private market investments: exploring investment behaviour to develop user profiles for Finexity
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 2.59 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
The rise of private market investments, fueled by blockchain-enabled tokenisation,
represents a new trend in the financial sector. Thereby, fintech platforms offering these
investments must effectively segment investors into distinct groups to retain them and finance
respective assets. To identify and analyse customer segments and pinpoint premium investors,
clustering methods k-means, hierarchical clustering, and DBSCAN were applied to four feature
sets: demographic, behavioural, combined demographic-behavioural, and UMAP-transformed
combined features. While behavioural features produced the most interpretable results, UMAP transformed combined features delivered the most accurate segmentation. These findings
provide actionable insights for implementing tailored marketing strategies for each customer
segment.
Descrição
Palavras-chave
Data science Cluster analysis Behavioural analysis User profiling K-Means Hierarchical clustering DBSCAN UMAP High-dimensional data Private market investments Tokenization Investment platform Real world assets