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Publicação
Data-driven customer segmentation in the medical beauty industry: clustering models and natural language processing techniques
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| 8.48 MB | Adobe PDF |
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Resumo(s)
This research study performs a customer segmentation on a medical beauty case to inform data driven decision making and refine marketing efforts. RFM analysis with a K-Means clustering
algorithm, and an HDBSCAN after UMAP dimensionality reduction have been applied.
Natural language processing, Doc2Vec, served as the means for sequence embedding. A
decision tree algorithm is used to interpret the clustering results. HDBSCAN achieved the best
clustering results, and customers were grouped into five clusters based on their purchasing
behavior and demographics. The recommended model is the RFM model, and the resulting
three customer groups are used to derive marketing strategy recommendations.
Descrição
Palavras-chave
Business analytics Customer segmentation Clustering Natural language processing