Publication
Data-driven customer segmentation in the medical beauty industry: clustering models and natural language processing techniques
| Summary: | 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. |
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| Main Authors: | Indorf, Fenja |
| Subject: | Business analytics Customer segmentation Clustering Natural language processing |
| Year: | 2022 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | 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. |
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Funded activities
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