Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering.
Effective and well-structured customer segmentation enables organizations to accurately identify and comprehend the distinct characteristics and needs of various customer groups, thereby facilitating the development of more targeted marketing strategies. Contemporary artificial intelligence technolo...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318519 |
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| Summary: | Effective and well-structured customer segmentation enables organizations to accurately identify and comprehend the distinct characteristics and needs of various customer groups, thereby facilitating the development of more targeted marketing strategies. Contemporary artificial intelligence technologies have emerged as the predominant tools for customer segmentation, owing to their robust capabilities in analyzing complex datasets and extracting profound customer insights. This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with K-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. Initially, a correlation matrix is used to identify redundant noise and multicollinear features within customer feature groups, and Principal Component Analysis is applied for denoising and dimensionality reduction to enhance the ability of the model to identify potential features. Subsequently, a parameter adaptive adjustment method based on Q-learning is proposed, which significantly augments the clustering performance of K-means. Ultimately, the effectiveness of the proposed method is validated using a Kaggle dataset, and the elbow method is employed to ascertain the optimal number of clusters. Based on the cluster category centers, the typical characteristics of different customer types are analyzed. Furthermore, four widely recognized machine learning methods are employed to classify the clustering results, achieving over 95% classification accuracy on the test set. The experimental results demonstrate that the proposed model exhibits a high degree of customer characteristic identification and segmentation, which not only enhances marketing efficiency and customer satisfaction but also fosters corporate profit growth through the strategic formulation of various marketing initiatives. |
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| ISSN: | 1932-6203 |