An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior
In today's highly competitive market, organizations face significant challenges in accurately understanding and segmenting customer behavior due to the inherently dynamic and evolving nature of customer interactions over time. Traditional customer segmentation methods often neglect these tempor...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Computers in Human Behavior Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958824001532 |
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| author | Hodjat (Hojatollah) Hamidi Bahare Haghi |
| author_facet | Hodjat (Hojatollah) Hamidi Bahare Haghi |
| author_sort | Hodjat (Hojatollah) Hamidi |
| collection | DOAJ |
| description | In today's highly competitive market, organizations face significant challenges in accurately understanding and segmenting customer behavior due to the inherently dynamic and evolving nature of customer interactions over time. Traditional customer segmentation methods often neglect these temporal variations, leading to ineffective business strategies and missed opportunities. This research addresses this critical gap by introducing an innovative time series-based approach for customer behavior segmentation. By modeling each customer's behavior as a time series capturing key metrics such as purchase frequency, transaction amounts, and customer lifecycle costs the proposed method dynamically adapts to behavioral changes over time. To enhance segmentation precision, a genetic algorithm is employed to optimize feature weights, ensuring that the most relevant factors are emphasized. These optimized features are then clustered using spectral clustering to identify distinct and meaningful customer segments. The effectiveness of the proposed method is validated using 30 months of transactional data from a payment services company. The results demonstrate that the proposed approach, particularly when combined with spectral clustering and optimally weighted features, significantly surpassing the performance of traditional static segmentation techniques. This research not only provides a more accurate framework for uncovering hidden patterns in customer behavior but also delivers actionable insights for targeted marketing and personalized customer strategies. |
| format | Article |
| id | doaj-art-12363716f6ab4a35a7d738fca2b4ec4f |
| institution | Kabale University |
| issn | 2451-9588 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers in Human Behavior Reports |
| spelling | doaj-art-12363716f6ab4a35a7d738fca2b4ec4f2024-12-18T08:50:12ZengElsevierComputers in Human Behavior Reports2451-95882024-12-0116100520An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behaviorHodjat (Hojatollah) Hamidi0Bahare Haghi1Corresponding author. IT Information Technology Engineering Group K.N. Toosi University of Technology, Iran.; Department of Industrial Engineering, Information Technology Group, K. N. Toosi University of Technology, IranDepartment of Industrial Engineering, Information Technology Group, K. N. Toosi University of Technology, IranIn today's highly competitive market, organizations face significant challenges in accurately understanding and segmenting customer behavior due to the inherently dynamic and evolving nature of customer interactions over time. Traditional customer segmentation methods often neglect these temporal variations, leading to ineffective business strategies and missed opportunities. This research addresses this critical gap by introducing an innovative time series-based approach for customer behavior segmentation. By modeling each customer's behavior as a time series capturing key metrics such as purchase frequency, transaction amounts, and customer lifecycle costs the proposed method dynamically adapts to behavioral changes over time. To enhance segmentation precision, a genetic algorithm is employed to optimize feature weights, ensuring that the most relevant factors are emphasized. These optimized features are then clustered using spectral clustering to identify distinct and meaningful customer segments. The effectiveness of the proposed method is validated using 30 months of transactional data from a payment services company. The results demonstrate that the proposed approach, particularly when combined with spectral clustering and optimally weighted features, significantly surpassing the performance of traditional static segmentation techniques. This research not only provides a more accurate framework for uncovering hidden patterns in customer behavior but also delivers actionable insights for targeted marketing and personalized customer strategies.http://www.sciencedirect.com/science/article/pii/S2451958824001532Dynamic segmentationFeature optimizationGenetic algorithmTime series analysisClustering techniquesCustomer behavior analysis |
| spellingShingle | Hodjat (Hojatollah) Hamidi Bahare Haghi An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior Computers in Human Behavior Reports Dynamic segmentation Feature optimization Genetic algorithm Time series analysis Clustering techniques Customer behavior analysis |
| title | An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior |
| title_full | An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior |
| title_fullStr | An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior |
| title_full_unstemmed | An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior |
| title_short | An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior |
| title_sort | approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior |
| topic | Dynamic segmentation Feature optimization Genetic algorithm Time series analysis Clustering techniques Customer behavior analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2451958824001532 |
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