Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach
Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2020.1865598 |
| _version_ | 1797706900972765184 |
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| author | Arthit Apichottanakul Masayuki Goto Kullaprapruk Piewthongngam Supachai Pathumnakul |
| author_facet | Arthit Apichottanakul Masayuki Goto Kullaprapruk Piewthongngam Supachai Pathumnakul |
| author_sort | Arthit Apichottanakul |
| collection | DOAJ |
| description | Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer loyalty and contribution that can only provide a single lump-sum value of the recency (R), frequency (F), and monetary value (M); hence, it discards information regarding customers’ product preferences. Typically, different customers make different purchases. Subsequently, purchases are likely to be different across customers. This creates data sparsity, which affects the performance of conventional clustering methods. In this study, we integrated the group RFM analysis and probabilistic latent semantic analysis models to perform customer segmentation and customer analysis. The results indicated that the developed approach takes into account the product preference and provides insight into and captures a wide variety of the types of true ordering behaviour of the company’s customers. The information allows the manager to improve customer relationships and build a personalised purchasing management system for grouping customers with similar purchasing patterns. |
| first_indexed | 2024-03-12T05:58:14Z |
| format | Article |
| id | doaj.art-14073be7bc594d79a82cc74102b343cf |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T05:58:14Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-14073be7bc594d79a82cc74102b343cf2023-09-03T04:21:27ZengTaylor & Francis GroupCogent Engineering2331-19162021-01-018110.1080/23311916.2020.18655981865598Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approachArthit Apichottanakul0Masayuki Goto1Kullaprapruk Piewthongngam2Supachai Pathumnakul3Khon Kaen UniversityWaseda UniversityKhon Kaen UniversityKhon Kaen UniversityUnderstanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer loyalty and contribution that can only provide a single lump-sum value of the recency (R), frequency (F), and monetary value (M); hence, it discards information regarding customers’ product preferences. Typically, different customers make different purchases. Subsequently, purchases are likely to be different across customers. This creates data sparsity, which affects the performance of conventional clustering methods. In this study, we integrated the group RFM analysis and probabilistic latent semantic analysis models to perform customer segmentation and customer analysis. The results indicated that the developed approach takes into account the product preference and provides insight into and captures a wide variety of the types of true ordering behaviour of the company’s customers. The information allows the manager to improve customer relationships and build a personalised purchasing management system for grouping customers with similar purchasing patterns.http://dx.doi.org/10.1080/23311916.2020.1865598customer segmentationdata sparsitypork industrymulti-category products |
| spellingShingle | Arthit Apichottanakul Masayuki Goto Kullaprapruk Piewthongngam Supachai Pathumnakul Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach Cogent Engineering customer segmentation data sparsity pork industry multi-category products |
| title | Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach |
| title_full | Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach |
| title_fullStr | Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach |
| title_full_unstemmed | Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach |
| title_short | Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach |
| title_sort | customer behaviour analysis based on buying data sparsity for multi category products in pork industry a hybrid approach |
| topic | customer segmentation data sparsity pork industry multi-category products |
| url | http://dx.doi.org/10.1080/23311916.2020.1865598 |
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