Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace
Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/16/7/162 |
| _version_ | 1827671648713572352 |
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| author | Kenan Kafkas Ziya Nazım Perdahçı Mehmet Nafiz Aydın |
| author_facet | Kenan Kafkas Ziya Nazım Perdahçı Mehmet Nafiz Aydın |
| author_sort | Kenan Kafkas |
| collection | DOAJ |
| description | Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers’ purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation. |
| first_indexed | 2024-03-10T03:45:47Z |
| format | Article |
| id | doaj.art-4abf1afad8e44480a48f89e527918659 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-10T03:45:47Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-4abf1afad8e44480a48f89e5279186592023-11-23T09:09:32ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762021-10-011672965298010.3390/jtaer16070162Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online MarketplaceKenan Kafkas0Ziya Nazım Perdahçı1Mehmet Nafiz Aydın2Department of Management Information Systems, Kadir Has University, Istanbul 34083, TurkeyInformatics Department, Mimar Sinan Fine Arts University, Istanbul 34380, TurkeyDepartment of Management Information Systems, Kadir Has University, Istanbul 34083, TurkeyMarketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers’ purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation.https://www.mdpi.com/0718-1876/16/7/162market basket analysisco-purchase networkcommunity detectionSBMproduct segmentation |
| spellingShingle | Kenan Kafkas Ziya Nazım Perdahçı Mehmet Nafiz Aydın Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace Journal of Theoretical and Applied Electronic Commerce Research market basket analysis co-purchase network community detection SBM product segmentation |
| title | Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace |
| title_full | Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace |
| title_fullStr | Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace |
| title_full_unstemmed | Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace |
| title_short | Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace |
| title_sort | discovering customer purchase patterns in product communities an empirical study on co purchase behavior in an online marketplace |
| topic | market basket analysis co-purchase network community detection SBM product segmentation |
| url | https://www.mdpi.com/0718-1876/16/7/162 |
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