Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm

Wu, Zengyuan; Jin, Lingmin; Zhao, Jiali; Jing, Lizheng; Chen, Liang

doi:10.1155/2022/9930613

Cited by 14 publications

(4 citation statements)

References 32 publications

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“…Such a goal coincides with the potential use of clustering to create groups of customers to be served with a specific UI. Undoubtedly, K-means is an algorithm with many advantages, but it should be noted that within the framework of the aforementioned study, modifications of this approach, such as K-medoids (partitioning around medoids-PAM [39]) and K-medians, were also understood under the term K-means. It is worth noting that despite their similarities, these K-.…”

Section: Clustering Of Usersmentioning

confidence: 99%

Functional Framework for Multivariant E-Commerce User Interfaces

Wasilewski

2024

JTAER

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Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.

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Section: Clustering Of Usersmentioning

confidence: 99%

Functional Framework for Multivariant E-Commerce User Interfaces

Wasilewski

2024

JTAER

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“…However, its computational difficulty is influenced by the size of the dataset, the number of clusters, and the initialization of the cluster centroids. K-medoids (uses the medoid instead of the mean) was used for e-commerce customer segmentation [21]. Classification method of e-commerce user behavior based on Fuzzy C-Means Clustering was proposed to improve the clustering analysis effect of e-commerce user behavior in [20].…”

Section: Related Workmentioning

confidence: 99%

Clusterization methods for multi-variant e-commerce interfaces

Wasilewski

2023

Annals of Computer Science and Information Systems

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E-commerce is a very popular method that lets consumers to purchase goods and services. The ability to purchase items online has increased the need for effective recommendation systems. Such recommendations usually relete to products in which the customer may be interested. However, there are wider opportunities to tailor e-commerce to individual customer needs and behaviour. In this paper, the architecture of the e-commerce platform (named AIM 2 ), which allows providing a dedicated interface to selected user groups, is discussed. A key component of the platform is the module responsible for dividing customers into groups, using selected clustering methods. Each of the implemented methods can be parameterised to adapt the customer segmentation to a given e-commerce business owner's requirements. This article describes the results of an analysis of the impact of selected methods and parameters on clustering results. Moreover, it identifies key metrics that should be considered when selecting clustering conditions during the implementation of the platform. Finally, the main results of the pilot implementation of AIM 2 are presented to assess the effectiveness of the multi-variant user interface.

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“…Analysis results demonstrate the importance of the demographics factor when merged with the RFM model. In 2022, for the dataset of the e-commerce platform, the extended RFM model with two variables, C and V, was proposed (Wu et al, 2022). Variable C represents the Frequency of customers adding items to their shopping carts, and variable V refers to the Frequency of customers adding items to their favourites list.…”

Section: Source: Authors' Summarymentioning

confidence: 99%

An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

Ho,

Nguyen,

Nguyen

et al. 2023

Business Systems Research Journal

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Background Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas. Motivations With the rapid increase of transactional data, the RFM model can accurately segment customers and provide deeper insights into customers’ purchasing behaviour. However, the traditional RFM model is limited to 3 variables, Recency, Frequency and Monetary, without revealing segments based on demographic features. Meanwhile, the contribution of demographic characteristics to marketing strategies is extremely important. Methods/Approach The article proposed an extended RFMD model (D-Demographic) with a combination of behavioural and demographic variables. Customer segmentation can be performed effectively using the RFMD model, K-Means, and K-Prototype algorithms. Results The extended model is applied to the retail dataset, and the experimental result shows 5 clusters with different features. The effectiveness of the new model is measured by the Adjusted Rand Index and Adjusted Mutual Information. Furthermore, we use Cohort analysis to analyse customer retention rates and recommend marketing strategies for each segment. Conclusions According to the evaluation, the proposed RMFD model was deployed with stable results created by two clustering algorithms. Businesses can apply this model to deeply understand customer behaviour with their demographics and launch efficient campaigns.

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Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm

Cited by 14 publications

References 32 publications

Functional Framework for Multivariant E-Commerce User Interfaces

Functional Framework for Multivariant E-Commerce User Interfaces

Clusterization methods for multi-variant e-commerce interfaces

An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

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