Segmentation of Shopping Mall Customers Using Clustering

Deepa, D.; Sivasangari, A.; Vignesh, R.; N, Priyanka; Antony, J. Cruz; GowriManohari, V.

doi:10.1007/978-981-19-6004-8_48

Cited by 3 publications

(1 citation statement)

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“…4) Mini-batch K-means: Mini-batch k-means is like an alternative version of k-means algorithm because the working steps are quite similar but mini-batch k-means is designed for reduce the computational time [26] and use a random mini batch from the dataset to update the clusters [27]. Here are the steps:…”

Section: Cluster Analysismentioning

confidence: 99%

Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms

Antonius,

Fitrianah

2024

IJACSA

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In this digital era, the use of e-commerce has expanded and is widely adopted by society. One of the reasons why people use e-commerce platforms is because of their convenience and ease of use. However, the rapid growth of ecommerce has led to a substantial rise in transactions within the platform, involving various business entities. Therefore, it is crucial to perform customer segmentation to group them based on their purchasing behavior. The implementation of data mining techniques, such as clustering, is highly beneficial in this case. Clustering helps process datasets and transform them into useful information. In this study, transaction data obtained from one of the e-commerce stores, i.e. MurahJaya888 and followed by analysis using various clustering methods such as K-means, Kmedoids, Fuzzy c-means, and Mini-batch k-means. We also proposed a new model that will become the attributes cluster, namely, RFM + DP (Discount Proportion). The Discount Proportion Rate will provide more insights for customer segmentation as it helps understand purchasing behavior that is more responsive to discount utilization. Implementing these four clustering methods with RFM + DP model resulted in four clusters based on the optimal elbow method. Furthermore, the evaluation and performance metrics for each clustering algorithm indicate that Mini Batch K-Means achieved the highest silhouette score of 0.50. Meanwhile, K-Means obtained the highest CH index value compared to the other algorithms, which was 1056.

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Section: Cluster Analysismentioning

confidence: 99%