Customer segmentation

Dannenberg, Holger; Zupancic, Dirk

doi:10.1007/978-3-8349-8782-2_7

Cited by 2 publications

(2 citation statements)

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“…An important technique in CRM is customer clustering, also called customer segmentation. Customer segmentation aims at dividing customers into distinct and homogeneous subgroups according to appropriate criteria (Dannenberg and Zupancic, 2009;Tsiptsis and Chorianopoulos, 2011). Clustering helps platforms learn more about merchants' behavior and develop differentiated management strategies.…”

Section: Introductionmentioning

confidence: 99%

A Wasserstein distance-based spectral clustering method for transaction data analysis

Zhu¹,

Huang²,

Li³

et al. 2022

Preprint

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With the rapid development of online payment platforms, it is now possible to record massive transaction data. The economic behaviors are embedded in the transaction data for merchants using these platforms. Therefore, clustering on transaction data significantly contributes to analyzing merchants' behavior patterns. This may help the platforms provide differentiated services or implement risk management strategies. However, traditional methods exploit transactions by generating lowdimensional features, leading to inevitable information loss. To fully use the transaction data, we utilize the empirical cumulative distribution of transaction amount to characterize merchants. Then, we adopt Wasserstein distance to measure the dissimilarity between any two merchants and propose the Wasserstein-distance-based spectral clustering (WSC) approach. Considering the sample imbalance in real datasets, we incorporate indices based on local outlier factors to improve the clustering performance. Furthermore, to ensure feasibility if the proposed method on large-scale datasets with limited computational resources, we propose a subsampling version of WSC. The associated theoretical properties are investigated to verify the efficiency of the proposed approach. The simulations and empirical study demonstrate that the proposed method outperforms feature-based methods in finding behavior patterns of merchants.

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Section: Introductionmentioning

confidence: 99%

A Wasserstein distance-based spectral clustering method for transaction data analysis

Zhu¹,

Huang²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This is because such clustering enables the platforms to learn more about their customers and then, to implement differentiated strategies effectively. The objective of customer segmentation is to divide customers into distinct and homogeneous subgroups according to appropriate criteria (Dannenberg & Zupancic, 2009;Tsiptsis & Chorianopoulos, 2011). However, useful information for customer segmentation, such as the balance sheet and profit statement, is difficult to collect for merchants on third-party payment platforms, as most of these merchants are small businesses (Devins et al, 2005;Storey, 2016).…”

Section: Introductionmentioning

confidence: 99%

Clustering Based on Kolmogorov–Smirnov Statistic with Application to Bank Card Transaction Data

Zhu

Deng

Huang

et al. 2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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Rapid developments in third-party online payment platforms now make it possible to record massive bank card transaction data. Clustering on such transaction data is of great importance for the analysis of merchant behaviours. However, traditional methods based on generated features inevitably lead to much loss of information. To make better use of bank card transaction data, this study investigates the possibility of using the empirical cumulative distribution of transaction amounts. As the distance between two merchants can be measured using the twosample Kolmogorov-Smirnov test statistic, we propose the Kolmogorov-Smirnov K-means clustering approach based on this distance measure. An approximation step is conducted to ensure the feasibility of the proposed method even for large-scale transaction data, and the associated theoretical properties are investigated. Both simulations and an empirical study demonstrate that our method outperforms feature-based methods and is computationally efficient for large-scale data sets.

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Customer segmentation

Cited by 2 publications

References 0 publications

A Wasserstein distance-based spectral clustering method for transaction data analysis

A Wasserstein distance-based spectral clustering method for transaction data analysis

Clustering Based on Kolmogorov–Smirnov Statistic with Application to Bank Card Transaction Data

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