Study on Customer Segmentation Intelligent Model of Air Cargo

Yin, Fangyi; Li, Cheng; Liu, Congling

doi:10.1088/1755-1315/252/5/052037

Cited by 2 publications

(1 citation statement)

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“…As an illustration, the segmentation cashback website customers in the field of e-commerce was presented (Ballestar et al, 2018). Furthermore, air cargo customers by an intelligent model to maintain customers and increase profit margins were segmented (Yin et al, 2019). Moreover, customer loyalty in the field of local brand fashion by concentrating on customer segmentation was discussed (Dachyar et al, 2019).…”

Section: Customer Segmentationmentioning

confidence: 99%

A multi-attribute data mining model for rule extraction and service operations benchmarking

Mahdiraji

Tavana

Mahdiyani

et al. 2021

BIJ

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PurposeCustomer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.Design/methodology/approachThe authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.FindingsAs a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.Originality/valueThe authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

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Section: Customer Segmentationmentioning

confidence: 99%