Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods

Saxena, Arpit; Agarwal, Ashi; Pandey, Binay Kumar; Pandey, Digvijay

doi:10.1007/s43615-023-00336-4

Cited by 93 publications

(1 citation statement)

References 36 publications

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“…In addition, the deep learning model can also process customer attribute information, such as age, gender, and geographical location, as well as tabular data, such as purchase history. Structures like the TabNet model have been widely used for feature selection and modeling of these data types, allowing us to better understand the impact of customer characteristics and attributes on market behavior (Saxena et al, 2024). However, although deep learning has made significant progress in customer segmentation, many challenges remain.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Customer Online Shopping Behavior Analysis and Personalized Marketing Strategy

Li,

Meng,

Tian

et al. 2024

Journal of Organizational and End User Computing

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In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. Experimental results show that this model has achieved excellent performance on each data set, has higher accuracy and AUC value than the baseline method, and has advantages over other control models in comparative experiments. This research provides solid support for the implementation of personalized marketing strategies.

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Section: Introductionmentioning

confidence: 99%