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In today's digital age, customer segmentation has become a key element of marketing strategy. With the popularity of the Internet and the rapid development of digital technology, enterprises can collect and analyze large-scale customer data, thus providing the opportunity to accurately understand customer needs and behaviors (Yolcu et al., 2020). However, traditional customer segmentation methods are often limited by the effectiveness of feature extraction and the complexity of the model, making it difficult to handle large-scale and high-dimensional data and, thus, limiting the implementation of personalized marketing strategies (Shao & Kim, 2020). The emergence of deep learning technology provides new ways to solve this problem. Deep learning has demonstrated amazing capabilities in areas such as image recognition and natural language processing (Alves Gomes & Meisen, 2023). Its powerful feature extraction and pattern recognition capabilities provide new tools and methods for customer segmentation and are expected to achieve more precise and personalized marketing strategies (Joung & Kim, 2023).
Researchers have made a series of important progress in applying deep learning technology to customer segmentation (Sarkar & De Bruyn, 2021). For example, they can use recurrent neural networks (RNN) to model customers' temporal behavior, including the time they visit a website, the order in which pages are viewed, and their purchase history (Koehn et al., 2020). This helps capture customers’ dynamic behavioral characteristics to better understand their interests and purchasing intentions. 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. Issues such as model optimization, data processing, feature selection, and hyperparameter adjustment still require more research and practice.
In marketing, the nascent emergence of deep learning technology is changing our understanding of customer segmentation. Deep learning models, such as neural networks, recurrent neural networks (RNN), and convolutional neural networks (CNN), have powerful pattern recognition and feature extraction capabilities, which makes them ideal tools for customer segmentation (Alkhayrat et al., 2020). Through deep learning, companies can better understand customer behavior, needs, and preferences and thereby develop more targeted marketing strategies. Customer segmentation is a key marketing strategy that aims to divide customers into different segments for more effective personalized marketing. The difficulty with customer segmentation lies in the diversity and uncertainty of customers (Nosratabadi et al., 2020). Customer needs and behaviors may change over time and context, and traditional statistical methods and shallow machine learning models struggle to capture this complexity. Deep learning technology has powerful nonlinear modeling capabilities and can process large-scale, high-dimensional customer data and extract useful features from it. However, the complexity of deep learning models also presents challenges since they require meticulous model optimization and parameter tuning to obtain the best customer segmentation results (Ullah et al., 2023). This article will explore how to effectively use deep learning technology to finely segment customers.
Existing research on customer behavior classification has mainly focused on the application of some classic traditional methods, especially in considering individual differences and environmental changes, and will combine some traditional methods and technologies based on traditional machine learning. Such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), etc.