Leveraging Deep Learning for Customer Segmentation: Patterns and Preferences Unveiled - Navneet Kumar, Chitralekha; Vasant Mahadik, Ramchandra; Paliwal, Sangeeta; Sajanapwar, Pallavi; B. Kasar, Chaitali; Chordiya Shingvi, Manisha Tejas (International Journal of Intelligent Systems and Applications in Engineering, 2024)
It is crucial to comprehend and accommodate consumers’ varied preferences and behaviours in the dynamic world of modern business. The revolutionary effects of deep learning on consumer segmentation are examined in this study, which also provides a thorough overview of the approaches, techniques, results, constraints, and potential future applications of this developing area.In the past, client segmentation depended on simple clustering methods and fundamental demographic characteristics. However, the development of deep learning has ushered in a new era by allowing businesses to explore complex patterns and preferences concealed within sizable and unstructured datasets. In order to put the deep learning revolution in historical perspective, our inquiry starts with an examination of conventional and machine learning-based segmentation techniques. We explore the possibilities of neural embeddings, RNNs, and unsupervised learning, emphasising their efficiency in simulating consumer preferences and behaviour. In the context of consumer segmentation, we also look at the possibility of deep reinforcement learning, hybrid methods, and transfer learning.Even while deep learning has a lot of potential, it is not without difficulties, such as computing demands and sensitivity to data noise. However, its scope is broad, encompassing anything from dynamic behaviour modelling to image-based segmentation. This paper gives businesses the knowledge they need to use deep learning to uncover complex patterns and preferences within their customer base, ultimately fostering more individualised and successful marketing strategies. Businesses are increasingly looking for data-driven insights to gain a competitive edge.