Abstract
Customers are more vigilant, intelligent, and dynamic in society. They change their preferences and habits according to their needs. Knowing the needs of customers is an important part of marketing where a company should discover the loyal customers in this heterogeneity. The concept of dividing heterogeneity into homogeneous forms is termed as customer segmentation. Customer segmentation is an integral part of marketing where companies can easily develop relationships with customers with a huge set of customer data in an organized manner. Understanding the customer’s hidden knowledge is a resourceful idea of computational analysis where accurate information could be optimized for the taste and preference of the customer. This type of computational analysis is termed as data mining. This paper discussed on a systematic review of customer segmentation via data mining techniques. It is a systematic review of supervised, unsupervised and other data mining techniques used in segmentation.
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Das, S., Nayak, J. (2022). Customer Segmentation via Data Mining Techniques: State-of-the-Art Review. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_38
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