Abstract
The advent of competitors in the industry has formed gravity among competing corporations regarding the search for new consumers and retaining existing ones. As a result, the need for innovative consumer retention and attraction tactics is significant regardless of the business size. The efficient way is to segment consumers based on some particular behavior. Market segmentation means clustering consumers into a few segments where consumers of each segment share common behavior or characteristics. Segmentation permits diverse business or commercial firms to direct and choose various groups of customers according to their characteristics and behavior in purchasing goods, items, or interests in a specific merchandise. In addition, with the segmentation approach, the capability of any corporate firm to understand the needs of each of its customers will be significantly enhanced in the provision of targeted customer services and the development of customized customer marketing plans. Big data ecosystem and machine learning/deep learning create appropriate ways to improve better recognition and segmentation of consumers. The statistical-based approach needs to be updated and often leads to incorrect results. In this chapter, we address the problem of segmenting the customers. We provide an in-depth understanding of various unsupervised machine learning algorithms such as K-Means, K-Means++, Principal Component Analysis, and unsupervised deep learning algorithms such as AutoEncoders. Next, we implement customer segmentation by proposing a learning-based framework on the open-source dataset. This dataset is available on Kaggle. The proposed framework uses K-Means clustering and AutoEncoder to segment the customers. We optimize hyperparameters for these algorithms using advanced deep learning frameworks like TensorFlow and popular machine learning frameworks like sci-kit-learn. The elbow method is used to find the optimal number of segments. It can also be extended to cluster the groups in diverse businesses using semantic features, temporal affinity, and sentiment analysis.
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Soni, J., Prabakar, N., Upadhyay, H. (2023). Deep Learning-Based Efficient Customer Segmentation for Online Retail Businesses. In: Yang, XS. (eds) Benchmarks and Hybrid Algorithms in Optimization and Applications. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3970-1_9
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DOI: https://doi.org/10.1007/978-981-99-3970-1_9
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