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Customer segmentation is crucial for businesses to employ as a tool to guide them toward more successful marketing and product development. Customers can be divided into essentially infinite segments. Businesses need to have a deeper grasp of their customers' behaviour from all angles if they want to retain their business. Earlier segmentation techniques may have helped identify the client segments that need more attention. However, they were unable to spot a pattern in client attrition to take alternative measures. Finding patterns in customer behaviour, predicting consumer behaviour, and providing customers with better options and opportunities have become increasingly important for fostering customer-company engagement. It became crucial to divide up clients into groups based on their behaviours and personal information. The most effective and adaptable analytical frameworks and machine learning models for client segmentation are examined in this review paper.
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Stored only when both papers are in the corpus — 6 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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