Observation-Based Hybrid Classification Algorithms for Customer Segmentation
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Abstract
The customer segmentation model aims to cluster customers based on their specific characteristics. Relying solely on a simple classification algorithm may not yield optimal results. Our research proposes an Observation- Based Hybrid Classification (OBHC) algorithm to enhance the customer segmentation model by utilizing customer segmentation data from a public source. Observation-based clustering methods differ from simple classification or clustering models by being a hybrid system specifically engineered to boost the performance of predictive models. Furthermore, the focus is on evaluating metric values after clustering to demonstrate performance improvement. The experiments demonstrate signicant performance improvements across various classification algorithms. The most notable enhancement observed with the proposed algorithm is up to 43.86% on average accuracy score, 24.25% on average precision score, 20.25% on average recall score, and 32% on average F1-score, as shown in the experiment section. This research contributes by introducing a novel process for data scientists to tackle customer segmentation challenges, identifying higher performing segments that meet business needs, and providing executives with the exibility to adopt them. The research underscores the significance of employing hybrid models to classify customers better, providing valuable insights for advancing business development and improving customer service.
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