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- W3102316376 abstract "Segmenting customers based on their preferences is a fundamental marketing problem. The current practice for segmentation relies on estimating the parameters of a mixture model using Expectation-Maximization (EM) based methodologies. The EM-based methodologies are not consistent, are sensitive to initialization, and exhibit slow numerical convergence. To address these issues, we propose a data-driven segmentation methodology that directly segments customers, without the need for estimating latent model parameters. Our approach relies on a 'model-based' projection technique, inspired by the recent projection-based techniques developed in the machine learning community for learning mixture models. Our approach is versatile (broadly applicable) and flexible (allows for incorporation of problem-specific structure). Theoretically, we derive separation conditions under standard assumptions that are required to guarantee asymptotic recovery of the underlying segments. Empirically, we demonstrate using synthetic data that our algorithm is fast, robust to model misspecification, and consistently outperforms popular benchmarks. Using real-world data, we characterize the segments recovered by our algorithm and demonstrate an automatic feature selection mechanism, which results in sharper separation of the segments than existing benchmarks." @default.
- W3102316376 created "2020-11-23" @default.
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- W3102316376 date "2015-01-01" @default.
- W3102316376 modified "2023-10-18" @default.
- W3102316376 title "A Data-Driven Approach to Customer Segmentation" @default.
- W3102316376 doi "https://doi.org/10.2139/ssrn.2696161" @default.
- W3102316376 hasPublicationYear "2015" @default.
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