UNICON: A unified framework for behavior-based consumer segmentation in e-commerce

• URL: http://arxiv.org/abs/2309.13068v1
• Date: Mon, 18 Sep 2023 14:58:13 GMT
• Title: UNICON: A unified framework for behavior-based consumer segmentation in e-commerce
• Authors: Manuel Dibak, Vladimir Vlasov, Nour Karessli, Darya Dedik, Egor Malykh, Jacek Wasilewski, Ton Torres, Ana Peleteiro Ramallo
• Abstract summary: Group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment. We introduce UNICON, a unified deep learning consumer segmentation framework. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments.
• Score: 0.9213852038999552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven personalization is a key practice in fashion e-commerce, improving the way businesses serve their consumers needs with more relevant content. While hyper-personalization offers highly targeted experiences to each consumer, it requires a significant amount of private data to create an individualized journey. To alleviate this, group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment, while still being able to personalize the results. We introduce UNICON, a unified deep learning consumer segmentation framework that leverages rich consumer behavior data to learn long-term latent representations and utilizes them to extract two pivotal types of segmentation catering various personalization use-cases: lookalike, expanding a predefined target seed segment with consumers of similar behavior, and data-driven, revealing non-obvious consumer segments with similar affinities. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments. Furthermore, we present experiments that showcase how segment information can be incorporated in a hybrid recommender system combining hyper and group-based personalization to exploit the advantages of both alternatives and provide improvements on consumer experience.

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