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Data-driven personalization is a key practice in fashion e-commerce,improving the way businesses serve their consumers needs with more relevantcontent. While hyper-personalization offers highly targeted experiences to eachconsumer, it requires a significant amount of private data to create anindividualized journey. To alleviate this, group-based personalization providesa moderate level of personalization built on broader common preferences of aconsumer segment, while still being able to personalize the results. Weintroduce UNICON, a unified deep learning consumer segmentation framework thatleverages rich consumer behavior data to learn long-term latent representationsand utilizes them to extract two pivotal types of segmentation catering variouspersonalization use-cases: lookalike, expanding a predefined target seedsegment with consumers of similar behavior, and data-driven, revealingnon-obvious consumer segments with similar affinities. We demonstrate throughextensive experimentation our framework effectiveness in fashion to identifylookalike Designer audience and data-driven style segments. Furthermore, wepresent experiments that showcase how segment information can be incorporatedin a hybrid recommender system combining hyper and group-based personalizationto exploit the advantages of both alternatives and provide improvements onconsumer experience.

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