  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.
