Exploring 360-Degree View of Customers for Lookalike Modeling
This work addresses customer targeting challenges in advertising for e-commerce and travel companies, but it appears incremental as it builds on existing lookalike modeling approaches.
The paper tackles the problem of improving customer targeting for Rakuten Group, Inc. by proposing a novel framework that unifies diverse customer behaviors and features to build a lookalike model, with extensive experiments on real e-commerce and travel datasets demonstrating its effectiveness.
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.