The empirical findings presented in the preceding chapters demonstrated that customer behavior in e-commerce can be effectively captured using a self-supervised learning approach that constructs a UCR from interaction data alone. The proposed UCR embeddings have been shown to be effective across diverse prediction tasks, including purchase prediction, churn estimation, and CTR forecasting, when tested on different datasets. Furthermore, the approach satisfied critical operational requirements, such as low inference latency, adaptability to heterogeneous data sources, and robustness under dynamic conditions.