ET-USB: Transformer-Based Sequential Behavior Modeling for Inbound Customer Service
This work addresses inbound call prediction for customer service at Cathay Financial Holdings, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled inbound call prediction for customer service by developing ET-USB, a Transformer-based model that incorporates sequential and nonsequential user features, and reported that it delivers superior results compared to other deep-learning models.
Deep learning models with attention mechanisms have achieved exceptional results for many tasks, including language tasks and recommendation systems. Whereas previous studies have emphasized allocation of phone agents, we focused on inbound call prediction for customer service. A common method of analyzing user history behaviors is to extract all types of aggregated feature over time, but that method may fail to detect users' behavioral sequences. Therefore, we created a new approach, ET-USB, that incorporates users' sequential and nonsequential features; we apply the powerful Transformer encoder, a self-attention network model, to capture the information underlying user behavior sequences. ET-USB is helpful in various business scenarios at Cathay Financial Holdings. We conducted experiments to test the proposed network structure's ability to process various dimensions of behavior data; the results suggest that ET-USB delivers results superior to those of delivered by other deep-learning models.