  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.
