  For agents at a contact centre receiving calls, the most important piece of
information is the reason for a given call. An agent cannot provide support on
a call if they do not know why a customer is calling. In this paper we describe
our implementation of a commercial system to detect Purpose of Call statements
in English business call transcripts in real time. We present a detailed
analysis of types of Purpose of Call statements and language patterns related
to them, discuss an approach to collect rich training data by bootstrapping
from a set of rules to a neural model, and describe a hybrid model which
consists of a transformer-based classifier and a set of rules by leveraging
insights from the analysis of call transcripts. The model achieved 88.6 F1 on
average in various types of business calls when tested on real life data and
has low inference time. We reflect on the challenges and design decisions when
developing and deploying the system.
