  Auto dealerships receive thousands of calls daily from customers who are
interested in sales, service, vendors and jobseekers. With so many calls, it is
very important for auto dealers to understand the intent of these calls to
provide positive customer experiences that ensure customer satisfaction, deep
customer engagement to boost sales and revenue, and optimum allocation of
agents or customer service representatives across the business. In this paper,
we define the problem of customer phone call intent as a multi-class
classification problem stemming from the large database of recorded phone call
transcripts. To solve this problem, we develop a convolutional neural network
(CNN)-based supervised learning model to classify the customer calls into four
intent categories: sales, service, vendor and jobseeker. Experimental results
show that with the thrust of our scalable data labeling method to provide
sufficient training data, the CNN-based predictive model performs very well on
long text classification according to the quantitative metrics of F1-Score,
precision, recall, and accuracy.
