Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 7, June 2019

Predicting Customer Call Intent by Analyzing Phone Call Transcripts Based on
CNN for Multi-Class Classification


Junmei Zhong and William Li, Marchex Inc, USA


Auto dealerships receive thousands of calls daily from customers interested in sales, service, vendors and jobseekers. With so many calls, it is very important for auto dealers tounderst and the intent of these calls to provide positive customer experiences that ensure customer satisfaction, deeper 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 or 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 tests that measure the model’s quantitative metrics of F1-Score, precision, recall, and accuracy.


Word Embeddings, Machine Learning, Deep Learning, Convolutional Neural Networks, Artificial Intelligence, Auto Dealership Industry, Customer Call Intent Prediction.