Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification
This addresses the need for auto dealers to efficiently manage calls and allocate resources, though it is incremental as it applies an existing CNN method to a new domain-specific dataset.
The paper tackled the problem of classifying customer call intents from transcripts in auto dealerships using a CNN-based model, achieving high performance on metrics like F1-Score, precision, recall, and accuracy.
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.