English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

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