Mining the Minds of Customers from Online Chat Logs

TL;DR

This paper analyzes over 170,000 online chat sessions to identify key factors influencing customer satisfaction, highlighting sentiment as the most predictive element and offering insights to improve customer service quality.

Contribution

It introduces a large-scale analysis of chat logs to determine the primary predictors of customer satisfaction, emphasizing sentiment analysis over traditional session metadata.

Findings

Sentiment is the most predictive factor of satisfaction.

Session length and response time have weaker correlations.

Predictive insights can help improve customer service quality.

Abstract

This study investigates factors that may determine satisfaction in customer service operations. We utilized more than 170,000 online chat sessions between customers and agents to identify characteristics of chat sessions that incurred dissatisfying experience. Quantitative data analysis suggests that sentiments or moods conveyed in online conversation are the most predictive factor of perceived satisfaction. Conversely, other session related meta data (such as that length, time of day, and response time) has a weaker correlation with user satisfaction. Knowing in advance what can predict satisfaction allows customer service staffs to identify potential weaknesses and improve the quality of service for better customer experience.