Enhancing Customer Satisfaction Analysis with a Machine Learning Approach: From a Perspective of Matching Customer Comment and Agent Note

Abstract

With the booming of UGCs, customer comments are widely utilized in analyzing customer satisfaction. However, due to the characteristics of emotional expression, ambiguous semantics and short text, sentiment analysis with customer comments is easily biased and risky. This paper introduces another important UGC, i.e., agent notes, which not only effectively complements customer comment, but delivers professional details, which may enhance customer satisfaction analysis. Moreover, detecting the mismatch on aspects between these two UGCs may further help gain in-depth customer insights. This paper proposes a machine learning based matching analysis approach, namely CAMP, by which not only the semantics and sentiment in customer comments and agent notes can be sufficiently and comprehensively investigated, but the granular and fine-grained aspects could be detected. The CAMP approach can provide practical guidance for following-up service, and the automation can help speed-up service response, which essentially improves customer satisfaction and retains customer loyalty.