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Mining the Minds of Customers from Online Chat Logs

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arxiv 1510.01801 v2 pith:O3RRGMLF submitted 2015-10-07 cs.CY cs.SI

Mining the Minds of Customers from Online Chat Logs

Kunwoo Park , Jaewoo Kim , Jaram Park , Meeyoung Cha , Jiin Nam , Seunghyun Yoon , Eunhee Rhim This is my paper
classification cs.CY cs.SI
keywords satisfactionchatcustomeronlineservicecustomersdataexperience
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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