Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2806416.2806621 | ACM International Conference on Information and Knowledge Management |
Field | DocType | Volume |
Customer delight,Customer retention,Customer satisfaction,Internet privacy,Service quality,Advertising,Computer science,Customer to customer,Attitudinal analytics,Knowledge management,Customer advocacy,Online chat | Journal | abs/1510.01801 |
Citations | PageRank | References |
1 | 0.35 | 5 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kunwoo Park | 1 | 136 | 14.51 |
| Jaewoo Kim | 2 | 1 | 0.35 |
| Jaram Park | 3 | 55 | 4.26 |
| Meeyoung Cha | 4 | 4391 | 237.20 |
| Jiin Nam | 5 | 1 | 0.35 |
| Seung-hyun Yoon | 6 | 160 | 26.47 |
| Eunhee Rhim | 7 | 16 | 2.30 |