Name
Abstract | ||
|---|---|---|
Prompt and knowledgeable responses to customers' emails are critical in maximizing customer satisfaction. Such emails often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They provide valuable feedback to improve contact center processes and customer care, as well as, to enhance customer retention. This paper describes a method for extracting salient features and identifying emotional emails in customer care. Salient features reflect customer frustration, dissatisfaction with the business, and threats to either leave, take legal action and/or report to authorities. Compared to a baseline system using word ngrams, our proposed approach with salient features resulted in a 20% absolute F-measure improvement. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1111/j.1467-8640.2012.00454.x | Computational Intelligence |
Keywords | Field | DocType |
boosting,support vector machines | Customer delight,Customer retention,Customer intelligence,Internet privacy,Customer satisfaction,Voice of the customer,Customer to customer,Computer science,Customer advocacy,Artificial intelligence,Natural language processing,Customer reference program | Journal |
Volume | Issue | ISSN |
29 | 3 | 0824-7935 |
Citations | PageRank | References |
6 | 0.54 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narendra K. Gupta | 1 | 182 | 55.47 |
| Mazin Gilbert | 2 | 62 | 7.31 |
| Giuseppe Di Fabbrizio | 3 | 330 | 44.45 |