Advanced Text Analytics
for Call Center Applications
Improve agent productivity through better caller insight. Rosette analyzes the text in your call center to uncover information that improves caller interaction, reducing average handle time and improving customer satisfaction.
- No more duplicate records: connect caller identity to caller history using machine-learned phonetic name matching across 15 languages.
- Know how they feel: discover customer sentiment from comments left in your help desk system and social media sources such as Twitter.
- Designed to integrate into call center applications, on premise or in the cloud.
12 Ways Rosette Matches Callers to Their History
- Phonetic similarity
Jesus ↔ Heyzeus ↔ Haezoos
- Different spellings
Abdul Rasheed ↔ Abdulrashid
William ↔ Will ↔ Bill ↔ Billy
J. E. Smith ↔ James Earl Smith
- Titles and honorifics
Dr. ↔ Mr. ↔ Ph.D.
- Out-of-order name components
Diaz, Carlos Alfonzo ↔ Carlos Alfonzo Diaz
- Missing name components
Phillip Charles Carr ↔ Phillip Carr
- Truncated name components
McDonalds = McDonald ↔ McD
- Missing spaces or hyphens
MaryEllen ↔ Mary Ellen ↔ Mary-Ellen
- Names split inconsistently across database fields
Dick . Van Dyke ↔ Dick Van . Dyke
- Same name in multiple languages (15 supported)
Mao Zedong ↔ Мао Дзэ-дун ↔ 毛泽东 ↔ 毛沢東
- Date of birth
Dec 15, 1984 ↔ 12/15/84
Support Machines is a self-service customer support platform that helps customers find answers to their questions on the web, in mobile applications, and via social networks. With the help of Rosette, the platform provides answers to questions based on the background context of the conversations. Answers flow to the customer in a meaningful way, for example, offering instructions or conveying information from general to specific.