Call center ai · Production
Bradesco Seguros elevates customer experience with Verint Speech Analytics and Verint Quality Bot
The problem
Bradesco Seguros needed to evaluate customer calls more efficiently and reduce heavy reliance on manual human intervention to achieve improved operational efficiency and cost reduction at scale.
Workflow diagram · grounded in source
1
Customer call received
trigger
“evaluate 100% of calls”
2
Speech and quality AI processing
ai_action
“verifying, processing, and validating words, terms, and phrases efficiently with speed and accuracy”
3
Automated evaluation output
output
“an automated system whereby Bradesco can evaluate 100% of calls with considerably less human intervention”
Reported outcome
Bradesco Seguros achieved a 9-point year-over-year increase in NPS, reduced product-related complaints by 55%, and achieved an approximate 70% reduction in resources allocated to standardized activities.
Reported metrics
NPS year-over-year increase9-point
Product-related complaints reduction55%
Resources allocated to standardized activities reductionapproximate 70%
Reported stack
Verint Speech AnalyticsVerint Quality Bot
Source
https://www.verint.com/case-studies/bradesco-seguros-elevates-customer-experience-with-verint/
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
Bradesco Seguros achieved a 9-point year-over-year increase in NPS, reduced product-related complaints by 55%, and achieved an approximate 70% reduction in resources allocated to standardized activities.
What tools did this team use?
Verint Speech Analytics, Verint Quality Bot.
What results were reported?
NPS year-over-year increase: 9-point; Product-related complaints reduction: 55%; Resources allocated to standardized activities reduction: approximate 70% (source-reported, not independently verified).
How is this call center ai AI workflow structured?
Customer call received → Speech and quality AI processing → Automated evaluation output.