Call center ai · Production

Fortune 500 Financial Services Firm Automates Customer Sentiment Index and Customer Effort Impact Using Verint Speech Analytics

The problem

A Fortune 500 financial services firm handling approximately nine million calls annually lacked an automated means to quantify customer sentiment and effort pain points, making it difficult to prioritize customer experience improvement initiatives.

Workflow diagram · grounded in source
1
Call ingestion trigger
trigger
“The firm's contact center agents handle approximately nine million calls annually.”
2
Speech analytics categorization
ai_action
“The company leveraged Verint Speech Analytics call categories, enhanced with specific key words and phrases, as the foundation for its automated CSi and CEi indices.”
3
CEi flagging for effort signals
ai_action
“the company assigned call categories to capture a subset of calls to align to its CEi where customers expressed confusion, evidence of misinformation, or were making repeat calls due to lack of resolution. When confusion, misinformation,…”
4
Trend reporting to stakeholders
output
“The company now has visibility into and the ability to monitor month-over-month trends in CSi by top call drivers.”
5
Agent development feedback loop
feedback_loop
“contact center managers can drill down to identify specific agent improvement opportunities”
Reported outcome

The firm now has automated, month-over-month visibility into customer sentiment and effort trends by call driver, and has translated those insights into process improvements — including modified agent scripts, web self-service offers, CRM authentication changes, and targeted training — to mitigate excessive average handle time and reduce repeat calls.

Reported metrics
Average call duration increase when confusion or repeat calls found60 percent
Annual calls handlednine million
Flags high-impact callsAutomatically flags calls that have the greatest bearing on understanding of customer sentiment and effort
Average handle timeMitigates excessive average handle time
Show all 5 reported metrics
average call duration increase when confusion or repeat calls found60 percent
annual calls handlednine million
flags high-impact callsAutomatically flags calls that have the greatest bearing on understanding of customer sentiment and effort
average handle timeMitigates excessive average handle time
repeat callsreduces repeat calls
Reported stack
Verint Speech AnalyticsCRM system
Source
https://www.verint.com/case-studies/fortune-500-financial-services-firm-automates-customer-sentiment-index-and-customer-effort-impact-using-verint-speech-analytics/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The firm now has automated, month-over-month visibility into customer sentiment and effort trends by call driver, and has translated those insights into process improvements — including modified agent scripts, web sel…

What tools did this team use?

Verint Speech Analytics, CRM system.

What results were reported?

Average call duration increase when confusion or repeat calls found: 60 percent; Annual calls handled: nine million; Flags high-impact calls: Automatically flags calls that have the greatest bearing on understanding of customer sentiment and effort; Average handle time: Mitigates excessive average handle time (source-reported, not independently verified).

How is this call center ai AI workflow structured?

Call ingestion trigger → Speech analytics categorization → CEi flagging for effort signals → Trend reporting to stakeholders → Agent development feedback loop.

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