Customer Conversations
Interaction data is captured across the contact center.
DFCU Case Study
How DFCU Transformed Manual QA Into Automated Conversation Intelligence
Learn how Dort Financial Credit Union expanded QA coverage, reduced manual effort, and created actionable coaching intelligence with automated scorecards, calibration, and analyst-supported workflows.
Download the case study Complimentary PDF · Practical insights for regulated contact centers
~15,000Calls monitored through automated scoring and analyst-supported QA workflows
$32,718Annualized labor savings under DFCU's current sampled QA policy
BroaderCoverage, moving beyond limited manual sampling
Jul–Dec2025 reporting period represented in the case study
The Challenge
DFCU Had a QA Process, But It Was Difficult to Scale
DFCU's quality assurance program depended on manual listening and scoring. Each monitored call required more than 30 minutes of senior team effort, limiting coverage to approximately four calls per agent per month.
That sampling produced real findings—but not a reliable picture of what was happening across the calls the team never reached.
Before ZenyliticsAfter Zenylitics
Manual call reviewAutomated scorecards
Limited samplesBroader call coverage
Reviewer-dependent scoringCalibrated scoring logic
Reactive coachingData-backed coaching
The Solution
From Raw Conversations to Coaching and Governance Intelligence
Zenylitics combined the CallMiner conversation analytics platform, automated scorecards, analyst validation, calibration feedback, guided insights, and Team-as-a-Service support into one structured workflow.
Conversation Analytics
The platform processes calls at scale and surfaces signals.
Automated Scorecards
Consistent scoring logic is applied across interaction volume.
Analyst Validation
Zenylitics calibrates findings to DFCU's real environment.
Guided Insights
Supervisors receive findings ready for coaching and decisions.
The Impact
More Coverage. Less Manual Effort.
The engagement delivered operational value without adding internal QA headcount or leaving the team with another dashboard to interpret.
Annualized Labor Savings
Under DFCU's existing sampled QA policy. This is current-policy labor savings, not a projected full-coverage benchmark.
Calls Monitored
Through automated scoring and analyst-supported quality workflows.
Improved QA Efficiency
Supervisors spent less time manually reviewing calls and more time using structured findings for coaching.
Better Operational Visibility
Teams identified call drivers, silence metrics, avoidance signals, recap issues, and investigative-language opportunities.
Operational Change 01 02 03 04
What Changed After Implementing Conversation Analytics
The program gave DFCU a repeatable way to turn a broader set of conversations into consistent action.
QA Automation
Manual listening, documenting, and scoring no longer served as the primary QA process.
Consistent Scorecards
Calibration improved scoring consistency, category accuracy, and reporting reliability.
Specific Coaching
Transcripts and scores focused coaching on handle time, upsell behavior, recap language, and call consistency.
Risk Visibility
The program supported UDAAP reporting, compliance review, and documentation of member service issues.
Inside the Study
See the Complete DFCU Story
- How DFCU moved beyond manual QA sampling
- How automated scorecards expanded monitoring coverage
- How calibration improved scoring consistency
- How analyst-supported insights improved coaching
- How conversation analytics supported governance and compliance
Why Zenylitics
Finished Intelligence, Not Raw Platform Output
Zenylitics combines conversation analytics technology, analyst expertise, scorecard development, calibration, and guided insights. Financial institutions get a process that turns conversations into measurable operational improvement—not simply a wider dashboard to interpret on their own.
Take the Next Step
See How Financial Contact Centers Turn Conversations Into Actionable Intelligence
Download the DFCU case study for a closer look at the workflow, measured results, and scalable QA model.