Voice AI sits at the intersection of speech analytics and quality management, using cutting edge speech technology and natural language processing to transcribe and analyze support calls at a massive scale. It enables organizations to analyze 100% of customer conversations with the ultimate goal of improving agent performance and the overall customer experience.
With Voice AI, key moments in conversation can be unearthed to provide a more accurate picture of how the contact centers as a whole, and the individual agents staffing them, are performing across key metrics. Analytics on interactions like sentiment, emotion, dead air, hold times, supervisor escalations, redaction, and more are often game-changing for businesses who previously had low QA coverage, and Voice AI is the key to identifying them.
Once transcribed and analyzed, Voice AI automatically scores some parts of conversations and enables organizations to create tailored coaching programs for agents.
Voice AI emerged as a result of the inefficiencies of highly manual traditional quality management (QM) programs. Organizations struggled to fully-understand performance, monitor mission-critical KPIs and compliance, and better enable their agents with relevant training.
Voice AI, built around Analytics-enabled Quality Management, radically transforms an organization’s quality programs in a number of ways:
Before Voice AI
- QA is Manual Low Call Coverage: Quality checks take 30+ minutes per call, analysts use lengthy checklists, and scoring is subjective and calls selected at random.
- Transcription Inaccurate and Simple: Accents, overtalk, industry specific terms, and spotty connectivity make speech-to-text transcription challenging.
- Lack of Benchmarking: Performance is assessed from a few scored calls and minimal benchmarks across the organization.
- One Size Fits All Training: Blanket, one size fits all trainings might not be relevant to groups of agents. Minimal data available to create targeted, impactful coaching programs.
- Low Visibility Across the Organization: Nearly impossible to monitor organization-wide performance and monitor progress.
After Voice AI
- ⏰ Automates the Tedious Parts of QA: Analyze and score 100% of calls for every agent and identify benchmarks and spot performance trends.
- 🎯 Improved Transcription Accuracy: Accurate transcription (80+%) at scale delivers full view of performance and insights with confidence.
- 👁️ Comprehensive Evaluations: Get a full view into every voice call for every agent and enable supervisors to spot trends and identify critical areas of improvement.
- 😀 More Targeted Feedback/Coaching to Agents: Provide more targeted feedback to agents and use personal scorecards as reference points for more relevant training.
- 📊 Better Performance Analytics and Trends: Operation leaders can identify inefficiencies and trends and improve key metrics with data-driven training.
”Success for our team means bringing out the best in each agent. We’re able to do that by throwing out the one size fits all coaching approach and tailoring conversations on an individual basis. Voice AI helps ensure you’re an optimized leader by identifying and addressing the right gaps.”
– Kyle Kizer, Compliance Manager at Root Insurance
Voice AI provides a wide variety of benefits to improve processes across a contact center. Next, we’ll dig into some real-world use cases of how Voice AI and quality automation is used today.
Regulatory compliance is paramount across all industries, most notably financial, insurance, and healthcare. It ensures the protection of customer data, backed by strict legislation to enforce it. As a result, monitoring mandatory compliance dialogues and categorizing voice calls relevant to specific compliance regulations is mission-critical.
Examples
- Mini Miranda
- Settlement Disclosure
- Recorded Line Message
- PII Redaction (eg. credit card, account number, SSN)
- Customer verification %
- Mandatory compliance dialogue %
Measurable KPIs
- Customer verification %
- Mandatory compliance dialogue %
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