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What is contact center AI? Benefits and use cases for transforming quality management

Joe Hanson
February 7, 2020

What is contact center AI?

Contact center 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 contact center 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 contact center AI is the key to identifying them.

Once transcribed and analyzed, contact center AI automatically scores some parts of conversations and enables organizations to create tailored coaching programs for agents.

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The advantages of contact center AI

Contact center 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.

Contact center AI, built around Analytics-enabled Quality Management, radically transforms an organization’s quality programs in a number of ways:

Before contact center 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 contact center 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. Contact center AI helps ensure you’re an optimized leader by identifying and addressing the right gaps.”
- Kyle Kizer, Compliance Manager at Root Insurance 

Top contact center AI use cases

Contact center 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 contact center AI and quality automation is used today.

Mandatory Compliance Tracking

Why It Matters 

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)
  • Lawsuit mentions

Measurable KPIs

  • Customer verification %
  • Mandatory compliance dialogue %
Contact center AI monitors for compliance interactions, including redaction and customer verification.

webinar

If you're interested in diving deeper in compliance in particular, check out our on-demand webinar on 4 Can't Miss Compliance Trends and Predictions for 2020, presented by Jason Davis, Chief Compliance Officer at ERC BPO.

Openers & Closers

Why It Matters 

The beginning of a conversation is important from both a customer experience and a compliance standpoint. The end of a conversation is also important for customer experience, and it also is an opportunity to both better confirm how the call went and create next steps.

Examples 

  • Mention company name
  • Self introduction
  • Offer assistance
  • Customer verification
  • Recorded line message
  • Thank customer for calling
  • Offer further assistance

Measurable KPIs

  • Increase positive sentiment
  • Decrease negative sentiment
  • NPS
  • Adherence to brand standards
  • Lower average handle time
Contact center AI gets granular with opener and closing dialogues, monitoring for important interactions for both compliance and customer satisfaction.

Supervisor Escalations

Why It Matters
Supervisor escalations are a strong indicator of a negative customer experience, a metric for agent call-handling, or an organizational inefficiency. Escalations in any contact center are costly due to the amount of time and resources required to resolve them.

Examples

  • Issue cannot be solved by agent
  • Issue is outside of the agent’s role 

Measurable KPIs 

  • First call resolution (FCR)
  • Supervisor escalation rate
  • Average speed of answer (ASA)
  • CSAT
Contact center AI enables organizations to track how often supervisor escalations are taking place, and uncover the root cause, whether it be agent, product, or service-related.

Customer Sentiment Analysis

Why It Matters

Customer sentiment analysis is an indicator of how people feel about a brand, its products, and its service. Simple sentiment analysis is determined based on words alone (what's being said), while advanced sentiment analysis (tonality-based) considers tone and volume as well (what, how, and why it's said).

Examples 

  • Negative experience based on agent, process, or product/service

Measurable KPIs

  • Customer satisfaction (CSAT)
  • Reduced negative sentiment
  • Improved products and services
Sentiment analysis is a key component of contact center AI, analyzing voice calls to gauge emotion for both what is being said, and how it's being said.

Contact center AI: big benefits, bigger potential

Contact center AI is transforming the contact center as we know it, uncovering deep insights across every single voice call that takes place, and providing the data needed to drive more targeted training programs for agents.

“What’s exciting about contact center AI is that we can change the way we’re coaching and re-write our quality cards. We can move away from check-boxes and focus on real skill development. Using contact center AI helps us change behavior faster.”
- Dale Sturgill, VP Call Center Operations, EmployBridge
webinar

If you're interested in diving deeper in compliance in particular, check out our on-demand webinar on 4 Can't Miss Compliance Trends and Predictions for 2020, presented by Jason Davis, Chief Compliance Officer at ERC BPO.

About the Author

Joe Hanson leads content marketing at Observe.AI. Want to guest blog? Or maybe you have some expertise you want to share? Connect with him on LinkedIn.

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