ūüöÄūüöÄūüöÄ Observe.AI Launches Real-Time AI for Contact Centers ūüöÄūüöÄūüöÄ Learn More ‚Üí

Speech analytics

Speech analytics is a software technology that transcribes 100% of voice calls and derives deep insights, trends, and metrics from each call. It utilizes AI services including transcription, natural language processing, and speech technologies to understand, analyze, and derive insights from a voice conversation. These insights are then used to evaluate agent performance, assess customer experience, and monitor organization-wide strengths and shortcomings on every voice interaction.
Glossary >S - Z

How does speech analytics work?

  1. Data Processing: It uses a number of AI-services, including automatic speech recognition, transcription, and tonality-based sentiment analysis to analyze both the audio recording and call metadata.
  2. Analysis: Once the call recordings are analyzed, speech analytics then categorizes, keyword spots,¬† redacts (for compliance purposes), and reports its analysis of the call.¬†‚Äć
  3. Insights: The speech analytics platform then delivers detailed reporting on the analysis, including call quality, sentiment, agent performance, and compliance monitoring.

Speech analytics benefits

  • Significantly increases call coverage: Historically, QA teams in call centers on average quality check 2-4 voice calls per agent, per month. With speech analytics, organizations can review up to 100% of voice calls.
  • Monitor key KPIs: Speech analytics empowers customer service and support teams to set up analysis on any number of customer interactions moments. This is anything from supervisor escalations and compliance violations, to customer satisfaction and average handle time (AHT).
  • Provide near-realtime speech analytics feedback: With faster analysis and 100% call coverage,¬†supervisors can deliver tailored feedback almost immediately to agents.
  • Uncover hidden inefficiencies:¬†By monitoring a variety of contact center KPIs, leadership can better understand what's impacting those KPIs and unearth inefficiencies causing them.
  • Personalized¬†training:¬†With deep insights on 100% of customer calls per agent, supervisors and L&D¬†teams can create custom tailored coaching sessions for individual agents.
  • Improve customer experiences:¬†With sentiment analysis, teams can look at the things driving positive customer experiences (eg. empathy statements), and indicators of negative ones (eg. supervisor escalations), and in turn, reduce customer churn.

Speech analytics use cases, examples, and KPIs

Here's an example of a voice call after analysis with speech analytics. You can see instances of call opening, negative sentiment, supervisor escalations, and call closers exactly where they took place.

Monitor mandatory compliance dialogues

Regulatory compliance is paramount across all industries, most notably financial, insurance, and healthcare, ensuring 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 include:

Monitorable KPIs:

  • Customer/account verification
  • Legal cancellation disclosure
  • Recorded line message
  • Mini-miranda

Call Openers

The beginning of a conversation is important from both a customer experience and a compliance standpoint.

Examples include:

  • Did the agent positively greet the customer, introduce themselves, and get the customer‚Äôs name?
  • From there, did the agent successfully go through any customer verification required for compliance (eg. phone number, SSN, credit card information, etc) or any required dialogues (eg. ‚ÄúThis line is recorded.‚ÄĚ)

Monitorable KPIs:

  • Mention company name
  • Self introduction
  • Offer assistance
  • Customer verification
  • Recorded line message

Call Closers

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 include:

  • Did the agent adhere to a call closure script?
  • Did they set a follow-up appointment when necessary, ask if the customer if they have any additional questions or issues before ending the call, or ask if the service they were provided was within their standards?

Monitorable KPIs:

  • Thank customer for calling
  • Offer further assistance

Supervisor Escalations

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 include:

  • Identify when customers are escalating calls to a supervisor/manager, and know not only who, but what is driving escalations.
  • At an agent level, see who the top outliers are. For why escalations are happening, review what behaviors and situations drive escalation rates, and data to quickly address it.
  • Training teams can course-correct agent behavior through education and awareness.

Monitorable KPIs:

  • First call resolution (FCR)
  • Supervisor escalation
  • Average speed of answer (ASA)

Customer Sentiment Analysis

Customer sentiment analysis is an indicator of how people feel about a brand, its products, and its service.

Examples include:

  • Monitor where negative experiences are occurring, and determine if they are people, process or product-related.
  • Make data-backed decisions to create coaching programs for agents, redesign processes, and deliver product feedback back to the organization.

Monitorable KPIs:

  • Customer satisfaction (CSAT)
  • Customer negative sentiment
  • Hold time violation
  • Dead air
  • Average speed of answer (ASA)
  • Gestures of Good Will (GOGW)

Operational Efficiency

Operational efficiency is critical for improving critical contact center KPIs, all contributing to lowering average handle time (AHT).

Examples include:

  • Identify hold time violations, dead air, first call resolution and determine AHT.
  • Build comprehensive scorecards of efficiency KPIs to better train agents with more relevant coaching to improve performance.

Monitorable KPIs:

  • Call hold
  • Dead air
  • Hold time violation
  • AHT
  • Average speed of answer (ASA)

Speech analytics in the contact center

Speech analytics has driven QM processes to grow more automated, more accurate, more efficient, and more relevant to the agents themselves. It’s had a massive benefit on organization leaders, supervisors, and the contact center agents themselves, impacting customer experience, compliance, and learning and development. A speech analytics solution drives:

  • ‚ÄćMore automation:¬†Analysts no longer have to manually score calls. From transcription to analysis, the entire process is automated.‚Äć
  • More accuracy:¬†Transcription of voice calls will continue to improve, with the industry benchmark at 78% and climbing.‚Äć
  • More intuitive:¬†Comprehensive reports and dashboards of contact center performance, organization-wide to per-agent accessible and easily crunched.‚Äć
  • Improved scorecarding:¬†Scorecards allow teams to dig into individual agent performance, pinpoint areas that need improvement, and develop new training programs.¬†‚Äć
  • Improved business insights:¬†Organization leaders can visualize performance, bringing KPIs to life and create programs to change behavior.‚Äć
  • Improved agent coaching:¬†Supervisors and managers will be able to rapidly prep coaching sessions relevant to every agent, backed by data.