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Speech Analytics

Contact center speech analytics is a technology that transcribes 100% of voice calls using AI and derives deep insights, trends, and metrics from each call.
Glossary >S - Z


How Speech Analytics Can Revolutionize Your Contact Center Operations

If your contact center is looking to uplevel everything from its operations to its revenue generation capabilities, speech analytics will be a crucial part of the solution. 

Speech analytics, which refers to the transcription and analysis of customer calls, is gaining traction across industries for its positive impact on key performance indicators such as average handle time (AHT), first call resolution, and customer satisfaction.

This versatility and potential for deep impact is one of the reasons Observe.AI focused on perfecting its speech analytics technology in its suite of conversation intelligence capabilities, causing G2 to name Observe.AI a leader in speech analytics and quality assurance in its Summer 2022 report.

Here’s how speech analytics works and its benefits for contact centers.

What is speech analytics?

First things first: What is speech analytics? Speech analytics software uses speech recognition, natural language processing, and machine learning to convert the spoken words of customer conversations into text. The software can then analyze this text to provide insights into customer sentiment, preferences, and needs.

Speech analytics tools offer real-time analysis of voice recordings, providing an instantaneous feedback loop for contact center agents and promoting continuous improvement. This allows agents to better understand customers' needs and adjust the conversation accordingly, improving both performance and customer satisfaction.

How is speech analytics different from conversation intelligence?

Speech analytics is a subset of conversation intelligence—it’s specifically the call recording and transcription part of the process that transforms call center interactions into business results. 

Conversation intelligence is the larger umbrella that includes the end-to-end contact center operations:

  • Quality monitoring and automated QA
  • Coaching and training workflows
  • Performance analytics
  • Real-time AI 
  • Generative AI 

Speech analytics is one piece of the puzzle to allow organizations to extract the kind of insights that drive business results. 

How does contact center speech analytics work?

At its core, speech analytics software collects and analyzes the data from customer conversations and provides aggregate data through dashboards, reports, and call transcripts. Dashboards provide contact center agents and managers with real-time insights into call volume, agent performance, customer sentiment, and other metrics. The transcripts offer accurate, searchable records of customer interactions that agents and managers can use for training and quality assurance purposes.

There are three phases to speech analytics and each plays a key role in reaching the desired objective of surfacing deep contact center insights, trends, and metrics from each call that can be used for strategic business decisions and contact center improvements. 

Before entering the first phase, 100% of voice calls and their metadata are injected into the speech analytics software. That kicks off the following process:

  1. Data processing: The technology uses a number of artificial intelligence 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, highlights keywords, redacts (for compliance purposes), and reports its analysis of the call.
  3. Insights: The speech analytics platform then delivers detailed reporting on the analysis, including sections on call quality, sentiment, agent performance, and compliance.
Visual of the three phases to speech analytics technology

The benefits of speech analytics

There are numerous benefits of speech analytics depending on the size of organization, industry, volume of agents, and many other factors, but the most common and evergreen benefits are the following:

  • Significant increase of call coverage: Historically, QA teams in call centers quality-check 2 to 4 voice calls per agent, per month, on average. With speech analytics, organizations can review up to 100% of voice calls.
  • Monitor key KPIs and boost quality assurance: 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). Speech analytics solutions can identify areas where agents fall behind on quality benchmarks and provide actionable insights for improvements. This enhances quality assurance, a vital aspect of contact center operations.
  • Provide near-real time speech analytics feedback: With faster analysis and 100% call coverage, supervisors can deliver tailored feedback almost immediately to agents. Many contact centers have begun to implement Real-Time AI, which provides in-the-moment agent guidance and feedback. Speech analytics also uncovers valuable business intelligence, delivering customer insights around needs and trends that contact centers can use to inform strategic initiatives.
  • Improve operational efficiency: Speech analytics reduces the need for time-intensive review processes, enabling contact centers to maximize operational efficiency and handle larger call volumes. Speech-to-text functionality also allows agents to ramp up their self-service capabilities, freeing up contact center resources for more complex issues that require a live agent.
  • 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: Speech analytics provides detailed insights into customer needs and preferences. 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. 
  • Automate your QA process accurately: Ultimately, if you have good speech analytics, you’ll be able to ingest calls accurately and more effectively automate your QA process to expand and deepen your conversation insights.

Speech analytics use cases, examples, and KPIs

Speech analytics has numerous use cases, demonstrating its versatility in contact center operations. Financial services companies can use speech analytics to identify and prevent instances of fraud or unauthorized disclosures. Healthcare providers can use speech analytics to ensure compliance with HIPAA regulations and improve patient outcomes by identifying valuable care insights. Retailers can use speech analytics to improve omnichannel experiences and identify opportunities for upselling and cross-selling.

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.

Use case #1: Monitor mandatory compliance dialogues

Regulatory compliance is paramount across all industries, most notably in financial services, insurance, and healthcare. Strict legislation exists to help ensure the protection of customer data. 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

Use case #2: Call openers

The beginning of a conversation is important from both a customer experience and a compliance standpoint. It's incredibly important for contact centers to optimize call openers to improve CSAT, mitigate compliance risk, and improve conversion rates for sales calls.

Examples include:

  • Did the agent positively greet the customer, introduce themselves, and get the customer’s 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. “This line is recorded.”)

Monitorable KPIs:

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

Use case #3: 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 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

Use case #4: Supervisor escalations

Supervisor escalations are a strong indicator of a negative customer experience 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)
Example of a voice call analysis when using speech analytics
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.


Use case #5: Customer sentiment analysis

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

Use case #6: 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)

4 things to look for in a speech analytics model

When looking to implement a speech analytics solution, contact centers should prioritize 4 key features:

1. Built for the contact center

You want a solution developed specifically for the contact center environment, which accounts for things like noise, low audio quality, and poor microphones.

The solution should also expertly leverage Natural Language Processing (NLP): the branch of AI focused on “understanding” text and spoken words. A common use of NLP is in interactive voice response (IVR) systems for customer interaction, as well as in question answering, text classification, and information retrieval with features like automatic suggestions. Using NLP, customers can interact with a company’s automated systems using natural speech.

2. Custom business term recognition

Avoid black-box solutions that can’t be tailored to your specific needs or updated with new information. Observe.AI uses a proprietary AI engine to recognize (and allow you to update) critical business phrases customized by you. Examples include brand names and specific compliance terms.

It’s important to note that recognition of these specific business terms is a key feature in a speech analytics solution because this feature contributes to transcription accuracy. At the end of the day, speech analytics is useless unless transcriptions are accurate. As they say, “garbage in, garbage out:” If your transcriptions aren’t correct and complete, the data and insights you pull from them will be unhelpful—or, in the worst-case scenario, downright harmful. After all, the impact of low-quality transcripts exponentially increases when you’re factoring in thousands of calls. Not to mention the potential negative impact on compliance.

3. Sentiment detection 

Context matters. 

“Don't take 80% or 90% [transcription] accuracy at face value. What matters most is if the context behind the words is captured and critical business terms are recognized,” explains Jithendra Vepa, chief scientist at Observe.AI. “These words and the intent gathered from tonality, frequency, rate of speech, and more become the foundation to deciphering key moments and sentiments from conversations.”

Your solution should be able to detect the meaning and emotions behind the words. To do this, the best solutions use Natural Language Understanding (NLU), which is a subset of natural language processing (NLP). Using NLU, AI-powered solutions like Observe.AI can precisely determine the speaker’s intent, regardless of how they’ve expressed it.

4. Quality improvements

Again, your solution should be adaptable. It should include regular transcription improvements and an easy-to-use feedback loop to ensure the highest level of accuracy at all times.

This is where Natural Language Generation (NLG) can really help. Another subset of NLP, NLG enables AI to produce natural-language text responses to users based on data input. Not only can NLG provide agents with real-time notes and suggestions during a call, but it can also summarize calls and produce detailed post-call summaries, alerts, and coaching.

Why every contact center needs speech analytics

Speech analytics has driven QM processes to grow more automated, more accurate, more efficient, and more relevant to the agents themselves. It has 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:

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

If you are interested in learning about Observe.AI's contact center speech analytics software, then schedule a demo with our sales team or watch our 15-minute on-demand demo.