What is Speech Analytics and How Does it Work

What is speech analytics?  

Speech analytics is a technology that utilizes artificial intelligence and natural language processing (NLP) to analyze customer conversations from live or recorded audio data. Contact center managers often use speech analytics to identify and understand their top contact drivers by keyword spotting in conversations. Insights gained from speech analytics can also reveal customer sentiment, customer satisfaction level and overall tone.   

Additionally, speech analytics is used for quality monitoring of voice-related operations since it can indicate call quality and agent performance with a fair degree of accuracy.  

Why is speech analytics important?  

Speech analytics is critically important for today’s businesses as it revolutionizes customer service operations in multiple aspects, enabling your organization to function at peak efficiency all the time. It enables contact centers to:  

• Identify customer preferences, pain points and satisfaction levels and use them to further improve service quality 

• Mitigate compliance risks by monitoring calls and ensuring adherence to industry regulations and brand guidelines 

• Monitor agent-customer interactions and help improve agent performance with actionable feedback and training programs 

• Increase cost efficiency and streamline processes by identifying scope for improvement in operations 

• Stand out from the competition by delivering consistently satisfying customer service experiences 

Speech analytics vs. voice analytics   

As we’re learning about gathering data-driven insights from spoken language, distinguishing between voice analytics and speech analytics becomes essential. Below is a quick differentiation between the two.  

Types of speech analytics 

 There are two primary types of speech analytics based on the timing of audio data analysis. 

1. Real-time speech analytics 

With real-time speech analytics, audio data is analyzed on live voice calls with customers. This allows agents to access actionable insights, trends and metrics in the moment, so they can improve the interaction quality of their current customer conversation. Real-time analytics reveal insights into customer sentiment and tone and even give cues to agents to enhance the customer experience — all while they are on a call. 

Take this conversation, for instance: the agent realizes the caller is agitated and quickly assigns her to an agent who tops the leaderboard to enable quick redressal. All of this happens on the fly as the agent has real-time access to insights such as customer sentiment and agent performance scores. 

Also Read: How to leverage a call center agent performance scorecard

2. Post-call speech analytics 

Post-call speech analytics provides you with insights into a voice call only after the call has ended. These insights include — but are not limited to — identifying keywords in conversations and building custom text classification models to help build future customer support processes and strategies. 

Here is an example of post-call speech analytics from a call transcript. It yields metrics like average handle time (AHT), overall customer satisfaction and sentiment on the call. Call quality analysts can get their hands on insights at specific timestamps to map trends and keywords. 

Benefits of speech analytics  

Contact center managers use speech analytics platforms to identify the reason for each call, the products mentioned and the callers’ mood — helping them to better understand customer needs, wants and expectations. Instead of making decisions based on assumptions (what they think customers are feeling), speech analytics enables you to make decisions based on what customers are actually saying in real time — removing the guesswork.    

Speech analytics platforms gather actionable insights that can be used in the following ways:   

1. Increase customer satisfaction 

The additional layer of AI in speech analytics helps analyze data from customer journey  — including their tone of voice, sentiment and key phrases, so you can:   

• Better comprehend inscrutable customer behavior and draw insights about satisfaction levels 

• Detect buyer intent, helping you set up behavioral remarketing campaigns and seamless omnichannel customer experiences 

• Coach new hires and prep them to address queries faster — helping improve your overall customer experience  

Learn more: Customer service tips: How to improve your strategies with technology   

2. Improve agent performance and resolution rates  

AI-powered speech analytics software can continuously retrain and improve their analysis models to ensure they provide the freshest insights to your agents — helping them improve performance and resolution rates. Here’s how this is achieved: 

• Identify repetitive customer queries, help agents automate resolutions and speed up some of their routine tasks to free up time for more complex issues   

• Empower agents with AI-driven insights about critical performance metrics such as first-contact resolutions and average handle time, meet their SLAs and keep up their productivity level 

• Help agents identify speech and communication issues during calls and make proactive improvements (for instance, are they talking too fast or facing other speech issues from nervousness?)   

• Ensure agents stick to their scripts and provide only brand-compliant responses — to avoid any legal/compliance-related issues and establish data privacy 

Learn more: Leverage AI to improve agent productivity and boost morale

3. Drive operational efficiency

AI-led speech analytics surface business intelligence that can help align your customer service, marketing and sales departments — enabling you to answer questions like:  

• Are better-priced competitors, poor customer service or misunderstood business value the reasons your customers are not buying your services or products?   

• Are your sales and customer service teams taking callers through a compliant script?    

• Is your support team responding promptly and nurturing a positive customer relationship?    

• What are the skills you should equip your sales and customer service teams with to help them be successful? 

Learn more: Call center agent skills to improve customer experience 

Once you are done uncovering the above insights, you can create support strategies to:  

• Identify process bottlenecks and improve your operational efficiency   

• Execute on-point capacity planning to keep your agents optimally engaged   

• Perform root cause analysis of customer issues to identify recurring problems and fix them

• Monitor mandatory compliance dialogs and phrases, such as settlement disclosures, data breaches and insider trading of sensitive information   

• Identify employee training opportunities and provide your agents with on-call coaching and assistance    

• Drive upsells, cross-sells and advocacy by monitoring customer conversations that indicate positive sentiment  

Learn more: 3 secrets of efficient and quality customer service  

4. Reduce costs

Real-time speech analytics tools and their capabilities also help businesses cut costs in several ways:   

• Avoid unnecessary callbacks and improve resolution rates 

• Direct customer queries to cost-effective channels like IVR or online self-service 

• Prevent fines payable for non-compliance 

• Eliminate costs associated with overstaffing 

Watch on-demand: Transform your contact center from a cost center to an intelligence center 

5. Mitigate risk pertaining to compliances  

Scoring each call and flagging breaches of compliance criteria in real-time can help you stay on the right side of the law and avoid penalties from regulatory authorities. At the same time, call monitoring also helps your compliance team focus on high-risk and low-quality calls. 

How does speech analytics work? 

Speech analytics follows a three-step approach to process, transcribe and analyze unstructured audio data from customers' voice calls.  

1. Data processing  

Speech analytics combines sophisticated artificial intelligence technologies — these include automatic speech recognition (ASR), natural language processing (NLP), machine learning, transcription, tonality-based sentiment analysis and algorithms to process and analyze human speech.  

2. Analysis 

Once the audio data from recorded and live voice calls is processed, speech analytics picks up on customer sentiment — ranging from positive to neutral or negative. For regulatory compliance purposes, it simultaneously masks sensitive information, such as credit card numbers, social security numbers and other personally identifiable information (PII). Keyword spotting can also detect pre-determined words in customer conversations.

3. Generate insights  

The next step is detailed reporting and analysis based on the parameters your team has set — such as call quality, agent performance, sentiment, compliance monitoring and trend identification. Measure these against your customer service KPIs to ensure business goals are being met and to uncover areas of improvement. 

Pro tip: Today, the best speech analytics software can perform automated quality management. It can assess 100% of your conversations, evaluate agent performance based on predefined parameters such as call opening, handoff etc., give individualized improvement suggestions and compare the handling agent’s performance against team benchmarks and industry benchmarks.

Read more: Important call center agent performance metrics to track [+ Optimization Tips] 

Application of speech analytics in different industries 

Even though speech analytics is a comparatively new technology in the market, it is being increasingly adopted by organizations worldwide owing to the level of insights it can bring into contact center operations. Here are some of the real-life use cases of speech analytics software across multiple verticals: 

1. Technology

Speech analytics helps with streamlining customer support and enhancing product development in the technology industry.  

For example, a software company can use speech analytics to:  

• analyze customer support calls 

• identify recurring issues and user frustrations 

• align your product development to address those concerns better.  

 By identifying these pain points, the company can prioritize improvements that directly address customer concerns, leading to a more user-friendly and competitive product. This approach also reduces support costs in addition to improving customer satisfaction, as fewer customers encounter problems due to proactive software enhancements.   

Download: Tech industry benchmarks report for digital CX & social  

2. Financial services

The financial sector employs speech analytics as the primary layer of security enhancement and fraud detection. FinServ brands can identify unusual or suspicious activities by analyzing the voice patterns of callers.   

For example, if a customer's voice pattern during a high-value transaction call differs noticeably from their previous calls, it could be flagged as suspicious. With real-time analysis of voice calls, fraud prevention and security become seamlessly integrated with your day-to-day operations, while legitimate transactions are allowed to proceed seamlessly.  

Make Compliant Customer-Centricity a Reality for Your Financial Services Business

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3. Retail industry  

In the retail sector, call center statistics indicate that more than half of customers prefer voice calls to contact support and resolve their issues. Retail companies often gather lots of data through customer interactions — and analyzing these conversations can provide valuable insights.  

 For example, an apparel store can use speech analytics to: 

• Monitor customer service calls and identify common concerns or preferences with products. 

• Use this data to optimize their inventory, stocking items that customers usually purchase more 

• Train their salespeople to address customer needs better — which helps improve sales and customer satisfaction. 

A guide to retail customer service (+ tips to improve it)

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How Sprinklr helped Cdiscount optimize operations with speech analytics

Cdiscount, a prominent French e-commerce company, recognized the need to enhance its customer experience given its vast customer base and extensive transaction volume. They aimed to gain a deep understanding of customer sentiment and feedback at scale, along with assessing the quality of customer interactions across all their channels.  

The challenge: With millions of customer conversations across channels, the company struggled to monitor them and gather useful insights that can help improve their customer service. Their primary goals were to:  

• Analyze all customer conversations with no/minimal manual efforts 

• Provide real-time sentiment analysis to deliver highly satisfactory service experiences 

• Measure interaction quality and identify an action plan to improve the quality of customer service. 

The solution: As a result of this initiative, the e-commerce company chose to try Sprinklr Service — an omnichannel customer service solution with advanced speech analytics capabilities. Through Sprinklr's AI-powered speech and text analytics, Cdiscount was able to: 

• Transcribe and analyze voice, chat and social interactions on one platform.  

• Access valuable insights into customer concerns like delivery issues, refunds and subscription queries. Provide highly personalized agent training that helps improve their customer service quality.  

• Protect brand reputation with real-time alerts flagged sensitive interactions,  

• Detect operational issues with Sprinklr AI.  

The result: After implementing Sprinklr Service, the company has seen tremendous improvement in the quality of customer service being delivered due to the amount of monitoring being performed. To mention a few support milestones that they achieved post-implementation:  

• Analyzes 100% of voice calls — amounting to about 2M calls or 200k hours of calls every month 

• Monitors 75k conversations across channels in real-time 

• 15% improvement in CSAT since implementation 

  

Interested in knowing more? Read the full story here >   

Bottom line  Customers reach out to your business on multiple channels — including voice — and leave insights that should be leveraged to improve customer service performance and ROI. But to do so, you need a single point of truth that unifies all those insights, metrics and trends in one place — so your decision-makers can access them quickly.   

Sprinklr’s AI-driven speech analytics software can help. Powered by the world’s only unified customer experience management (Unified-CXM) platform, this technology helps centralize analytics and insights from scattered, raw voice call data — and leverages advanced capabilities, including:  

• Automated speech recognition engine that detects user expressions with a Word Error Rate (WER) of 0.15 (tested)  

• Text-to-speech feature that can train the voice of your bot to resemble any human voice  

• Custom intents and ASR models that are tailored to industry use cases and support multiple languages 

• Speech analytics dashboard with a Tone of Voice widget to identify the prevalent customer intents — and a conversations widget to view and access all of your cases  

• AI-led Smart Alerts to indicate breached SLAs and escalations  

• Customer sentiment insights are organized by issue type, priority and channel  

• AI-powered CSAT prediction to detect fluctuating sentiment during a call and generate insights to boost CSAT 

Sign up for a free, fully functional trial and know how speech analytics in Sprinklr Service can enhance and optimize your customer service operations! 

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