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CXM Guide

Speech Analytics

Learn everything about speech analytics – what it means, how to use it to optimize your voice-based processes, and how it helps boost CSAT and customer retention.

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What is speech analytics?

Speech analytics is a technology that leverages artificial intelligence and natural language processing (NLP) to process and 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 detecting frequently-used phrases during customer interactions. Insights gained from speech analytics can also reveal customer sentiment, 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. 

How does speech analytics work?

Speech analytics follows a three-step approach to process, transcribe, and analyze unstructured audio data from customer 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 KPIs to ensure business goals are being met, and to uncover areas of improvement. 

Discover why AI is a must have for Care Agents

Types of speech analytics

There are two types of speech analytics based on the time the audio data is analyzed.

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, tone, and trends, and even give cues to agents to enhance the customer experience — all while they are on a call.

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.

Post-call speech analytics vs. real-time speech analytics

Post-call speech analytics tools are prescriptive in nature and are useful for quality management and assurance. Customer support managers and contact center agents need to see key metrics in aggregate, like common queries and agent performance.

Say you want to track how often words like “refund” or “payment issue” are spoken in customer calls. You can create queries with these keywords and track them using post-call speech analytics to understand how often these issues arise.

Real-time speech analytics tools allow you to monitor ongoing conversations, so that live agents can better deal with customer issues in the moment. This can help your agents understand the true sentiment of a caller — as well as their buying intent. When real-time call quality monitoring is coupled with real-time coaching and assistance, agent performance improves significantly. 

Additionally, contact centers can use speech analytics tools to improve compliance and partially automate the quality assurance process — reducing the possibility of SLA or compliance breaches and resulting penalties. 

Introducing Sprinklr Voice: A Radically Different Contact Center Solution

What are the uses and benefits of speech analytics? 

Contact center managers use speech analytics platforms during customer interactions 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 customer journey data — including their tone of voice, sentiment, and key phrases, so you can:

  • Better comprehend inscrutable customer behavior and draw customer insights about intent and satisfaction level 

  • Detect buyer intent, helping you set up behavioral remarketing campaigns to create seamless 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 process call recordings and interpret sentiment from customer interactions quite accurately. In addition, these tools 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, and help agents automate and speed up some of their routine tasks 

  • Empower agents with AI-driven suggested answers to common customer questions — so they can offer faster resolutions, 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?) 

  • Free up agents’ time so they can focus on more complex tasks which require critical thought and a human touch

Learn more: How to Turn Your Agents Into Advocates

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?

Once you are done uncovering the above insights, you can create marketing and sales strategies to: 

  • Highlight your product or service benefits and differentiators to outperform the competition

  • Make customer service a true differentiator for your business — and reduce customer churn — by offering your callers an exemplary support experience

  • Ensure that customers receive your marketing messages and product information as intended

  • Monitor mandatory compliance dialogs and phrases, such as settlement disclosures, data breaches, and mis-selling 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: Your customers don’t want to call you, and other hard customer care truths

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 

  • Automate certain processes and reduce agent headcount 

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 to Resolve Issues Faster with the Right AI-Powered Routing Strategy

Use Sprinklr’s AI-powered speech analytics solution to improve your CSAT by 200%

Customers reach out to your business on multiple channels — including voice — and leave insights that should be leveraged to improvise 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 organized by issue type, priority, and channel 

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

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