Voice Analytics: The power of Data Insights

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Analyzing customer interactions is the key to improving customer experience. It also helps enhance customer service quality and assess agent performance.

However, listening to thousands of customer calls or manually examining transcripts is challenging. It can also prove to be a counterproductive effort that consumes time and resources. Here is how voice analytics can help you. 

What Is Voice Analytics?

Voice analytics are a set of AI-enabled technologies for analyzing voice data and drawing valuable information from it. Voice and speech analytics help managers optimize customer service quality monitoring and identify areas of improvement. 

A voice analytics tool does many things – from recording customer calls to transcribing them to interpreting the underlying emotions. 

How Does Voice Analytics Work?

Voice analytics use a mix of technologies to analyze customer interactions, such as speech recognition, Natural Language Processing (NLP), machine learning, and sentiment analysis. We will talk more about these technologies later. 

Together, they convert unstructured voice data into structured text data and draw insights from it. The process happens in multiple steps: 

• Recording calls and other voice interactions

• Transcribing calls using speech recognition 

• Analyzing transcriptions to identify keywords, phrases, and themes 

• Performing sentiment analysis to reveal the emotions of speakers

• Presenting insights as real-time analytics

Voice analytics can identify the intents and emotions of speakers by studying both words and voice patterns, such as tone and pitch. For instance, this technology can analyze how loudly, quickly, or slowly the customer speaks to detect emotions. 

In addition, robust voice and speech analytics software offers easy integration with other digital tools, such as CRM, ERP, VoIP telephony, and APIs. 

Is There A Difference Between Voice Analytics And Speech Analytics?

Both speech analytics and voice analytics are subsets of conversation analytics, but they differ from each other in the following aspects: 

When used together, voice and speech analytics help improve customer experience and enhance agent performance. 

Key Technologies Behind Voice Analytics

Voice analytics tools come with a set of advanced technologies to turn voice data into valuable insights. Let us check out some of these technologies and components:

1. Automatic Speech Recognition

Automatic Speech Recognition (ASR) uses AI-powered technologies to convert spoken words into written text. It translates voice calls into readable and scannable transcripts. 

ASR uses adaptive algorithms and machine learning models to transcribe speech. These models are trained on large volumes of speech samples, so they can identify different accents, pronunciations, and speech patterns. 

2. Natural Language Processing (NLP)

Natural Language Processing (NLP) is the cornerstone of modern-day analytics. It combines artificial intelligence, computer science, linguistics, and machine learning to extract meaning and context from voice and text data. 

NLP examines sentences, words, and their relationships based on pre-set rules and understands even the most complex customer interactions and their context. It happens in five phases:

1. Analyzing word structures by breaking texts into paragraphs, phrases, and words 

2. Checking grammar and word arrangements to identify the relationship between words 

3. Looking for the meaning of a statement by focusing on the literal meaning of words/phrases

4. Identifying the larger context of a sentence by analyzing prior words and sentences

5. Extracting meaning from the use of language and derives the intended meaning of the text, rather than the literal meaning

3. Topic Modeling 

Topic modeling is a technique used in NLP to identify key topics. It scans large volumes of data to reveal the common themes and topics discussed in conversations. 

This feature enables customer support managers to tag conversations and route them to the right support teams. 

Example: You can tag conversations with words like ‘subscription’, ‘credit card’, and ‘balance check’ under one topic and route them to the accounts team. Likewise, calls containing words like ‘glitch’ and ‘login issues’ can be sent to the technical team.

4. Keyword Spotting 

Keyword spotting helps detect a given set of words and phrases in voice data. It allows you to choose specific words to act as triggers for specific actions. 

Example: It can detect negative words like ‘frustrated’ or ‘trouble’. Managers can set alerts for such negative words, monitor calls when alerts are triggered, and intervene if necessary. 

5. Sentiment Analysis 

Customer sentiment analysis examines conversations to detect three types of emotions – positive, negative, and neutral. It allows businesses to measure customer satisfaction, assess agent performance, and improve customer service. 

Sentiment analysis examines both lexical and acoustic features:

Example: Words like ‘terrible’ and ‘late delivery’ typically indicate negative emotions, while ‘great product’ indicates positive emotions. Managers can use voice analytics tools to tag these words and prioritize customer calls. 

6. Speaker Diarization

Speaker diarization is useful while analyzing voice communications of multiple speakers. This feature makes speech-to-text transcription easy and increases the readability of the transcript. Further, it helps sales managers review post-sales meetings that involve multiple people. 

Example: It enables managers to check whether a customer has agreed to the terms and conditions. 

7. Intent Classification

Voice analytics have algorithms that can identify and classify the intent or the purpose of a customer call. Individual words can be analyzed to indicate whether the customer is making a complaint, suggestion, query, feedback, or request. 

• Example 1: The product is faulty. There seems to be a glitch. (Complaint)

• Example 2: What is the status of my purchase? (Query)

Agents can then use actionable insights from this data to guide their decisions.

8. Real-time Analytics 

Voice analytics tools also offer real-time analytics through reports and dashboards. They make insights and data from current and past conversations readily available to agents during live calls. They can contextualize and personalize conversations using these insights. 

Managers can also access the call data and reports in real time to evaluate agent performance. You also get an instant picture of customer sentiment.

9. Security Features

Advanced analytics tools have security features to protect sensitive data. For instance, the use of voice encryption protects data from breaches. Many solutions also have features to ensure that the call is from a live person, not bots. 

Benefits of Voice Analytics

Analyzing customer calls enhances multiple aspects of businesses. Check out the top six benefits voice analytics have to offer:

1. Detect and resolve issues before they escalate 

Customers don’t tolerate poor interactions. Reports show that 50% of customers feel exhausted while dealing with support teams, and 73% will switch to a competitor after multiple bad experiences. 

Voice analytics help detect signs of dissatisfaction in customers before it escalates into bigger problems, helping decrease customer churn. Further, they flag areas of concern for agents to proactively intervene and prevent the same issues from happening in the future. 

Use case: Insights from a voice analytics software will reveal if customers are unhappy with your call hold time. Based on this intel, you can optimize your staff, offer self-service tools like Interactive Voice Response menus, or streamline call routing. 

2. Enhance regulatory compliance

Customer support teams deal with lots of personal and sensitive data, so they must adhere to regulations related to payment security, personal data protection, and more. 

Managers can leverage voice analytics to monitor compliance, tracking and scoring 100% of calls. The tool can spot specific words or phrases in customer conversations, allowing managers to make timely interventions and prevent breaches. 

Use case: Voice analytics software can alert you when agents use risky language or don’t give the required disclosures. Likewise, you can use it to trigger PCI/PII redaction when the customer or agent speaks words like ‘credit card’, ‘payment’, ‘account number’, etc. 

3. Saves time and money

By cutting costs and time in multiple ways, voice and speech analytics software allows businesses to spend their resources on high-value activities. 

• Reduces manual labor by automating speech-to-text transcriptions.

• Voice analytics tools facilitate automatic call monitoring and compliance checking. They help reduce the number of employees required for such activities and prevent costly litigations.

• Better insights about customers create opportunities for upselling, which improves sales efficiency and increases revenue. 

• Enables agents to spot and fix issues fast and with fewer calls, thus reducing average call handle time and the cost per call

Use Case: Insights from voice analytics point out areas where you can offer self-service solutions, such as frequently asked questions and Interactive Voice Response (IVR) menus. By letting customers figure out answers on their own, you can engage your resources on high-value calls. 

4. Increase customer satisfaction and loyalty through personalization

A study by McKinsey & Company shows that over 71% of customers expect personalization from brands. Moreover, 70% of customers expect support agents to have full context during interactions. 

Voice analytics software solutions increase context awareness by making real-time insights available to agents. By recognizing customer sentiment, they can personalize responses and increase customer satisfaction. It also comes in handy in customer service quality monitoring. 

Use Case: Analytics data of a software solutions company will show if a customer has recently experienced a malware attack. Your agents can proactively intervene to offer them suitable solutions or give them personalized product recommendations for data safety. 

5. Enhance the complaint tracking system 

Voice analytics tools classify complaints using the keywords and phrases used in customer conversations. Support teams can track complaints more efficiently and offer more focused and quicker resolutions using these features. 

Use Case: It picks words like ‘payment issue’, ‘refund’, ‘transaction failure’, etc., and classifies them under one category. The system can send such complaints straight to the relevant department and reduce unnecessary call redirections. 

6. Offer relevant products and services

Voice analysis tools examine the feedback, suggestions, and requests of customers to spot the key trends and patterns in their behavior. 

They give brands deeper insights into the needs of customers so that they can develop products that cater to the latest trends and take other quality assurance measures to enhance products. 

Use Case: If an online shopping store finds a positive customer sentiment towards ‘sustainable gifts’ or ‘green gifting’, they could introduce a new segment on that. 

Navigating Voice Analytics Limitations And Challenges

Despite the benefits voice analytics offer, they also have a few limitations. For instance, human speech contains dialects, regional variations, metaphors, jargon, and sarcasm. Analytics might not be able to comprehend such complexities using standardized rules.

Likewise, speech recognition relies heavily on the quality of voice data, so poor audio quality could affect the results. You could address this issue by choosing software solutions with echo/noise cancellation and speech enhancement.

The key to overcoming such limitations is to select the right technology stack and a state-of-the-art business phone system. Aircall, for instance, has features like call transcription, call recording, analytics, and call monitoring, all of which enhance voice analytics. 

Invest in the right tools today to start deriving insights from your customer call data. 

Published on May 3, 2024.