- Technological impact. AI-driven speech analytics revolutionizes customer experiences.
- Operational efficiency. Speech analytics boosts contact center performance and savings.
- Data-driven insights. Speech analytics facilitates improved decision-making and service.
Speech analytics plays a vital role in understanding customer interactions, intent and sentiments. By using artificial intelligence (AI) and natural language processing (NLP), speech analytics platforms are able to detect emotions, stress and audio patterns in a customer's voice. Speech analytics enables contact centers to detect recurring phrases in customer interactions, revealing call purposes, mentioned products and caller moods. These insights help them better comprehend customer needs and expectations and improve the overall customer experience.
This article will take a close look at speech analytics, what it is, why it is important, and what we believe are the best speech analytics platforms available today.
What Is Speech Analytics?
Speech analytics platforms employ advanced AI technologies such as speech recognition, language processing, machine learning, transcription, emotion detection through tone, and various algorithms to understand and analyze human speech. By providing a diverse set of features and applications, these platforms enable call centers to enhance customer service while boosting operational efficiency. A 2022 McKinsey report revealed that contact centers that use speech analytics can see a 10% improvement in customer satisfaction, an increase in sales and cost savings of 20% to 30%.
Marnix Broer, CEO and co-founder of Studocu, a student document resource community, told CMSWire that speech analytics platforms are an incredibly useful tool at several points in the customer journey, from sales to support and more. "While some of the most experienced sales or support reps might be able to understand customers so quickly it feels instinctual, a platform can give measurable data to quantify those instincts and help bring less experienced representatives up to speed faster," said Broer.
The step-by-step process involved in speech analysis typically includes the following stages:
- Audio collection: The first step involves gathering audio data from various sources, such as recorded calls, live calls or any other spoken data.
- Pre-processing: The audio data is pre-processed to eliminate noise, normalize volume levels and segment the audio into smaller, manageable chunks.
- Speech-to-text conversion: Automatic speech recognition (ASR) technology is used to convert the audio data into text format for further processing.
- Language processing: Natural language processing (NLP) techniques are applied to the transcribed text to identify linguistic elements such as words, phrases, sentences and their grammatical structure.
- Feature extraction: Key features and patterns within the transcribed text are identified, which can include specific words, phrases or emotions expressed by the speaker.
- Sentiment analysis: Emotions and sentiments within the speech are detected and analyzed, such as anger, happiness or frustration. This step can involve analyzing the tone, pitch and intensity of the speaker's voice.
- Machine learning: Machine learning algorithms enable speech analytics platforms to recognize patterns and trends in the data that has been extracted from conversations, which can facilitate the prediction of outcomes or provide recommendations for improvements.
- Results and insights: The analyzed data is presented in a format that is easily understood, such as reports or visualizations, and includes actionable insights and valuable information for decision-making.
- Continual improvement: The speech analytics system is continually updated and refined with new data and feedback, a process that ensures that analysis remains accurate and relevant.
Ricky Spears, CMO and founder of Ricky Spears, an internet and gaming tutorials and tips resource, told CMSWire that customers' wants, tastes and pain areas may all be better understood with the help of speech analytics. "This data may be utilized to enhance customer satisfaction, streamline business processes and boost sales. Real-time feedback for call center workers is another major advantage of speech analytics,” said Spears. “Real-time monitoring of client calls with speech analytics technologies allows for immediate coaching of employees on engaging with customers."
Related Article: NLP and Text Analytics Enhance VoC Programs, Boost CX Engagement
Top 13 Speech Analytics Platforms
Whether they are used to improve the effectiveness and efficiency of contact center agents, or provide marketers and advertisers with data-driven insights, speech analytics software solutions play a crucial role in understanding customer interactions and sentiments.
With a wide variety of features and functionality, these AI-enhanced platforms can be a valuable tool for improving the customer experience. “When selecting a speech analytics platform, it is important to consider your specific needs and requirements, as well as the platform's capabilities and pricing structure,” explained Spears, who reiterated that each platform is different, with its own unique features and benefits.
Here are some of the top speech analytics platforms that are available today:
- Observe.AI: This contact center speech analytics platform provides end-to-end conversation intelligence and enhances contact center performance by using what is billed as the industry's most accurate AI engine, which analyzes 100% of customer interactions across channels and provides actionable, context-driven feedback.
- Qualtrics Connect: Formerly known as Clarabridge Engage, this platform offers a comprehensive speech analytics solution that uses natural language processing (NLP) and machine learning algorithms to identify trends and patterns in customer conversations, enabling brands to make data-driven decisions.
- CallMiner Eureka: A full-featured platform, CallMiner Eureka enables brands to gain insights into customer interactions through live and recorded audio, email, chat and more. It is able to identify potential improvements in customer service, marketing and advertising by analyzing language patterns, sentiment and emotion in customer conversations.
- Invoca: Billed as a conversational intelligence platform, Invoca specializes in call tracking and conversational analytics for marketers. It provides real-time call analytics that can be integrated with other marketing tools, enabling brands to optimize their advertising strategies based on customer insights.
- NICE Nexidia: NICE Nexidia features advanced speech analytics, which allows brands to uncover valuable insights from customer interactions. Using AI-driven algorithms, it is able to analyze large volumes of audio data, enabling businesses to make well-informed decisions.
- Verint: Verint's speech analytics platform facilitates the analysis of customer interactions to identify trends and uncover opportunities to improve the customer experience. The platform's advanced analytics capabilities provide valuable insights into customer behavior and preferences.
- Genesys: Genesys provides a suite of customer experience solutions, including speech analytics tools tailored for the contact center. The platform helps brands to understand customer preferences and sentiments, and analyzes both voice and text interactions.
- Avaya: Avaya's conversational intelligence platform helps call center agents to monitor and analyze customer conversations to identify trends and patterns. It captures and analyzes conversations, transcribes spoken words, and highlights both customer and agent sentiment.
- Talkdesk: Talkdesk Interaction Analytics is an AI-powered speech and text analytics tool that provides valuable insights into customer interactions, enabling call center agents to discover key conversational moments, topics, customer intents and sentiments.
- Tethr: Tethr's speech analytics platform provides brands with actionable insights from customer interactions. Its AI-driven speech analytics uncovers trends, patterns, and customer sentiments that can be used to automate call center Q&A.
- Cogito: This real-time AI coaching and guidance platform includes speech analytics functionality that helps contact center agents analyze customer interactions and sentiments and gives supervisors visibility into live conversations between customers and agents.
- Convin: Convin is an AI-backed conversation intelligence platform that records, transcribes and analyzes customer conversations across omnichannel contact centers.
- Five9: Five9’s contact center analytics software enables brands to analyze both voice and digital customer interactions to obtain valuable insights into both the customer experience and agent performance.
Daniel Ziv, VP of speech and text analytics, global product strategy at Verint, told CMSWire that contact center agents possess many skills, but one of the most important is the ability to sense customer emotion and provide empathy where needed. "However, in stressful situations where agents must deal with emotionally charged situations time and again, they can become overwhelmed and respond ineffectively to customer emotions."
Ziv suggested that contact center solutions such as speech analytics platforms can help agents by identifying specific moments of positive and negative emotion or sentiment. "Agents can build empathic connections in real-time and consistently impact the positive connection customers have with the brand."
Ziv referenced a 2020 Aberdeen market research study entitled The ROI of Real-Time Agent Guidance which indicated that contact centers that are using AI capabilities are able to provide a better customer experience, with a 10% improvement in customer satisfaction, an 8.8% improvement in their Customer Effort Score (CES), and a 10.5% improvement in customer retention. The report also indicated that by using AI technology such as ML and automation, contact centers are able to analyze 100% of all interaction data in real-time, which minimizes the risk of poor customer satisfaction.
Related Article: Contact Center Technology and Strategies to Keep Customers Cool
How to Choose the Right Speech Analytics Platform
Choosing the right speech analytics platform largely depends on the specific goals and requirements of the business. For comprehensive contact center functionality, it should include contact center evaluation as a standard feature and should provide insights into agent performance and customer interactions. It's also essential to choose a solution that goes beyond the traditional analysis of call content and common causes, providing insights into the accumulated data by identifying trends and patterns.
Additionally, customization and scalability are crucial for a solution that can be tailored to the specific needs of the business and can scale with the company as it grows. Integration with existing systems is another important aspect to consider, ensuring that the speech analytics platform is compatible with and can be easily integrated into the business's existing stack. “Whatever platform you choose, it must integrate into your tech stack,” said Broer. “You must be able to analyze speech and interactions across channels like phone, email, instant messaging, and more to pull the most accurate data.”
Vendor reputation and support should be taken into consideration by looking at their track record in terms of customer support and ongoing product development. Finally, cost-effectiveness should be considered when evaluating the costs associated with the speech analytics software, including the initial investment and ongoing maintenance costs, to ensure it fits within the company's budget.
The Challenges of Speech Analytics
Although improvements in AI technology have dramatically enhanced the ability of speech analytics platforms to effectively understand speech, there are still substantial challenges remaining. Accurately converting spoken words into text can be difficult due to factors such as accents, dialects, background noise, speech disorders and varying speech rates. Misinterpretations can lead to inaccurate analysis results. Not all speech analytics platforms have the ability to understand different languages or variations within a language. Additionally, understanding speech can be extremely complex due to factors such as sarcasm, irony, figurative expressions and cultural nuances.
Other challenges include the integration of speech analytics platforms with existing call center systems, which requires that the data exchange be seamless and that different software and hardware components are compatible. This may be beyond the budget of many businesses, as it requires significant investments in hardware, software and training.
Final Thoughts on Speech Analytics Platforms
Speech analytics platforms help businesses more fully understand customer interactions, sentiments and emotions. By using AI and NLP, these platforms enable brands to provide service agents with the tools they need to accurately engage with customers, more easily resolve their problems, and leave them feeling emotionally satisfied.