Turn interaction into insight with speech and text analytics
Your customer interactions provide a wealth of knowledge that can be used to enhance the customer experience and refine strategy — but only if you use it. Leverage the power of artificial intelligence (AI) to extract crucial insights from all the vast data that you gather every day.
Native AI automatically transcribes interactions, analyzes sentiment and agent empathy, and identifies key topics.
Leverage AI to automate your processes and scale QA effortlessly by pre-answering quality and performance evaluations.
Know what drives customer behavior, identify trends and use AI-driven data to improve customer experience — and boost business outcomes.
Tour the speech and text analytics tools to see how they detect customer emotion, provide sentiment analysis and more
Genesys Cloud™ infuses powerful interaction tools within a single platform, making it easier to gather insights across all your channels. This simplifies data collection and also makes quality management, coaching and agent gamification simpler to drive. Giving supervisors insights they can easily act on empowers them to make data-driven decisions to improve both service quality and customer experience.
With the Genesys Cloud platform, you can make sure you’re optimizing for customer satisfaction with native AI-driven speech and text analytics, and you’ll be able to understand each interaction to improve service quality and agent performance alike.
Genesys Cloud leverages AI to bring together customer interactions across all channels in one place. You can identify customer needs, agent behaviors and business trends with ease.
Learn how customer sentiment and agent performance evolve over time. By combining AI-powered and manual review processes in one place, agents and supervisors stay a step ahead.
Leverage native AI to get more accurate transcripts that improve over time. You can manage your dictionary, so industry-specific terms, product names and business jargon can be included manually.
AI-driven analytics unlock valuable insights from each and every interaction. Customizable reporting and the ability to export data through APIs help tailor your analysis to meet your needs.
Keeping up with an ever-growing volume of customer inquiries across all your channels can be challenging. The Genesys Cloud platform makes it easier with native artificial intelligence. You get access to powerful conversational analytics and insights to more deeply investigate text and voice interactions. Share insights easily across your business, giving you the ability to better identify trends, uncover underlying issues and make data-driven decisions fast.
Understanding how your customers feel is key to delivering the best service. Get the tools to take control of your customer experience by analyzing customer sentiment — positive, negative or neutral. With just a quick search, you can group and identify trends that have a major impact on your business. This way you can easily enhance quality management, boost employee knowledge and find areas of opportunity for other business areas to drive growth outside the contact center.
Genesys Cloud also allows you to measure your agents’ empathy across all their interactions, elevating emotional intelligence across your organization. By fostering empathy, you can begin to build stronger customer relationships, driving loyalty and more sustainable growth.
With AI-driven Genesys Cloud capabilities, it’s easy to make strategic decisions based on the data you gather from every interaction. It quickly understands sentiment and meaning behind every input, and provides you with the information you need to be agile.
Accurately transcribe each customer interaction with native AI translation in over 70 languages to support global businesses without losing context.
Generate AI summaries for supervisors with key insights like reason for sentiment and on-demand translation to further streamline quality processes.
Analyze voice and digital interactions with AI and find emerging topics — which you can then use to uncover root causes, identify trends and improve how you handle recurring issues.
Prefill evaluation forms using AI, allowing supervisors to be able to manually adjust rather than having to start from scratch, saving time and effort.
Identify helpful and unhelpful behaviors and provide personalized coaching, driving improved customer engagement and fostering stronger, more meaningful connections with customers.
Native AI analyzes every interaction, gaining insights into customer needs and issues to enhance products, services and agent performance based on the customer experience.
The consumers of today have nearly endless buying options. Providing good customer support is key to setting your business apart. See how you can improve on your current call center sentiment analysis software with AI-powered text and speech analytics tools in Genesys Cloud.
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Contact center data analysis involves collecting your customer data from as many sources as you have access to. These include digital channels, calls, chats and emails — and using analysis software to interpret all that gathered data, preferably in real time. This analysis is made much easier by having access to AI, which can analyze data faster and more efficiently. Key metrics like average handle time, first-call resolution, and positive and negative sentiment scores can reveal where your agents and supervisors need to improve.
Speech and text analytics is a set of features that automatically analyzes content immediately after the end of an interaction. It transcribes your interactions, identifies the topics and categories that are covered, provides sentiment analysis and more.
This analysis gives you insight into your customer-agent conversations. Speech and text analytics includes the transcription of voice interactions, as well as customer sentiment analysis and topic spotting. That helps to create meaning from otherwise unstructured data, especially when AI is integrated within your analysis platform. You can use this data to fulfill use cases around agent performance improvement, compliance, customer satisfaction and business intelligence.
Voice and digital transcriptions capture conversations to understand who is saying what. This includes external (customer) and internal (flows or agents, for example). For voice interactions, a transcription engine transcribes the audio into the written word. The internal participant can be an IVR, a voicebot, ACD, agent, conference or voicemail.
For digital interactions such as email, message or chat, the internal participant can be a bot or an agent. This information gives you insights into how you can improve employee training and feedback, and it helps you to identify business problems.
Speech analytics employs technologies like automatic speech recognition to transcribe audio into text. It also uses natural language understanding to comprehend what’s being said by both sides, and to do sentiment analysis of the transcribed text.
Sentiment analysis is the process of understanding the quality of a customer’s experience during an interaction. It is based on the language used by the customer during each interaction. It uses the transcript generated from the interaction as a basis for analysis.
By capturing the sentiment of the customer’s words and phrases, you can gain valuable insight into positive and negative customer feedback and experience. In short, you can see whether customers are having a positive or negative experience. You can then use this information to improve service delivery.
Empathy analysis is the process of understanding agent behaviors during an interaction through conversational intelligence. Are your agents doing a good job of understanding customer needs? Are they genuinely being helpful? With agent empathy, you can gain deeper insights into interactions and agent language; personalize performance and learning; and deepen customer relationships.
A customer service sentiment score is a measure of the feeling customers have after interacting with your customer service team. You can get this score by analyzing customer interactions — are customers friendly, or do they become aggravated when being served? The result reveals their opinion of, and emotional response toward, a product, service or the brand sentiment overall. Calculating an overall sentiment score and an overall sentiment trend for the interaction requires the use of all sentiment values, including all brand mentions.
To improve sentiment scores in a contact center, start by using the best customer sentiment analyzer available to you. The better data you have, the more likely it is you’ll be able to identify and solve issues. Train agents to respond to customer calls with empathy, including actions such as repeating back what the agent heard to make sure they’re responding to the right issue. And apply text-mining techniques to customer interaction transcripts to gain deeper knowledge of what customers are saying and what that means for you.
A good tool should be able to accurately determine sentiment (positive, negative or neutral) and intensity. It should support the languages you work in and be able to handle variances, such as slang, abbreviations and misspellings. It may also offer features like entity recognition (e.g., identifying people, places and brands); emotion and intention detection; and trend analysis. Integration capabilities, usability and good support are also important in a sentiment analysis feature.
The “best” method for sentiment analysis can depend on the specific task. Machine learning methods, especially deep learning, can be powerful but require a lot of data and computing resources. Rule-based methods can be effective for simpler tasks or when resources are limited. Hybrid approaches that combine machine learning and rule-based methods can often provide a good balance.
Speech recognition transcribes spoken words into text. NLP interprets and generates human language. NLP, in particular, is integral to advanced sentiment analysis capabilities.
No, NLP is not the same as sentiment analysis. NLP is a broader field that involves using computers to understand, interpret and generate human language. Sentiment analysis is a specific application of NLP that involves determining the sentiment or emotion expressed in text.
Speech analytics and voice (or acoustic) analytics work together to provide a holistic view of the content and quality of interactions. Speech analytics focuses on what is being said, the words and phrases that are used by both agent and customer. Acoustics analytics focuses on overtalk, silence and total talk time. Using both allows you to analyze what customers are asking for, how they’re getting help and how agents and customers feel.
Yes, speech and text analytics can help identify ways to improve how customer service teams interact with customers. Examining what’s said during each interaction allows you to locate correlations between certain actions and positive or negative outcomes, which means you can accentuate the positive and eliminate the negative more easily.
Speech and text analytics can sometimes struggle with non-American English, but with time this has improved. Businesses around the globe have seen success using speech and advanced text analytics to examine and improve their customer service interactions.
