Easily transcribe every interaction, analyze sentiment and agent empathy, and spot key topics. Speech and text analytics transforms customer conversations into an essential resource for improving customer experience (CX).
Protect your business and agents. Quickly scale quality assurance and compliance by checking for interactions with topics or customer behaviors that need escalation and further analysis.
Separate the signal from the noise in interaction data and see what drives customer and employee behaviors. Understand why some interactions lead to better outcomes and spot recurring trends.
Get complete and accurate transcriptions of every interaction. Turn all customer engagements into data that you can use to improve their experiences and your business outcomes.
Analyze voice and digital transcripts with artificial intelligence (AI) and discover new topics or phrases of interest. Optimize your processes, campaigns and service based on insights. Bolster the performance of topic spotting and tagging features.
Get a 360-degree view of every interaction. Search through conversations to uncover key insights, including underlying issues, customer sentiments, agent performance and more.
Easily visualize and understand customer topics and sentiment trends by agent, queue and flow. Search and filter data based on your chosen parameters to get simple-to-use and actionable insights.
Genesys is the first in the industry to help measure agent empathy with AI. Identify more personalized coaching and learning opportunities, sharpen emotional intelligence, and foster stronger customer engagement.
Examine interaction language and gain deeper customer understanding. Get a better grasp of customer attitudes and perceptions, providing vital data about your product, service agents and areas needing enhancement.
82%
of organizations actively transcribe speech data. Yet two-thirds of them still use less than half of their available audio assets for business objectives.
Opus Research, 2023
Keeping up with increasing inquiry volumes can be difficult. With Genesys, you can quickly analyze text and voice interactions, no matter the channel or volume of interactions. We provide actionable, detailed analytics so you can share insights across your organization with ease.
Take control of customer experience and service by understanding what customers think of your brand, whether positive, negative or neutral. Quickly search, segment and identify trends and insights that can positively impact processes and operations. Then apply your findings. For example, quickly improve quality management or gauge business performance based on changing customer opinions.
And take it a step further. With Genesys, you can measure your agents based on how empathetic they are across interactions. This data allows you to operationalize empathy across your business. With this ability, you can elevate emotional intelligence to achieve loyalty and growth.
Speech and text analytics from Genesys integrates a robust suite of interaction analysis features into a single application. This eliminates the need for disparate tools, making it easy to gather valuable interaction insights across voice and digital channels. It also enriches quality management, coaching and agent gamification. Supervisors gain insights that empower them to make data-driven decisions that uplift the service and CX your organization provides.
Prospects and customers already contact your business across channels. And, as the number of digital channels grows, so does the complexity of your customer data.
You need tools that can do the hard work of making sense of the data. That means having one application to identify your customers’ needs, agents’ behaviors, business trends and more. Get a better understanding of all conversations — across all channels — in exactly the same way.
Say goodbye to random sampling, manual selection and interaction review processes. With an integrated system, you can identify and group analysis topics with ease. Optimize your customer sentiment analysis efforts.
Using the ability to visualize insights, you’ll learn how customers feel and how agents perform over time using AI. Identify important data for each segment of your company, and share that information with less effort.
Genesys elevates the value of transcription by harnessing AI analytics engines. With the support of our sentiment analysis tools, transcripts aren’t just clearer — they’re actionable. Machine learning, paired with our topic-spotting capability, allows you to see gaps in customer journeys.
Our all-in-one platform enables swift adaptations. That can be through updating knowledge articles, deploying conversational bots or fine-tuning coaching modules. Move beyond mere quality review, and let data transform your CX and employee experience (EX).
Plan and execute customer and employee experience strategies powered by AI-driven speech and text analytics. Unlock the full potential of your interaction data, extracting valuable insights from every conversation.
Share these actionable insights across your organization, reaching departments as diverse as legal, marketing, HR, development and sales. Add flexibility to your analysis with reporting you can customize. You also have the option to export your interaction data through APIs. Then harness these insights to fuel productivity, make informed decisions, drive business growth and forge meaningful customer connections.
Modern consumers have endless buying options. Providing good customer support is key to setting your business apart. See how you can improve your call center sentiment analysis software with text and speech analytics tools from Genesys. Schedule a demo today for better online reviews tomorrow.
We’ll contact you directly to set up a date and time that works with your schedule.
Contact center data analysis uses a collection of data from diverse sources. These include digital channels, calls, chats and emails — and deploying analysis software to interpret it, preferably in real time. Key metrics like average handle time, first-call resolution, and positive and negative sentiment scores can reveal areas that need improvement.
Speech and text analytics is a set of features that uses natural language processing (NLP). It automatically analyzes content immediately after the completion of an interaction.
This analysis gives businesses insight into customer-agent conversations. It includes the transcription of voice interactions and analysis of customer sentiment and topic spotting, thereby creating meaning from otherwise unstructured data. Organizations can use this data to fulfill use cases around agent performance improvement, compliance, customer satisfaction and business intelligence.
Voice and digital transcriptions capture conversations between participants 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. The internal participant can be an IVR, voicebots, ACD, agent, conference or voicemail.
For digital interactions such as email, message or chat, the internal participant can be bots or agents. This information provides insights for improving employee training and feedback, and to identify business problems.
Speech analytics employs technologies, like automatic speech recognition to transcribe audio into text. It also uses natural language understanding for contextual comprehension and sentiment analysis of the transcribed text.
Sentiment analysis is the process of understanding a customer’s experience during an interaction. It’s based on the language used during an interaction. The analysis uses the transcript generated from the interaction.
By capturing the sentiment of the customer’s phrases, users can gain valuable insight into the customer’s feedback and experience. Use this information to improve service delivery.
Empathy analysis is the process of understanding agent behaviors during an interaction through conversational intelligence. Generative AI develops an interaction transcript. 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 key aspect of experience management. It gets this score by analyzing customer interactions. The result reveals an understanding of 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.
To improve sentiment scores in a contact center, start by using the best sentiment analysis tools on the market. Train agents to respond to customer calls with empathy to their sentiments, such as repeating back what the agent heard. And apply text-mining techniques to customer interaction transcripts.
A good sentiment analysis tool should be able to accurately determine sentiment (positive, negative or neutral) and intensity. It should support the languages you need 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 detection and trend analysis. Integration capabilities, usability and good support are also important in a sentiment analysis tool.
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 tools.
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.
