These days, even if a customer care associate fails to notice that a caller is frustrated—the software analyzing the call doesn’t. As automated solutions like IVR and chatbots answer simple questions for customers, organizations are using AI-powered voice and speech analytics to gain insights into the more complicated phone conversations that fall on customer care associates to resolve.
But while both voice and speech analytics provide insights into the voice of the customer (and are sometimes referred to interchangeably), their methods are different. We’ll explain what those differences are, the technology trends that impact them, and how to decide which analytics solution is right for your business.
Speech versus voice analytics
Speech analytics technologies are centered on the content of a conversation. The solutions are designed to analyze what was said by customers and associates, and in what context. This is achieved by converting speech into text or through phonetic indexing. The result is a searchable database of what associates and customers said during the conversations.
A customer, for instance, may call a support line to complain about an erroneous charge on a bill. By executing a search for words spoken by the customer such as, “inaccuracy,” or “unexpected fee” along with a search for the associate saying “resolved” or “let me connect you” the customer’s reason for calling can be identified, as well as whether the customer received an answer to his or her question. By strategically pinpointing words and phrases used during a conversation, speech analytics help businesses make more informed decisions to create the best customer experience possible.
In contrast, whereas speech analytics analyzes what was said in a conversation, voice analytics focuses on how it was said. Voice analytics homes in on and scores certain qualities of a speaker’s voice, such as tone, pitch, and tempo to form an assessment, such as measuring the customer’s mood.
For example, a customer may use the word “fantastic,” but by recognizing cues, a voice analytics solution can determine that the customer is actually unhappy. The associate may then receive an alert on how to respond appropriately.
“The best solutions not only make it easier to extract more accurate insights and drive instant action through AI, but they also extend those capabilities by integrations with other systems,” according a Forrester report on AI-fueled speech analytics.
The research firm notes that AI has significantly ramped up the capabilities of speech analytics solutions, such as by improving speech-to-text accuracy, insight generation, quality management, and measurement of tone and sentiment. Indeed, there are multiple ways for companies to implement and leverage the advantages of AI-powered speech and voice analytics. Here are a few examples:
- Fraud detection: AI combined with speech analytics tools can be trained to assess and score calls for potential fraud and alert associates before they release confidential customer information.
- Mitigate liability risks: Using real-time speech analytics, associates can receive reminders about required disclosures that they must share before ending the call.
- Greater insights: Some solutions use deep learning—a type of AI that utilizes neural nets to deliver better insights—for emotion detection or to extract insights from speech.
- Predict NPS scores: Integrating NPS results with predictive and speech analytics allows companies to predict customer responses.
- Guided recommendations: Using real-time speech and/or voice analytics, associates can receive guidance to recommend relevant products or offers to the customer—or not, depending on the customer’s mood and the nature of the call.
- Associate training: Similarly, real-time voice analytics enables managers to gauge whether a customer is upset during a call and provide a new associate with immediate coaching or support.
- Self-managed learning: Giving associates fast access to analyses and insights about their call performance speeds up the learning and training process. Associates can track their own performance and make adjustments.
Which VOC solution is the best fit for your business?
Now that you have a better understanding of speech and voice analytics trends, you may be wondering which one would be the best fit for your company’s needs. Identifying your business needs and the scope and goals of the project will help determine which type of solution to pursue. Here are several key questions to consider before meeting with a vendor or consultant:
- What is the volume of calls that you want to analyze?
- What do you hope to gain? For example, are you trying to understand the caller’s temperament or reason for calling, or both?
- Are you looking for a tool to monitor script compliance?
- Do you plan to integrate the findings from voice calls with other analytics, platforms, or channels?
- How will the project’s success be measured?
Contact center associates have access to more customer data than ever before. Combined with artificial intelligence, voice and speech analytics make it possible for associates to provide callers with the right information at the right time—and even predict the customer’s reaction. And while there are many speech and voice analytics tools to choose from, the best ones are unnoticeable. They allow associates to focus on what they do best, engaging customers in a human conversation.