Voice analytics is a powerful and insightful process that can yield immense benefits for any organization that chooses to employ it. With voice analytics, your business can extract valuable information about the behaviors and emotions of your customers and even your employees. Armed with this knowledge, your business can take steps to become more competitive and more efficient. How can such knowledge be gained? Simply by paying closer attention to the conversations happening every day within your organization.
Voice analytics is the process of analyzing recorded conversations, such as phone calls or digital teleconferencing meetings, to gather information about one or multiple participants in the conversation. The information and insights drawn from such analysis can be simple and straightforward or multi-layered and complex. For example, a surface-level analysis of a phone conversation between a prospective customer and a sales agent may reveal that the call lasted for 5 minutes, the agent spoke for 4 minutes and 15 seconds while the prospect only spoke for 45 seconds, the agent did not mention the current sales promotion, and so on. However, a more nuanced and sophisticated analysis—one driven by modern technology—could reveal the prospect's emotional state and develop a personality profile for them and then predict their likelihood of converting to a paying customer.
In its infancy, voice analytics was a process that still required a great deal of human brainpower. Early speech recognition software could produce an accurate transcript of a conversation. However, it was still dependent upon the managers and analysts to read the transcript and manually extract any valuable information. However, as years passed, computer software evolved to the point where machines could both produce and analyze the transcript. Artificial intelligence and machine learning have taken voice analytics to a whole new level today.<blog-alt>
Artificial intelligence and machine learning add an exciting new layer to the voice analytics process, producing deeper understanding, more significant insights, and faster results.
Natural language processing, for example, has enabled computers to process language in the same way, humans process language. This is essential in the field of voice analytics, as the goal of the whole analytics process is not to simply review the words being used in a conversation but rather, to understand the true meaning and intent of the conversation. There is nuance and subtlety to human conversation, such as changes in tone or speed, which can reveal the speaker's intent or emotion in a way that the words they are using cannot. While humans are well equipped to detect these nuances, early computers were not. With natural language processing, modern technology is much better equipped to identify the true meaning behind the words being used in a conversation. This generates a deeper understanding of the conversation being analyzed.
Machine learning can also now automate the voice analytics process, reducing the need for the human-driven and time-consuming manual review of conversation transcripts. An algorithm could instantly detect key words, phrases, or topics in a conversation. Suppose a business wanted to know how often a particular product was being discussed, for example. In that case, the algorithm could be programmed to detect that product name and analyze the discussion surrounding that product. Furthermore, that conversation can be instantly compared against an entire database of similar conversations. This allows for real-time insights into the trends and patterns happening within an organization: the common questions, frequent complaints, and hot products being discussed can be identified quickly and accurately. Armed with this type of data, a business could make time-sensitive changes to their sales strategy, reconsider product offerings for the future, address personnel issues, and more. The competitive potential offered by such real-time analysis is limitless.
Another powerful application of artificial intelligence in the field of voice analytics is the ability to build models of predictive behavior. At VoiceSignals, we utilize artificial intelligence and machine learning to gather People Intelligence for our clients and predict the future behavior of their customers. We accomplish this by first reviewing recorded conversations and calculating hundreds of speech parameters. Then, our algorithms use speech parameters to identify each speaker's emotions, sentiments, and personality traits. Psychologists and engineers have trained and validated these algorithms, which brings a layer of advanced psychological science into the voice analytics process. With the emotions and personality traits identified, we can then predict the future behavior of the speaker.
VoiceSignals recently partnered with Refi.com, a mortgage loan refinancing company, to quickly identify their top-converting prospects with our machine learning algorithm. Click here to read the case study. We also recently worked with Ideal Health Benefits, a rapidly growing health insurance agency. Armed with the insights from our People Intelligence Platform, they have the opportunity to increase their average prospect conversion rate from 15% to 23%. Click here to read the case study.
Voice analytics is an essential tool for any business's toolkit. With modern advancements in machine learning and artificial intelligence, the insights delivered by voice analytics are more powerful than ever. And the good news: these insights are also easier to obtain than ever before, thanks to our People Intelligence Platform. Is your business ready to see what customer insights VocieSignals can provide for you through the power of voice analytics? Schedule a demo today.