Voice Analytics: What does your voice reveal about you?

If you could identify your employees’ or clients’ behavioral tendencies and predict how they will act and react, would that information be valuable to your organization?

What if you could do this using data you already have and are making more of every day?

The conversations that the people in your organization are having, amongst themselves or with external customers/clients/prospects, contain the often overlooked yet valuable data. The key to unlocking this data and making full use of its immense potential is voice analytics.

Imagine being able to discern whether a potential client is more likely to convert based on 15 seconds of them talking. Imagine being able to identify potential burnout in your employees in real-time. Voice Analytics makes this and more possible.

What is Voice Analytics?

Voice Analytics uses Artificial Intelligence and Machine Learning to identify a speaker’s personality and emotion from audio data—reading between the lines of words to reveal their actual sentiment and behavioral traits.

Where speech analytics focuses on what is being said, voice analytics focuses on how it is being said.

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The Applications of Voice Analytics:

Actions speak louder than words, and voice analytics considers hundreds of actions of speech such as emphasis, tone, pitch, energy, and more to provide insight into the speaker’s true emotional state. Inconsistencies between what is being said versus what is being conveyed are all too common. Take this imaginary exchange between a cable company customer service representative and a customer, for example:

CSR: Thank you for calling The Cable Company. How are you doing today?

Customer: I’m just great. My internet has been down for three hours.

CSR: I’m glad you’re doing great. Let me pull up your account. Can you tell me the last four digits…

In the context of this conversation, ‘great’ means anything but that. The customer is clearly dissatisfied, using sarcasm, and not happy. This is an easily detectable example of the inconsistency between what is being said versus what is being conveyed, but it is often far more subtle in reality.

Take this imaginary exchange between a manager and their employee, for example:

Manager: You’ve been working really hard lately, and I wanted you to know that I appreciate it. I just wanted to make sure that you are doing ok. You would let me know if you weren’t?

Employee: Yeah, I’m good. Busy, but good. Thanks for asking.

Manager: No problem, I mean it. I don’t want you burning out on us.

Employee: Yeah, that wouldn’t be good.

On the surface, this looks like a completely normal conversation. The words themselves don’t reveal any glaring red flags. If you think it would be helpful to hear the conversation instead of reading it, you’re validating voice analytics.

Because voice analytics focuses on how something is said instead of what is said, it eliminates confusion around the meanings of words or phrases. Identifying and understanding a speaker’s emotional state may sound like a degree of art is involved, but it is validated science. Under the direction of behavioral scientists and built on years of machine learning and artificial intelligence, hundreds of speech parameters combine to form a unique profile of a person’s behavioral tendencies.

(For more on the science behind voice analytics, read our article, People Intelligence: An Evolution in Business Intelligence.)

Predictive Voice Analytics:

Identifying and understanding a person’s emotional state is just scratching the surface and potential of this data. A People Intelligence Platform can extrapolate the emotional state and personality traits of a person to predict future behaviors. When you can predict future behaviors, you can influence key outcomes. The insights gained from predictive voice analytics can influence organizational decision making at multiple levels. Take this imaginary exchange between a person applying for a loan and a loan officer at a lending company.

Loan Officer: Thank you for choosing our lending company for your small business loan. How much money do you intend to borrow?

Loan Applicant: We think we need at least 60k to get started and to cover our expenses to launch.

Loan Officer: What is it that you are launching and how will you use the funds?

Loan Applicant: We are launching a dating app for people who love to cook. We will use the loan to acquire the domain, pay our developers, and hire an agency to help with marketing.

Loan Officer: What will you use as collateral?

Loan Applicant: I am prepared to use my primary residence to guarantee the loan, but I would like to avoid that, if possible.

Loan Officer: Can you provide detailed accounting of past or current loans, personal and business credit lines and accounts, and tax ID numbers?

Loan Applicant: Absolutely, I can send those to you today.

This hypothetical conversation is similar to thousands (if not more) of screening calls happening every day around the world. At first glance, it wouldn’t appear to provide any insights beyond the words being exchanged, but through the application of Voice Analytics, the behavioral traits—and therefore—blueprint of the speaker is revealed.

The conversation between the loan officer and applicant is similar to millions of interactions happening every day where true People Intelligence could help organizations make more informed decisions. Knowing someone’s emotions, sentiments, and personality traits can influence better decisions/outcomes and change the economic results at every step in your conversion funnel or sales cycle.

VoiceSignals’ People Intelligence Platform combines psychology, Artificial Intelligence, Machine Learning, and Voice Analytics to build people’s accurate personality and emotional profiles—simply by hearing them speak. Get to know your customers and employees on a whole new level and make smarter business decisions to transform sales cycles, mitigate risk, and achieve desired results—schedule a demo today.