VoiceSense on AiThority: Interview with Yoav Degani, Founder and CEO, VoiceSense
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VoiceSense on AiThority – Interview with Yoav Degani, Founder and CEO, VoiceSense

Interview with Yoav Degani, Founder and CEO, VoiceSense

By Viraj T
December 5, 2018

Voice analytics leverages artificial intelligence to provide behavioral personalization and anticipate future behavior.

Know My Business

Tell us about your background in Analytics and your connection to Voice technologies?

My background in clinical psychology along with my extensive practical experience working with signal processing technologies for defense and intelligence industries have enabled me to connect these two disciplines and develop an expertise in analyzing the characteristics of personality, emotions and interpersonal communications through the power of voice.

Would it be correct to say that Enterprise applications of Voice Analytics have matured more than Sentiment Recognition in recent years?

Yes, this is a correct statement. Whereas sentiment recognition refers to current, momentary emotions – happiness and enthusiasm or anger and frustration or sadness and disappointment or contentment and satisfaction — behavioral voice analytics goes much further. New behavioral Voice Analytics providing the behavioral tendencies that characterize the person beyond the current moment, namely, a profile of the personality of the speaker.

How is AI interlaced with Voice Analytics and what benefits does this combination offer to tech users?

Voice analytics leverages artificial intelligence to provide behavioral personalization and anticipate future behavior. Let’s take banks as an example: banks have a lot of information about their customers – credit score, financial strength and so on. However, they know very little about the customer’s behavioral tendencies.

We use voice analytics AI to tell the bank that the customer may tend to be impulsive, to take higher risks, to have lower personal integrity, or on the other hand, the customer tends to follow rules, be reliable, consistent and so on. Based on such classifications, the bank may attribute higher or lower risk to the customer when considering approving a loan or when choosing a collection practice on a debt.

Similarly, an enterprise can use this behavioral personalization to predict the probability of online buying, assess buying style, loyalty patterns and more, and adopt matching upselling and retention techniques, Overall, the purpose is to improve the enterprise bottom line – increase sales, improve retention, reduce risk.

Is this approach to AI in Voice Analytics being incorporated into VoiceSense’s technology?

Most certainly.

The essential difference is that, unlike other voice analytics solutions, VoiceSense focuses on speech prosody: the non-content features of speech, like intonation, pace and emphasis. These speech attributes are physiological by their nature, which means that they are language independent, highly accurate, objective and difficult for speakers to control or manipulate.

The importance of building a personality profile and understanding the current state-of-mind is clear. How does AI help with achieving this, compared to existing data-driven methods?

Typically, data-driven predictive analytics relies mainly on demographics and history – data on consumption history, health history and so on. For example, a 35-year-old customer with a certain socioeconomic status living in a certain area with a certain family status is more likely to purchase certain products. The enterprise can generally use this predictive analytics process for marketing segmentation purposes.

But, behavioral speech analytics refers to how people sound and points to typical behavioral tendencies and especially to typical consumer behaviors — as reflected in the customers’ speech patterns.

By using speech analysis within predictive analytics, organizations gain much more information, enabling more accurate predictions. For example, a prospect may belong to a certain data segment that typically tends to have certain consuming interests. However, the behavioral speech analytics may predict that this specific customer has other interests or tends to respond positively to a specific sales approach or that she is currently interested or not interested.

How can VoiceSense help a business improve its marketing and sales initiatives?

Enterprises leverage VoiceSense’s behavioral predictive speech to understand customers’ tendencies and to predict their behavior in different scenarios. A common example is providing prediction scores to increase sales probability.

Scores are tabulated, while prospects are on the phone, getting real-time go/no-go upsell and retention indicators. Agents then can personalize offers. For example, if VoiceSense detects that a person is likely to buy and is price-focused, then agents can offer special deals. Marketing can be improved by using VoiceSense’s unique approach, of understanding the customers’ personality tendencies and using it to predict behavior/feedback to different marketing content campaign and proposals.

Are these voice-based interactions limited to call center activities or can VoiceSense’s technology be applied to additional sales and marketing situations?

Our analysis requires a speech sample from the customer. However, it can be a historical sample as well. So if the customer has spoken with the enterprise once, now the insights may be used in any future interaction with the customer without further analysis. These future interactions don’t have to be call center interactions – they can be from marketing campaigns, face-to-face interactions and so on.

Which verticals can leverage VoiceSense’s technology for their Marketing and Sales operations?

Almost any enterprise can benefit from our predictive analytics offering and it can be used to improve decision-making processes in all areas of an enterprise’s operations from Marketing, Sales, Service and Risk Management to Human Resources, Development and Production.

Fintech verticals, such as banking and insurance, typically want to use our technology to assess financial risk – future loan default, future debt collection, future claiming. Sales and retention use cases are relevant to almost any vertical. Human Resources want to use technology for recruitment, assignment and employee management. Healthcare use cases involve patient tracking and mobile health as well as population screening, specifically for risk groups.

What additional applications of AI could be incorporated into MarTech and Salestech platforms in the near future?

Virtual assistants are steadily becoming a part of people’s lives, whether in call center bots or in-home smart-speakers or in connected cars.

In the US alone, one in five adults today have a smart speaker and 75% of the population is expected to use one by 2021. AI is starting to be incorporated into these speakers, but still mainly focused on the conversation content – what a person says.

We certainly expect behavioral personalization to start playing a significant rule in such man-machine interactions.

Thank you, Yoav! That was fun and hope to see you back on AiThority soon.

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