An Israeli provider of voice-based technology has recently launched an innovative new tool. The Voicesense application analyzes speech in real time and uses AI algorithms to provide an instant profile of the individual’s behavior.
Yoav Degani, founder and CEO, explains the technology can be employed to predict a calling customer’s buying preferences, for example.
We can not only show that a call is positive or negative, but we can reveal a much more sophisticated pattern. We can predict or assess if someone tends to take risks and guide the agent on whether they might purchase and, if so, what their buying style would be. We can also show if the customer is focused on price-value ratio, quality, brand or innovation, perhaps
Voicesense spent several years researching voice-based analytics solutions; correlating different speech patterns with behavioral tendencies and building databases.
Initially, it focused on generating complete personality profiles based entirely on the non-content features of an individual’s speech – intonation, pace and emphasis.
Now, says Mr. Degani, this has been taken further:
We can measure over 200 different parameters each second within the call and can generate the typical speech patterns of the individual. Today we can do that in real time, as well as offline: we can do it over the cloud or on the premises.
The technology can be integrated into a customer’s own system and connects back to Voicesense’s servers. It does not involve significant investment in hardware or infrastructure explains Mr. Degani.
We stream the audio in real time directly to our server for analysis and then present the data back to the users. We can analyze over 1,000 calls simultaneously on the same server in real time – another server calculates the predicative scores and we provide APIs back into the customer’s system. When the agents open their system they can see our analysis incorporated into their database.
Voicesense uses AI to build and update its predictive models, Mr. Degani says.
We apply machine learning techniques to our databases; we keep all the data from customers in our central database and once in a while update our models at the customer site. The system is learning, and we update the models, but we are trying to do that in a controlled way.
The technology is already being used in Israel, Europe, the US and the Far East. It is suitable for sales companies, where it can help recruit and train as well as increase purchasing, says Mr. Degani.
We can analyze agents as well, predicting who will excel at selling and who at customer service.
Fintech is another area of application, he stresses: in the world of banking, lending and loans, risk assessments are essential.
These companies have information about their customers (credit scores etc.) but they know little about their behavioral patterns. They do not know if someone is impulsive, takes risks or has integrity – and these factors play a critical role in someone’s financial decisions.
But the platform can have a wide range of applications.
In healthcare, for example, there is still no remote objective tracking tool like this. Someone with depression might need to see a doctor once a month so we are talking about huge resources. If we can analyze their speech patterns through their smartphone, upload the data to the cloud and send a report to the hospital or therapist, there could be significant implications.
This technology could become part of the big trends we are seeing in digital health.