VoiceSense on Technology Signals: Cracking the predictive speech analysis code
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VoiceSense on Technology Signals – Cracking the predictive speech analysis code

Cracking the predictive speech analysis code

By Merilyn Pereira
January 3, 2019

“Most predictive analytics still focus on demographics and customer history; however, every decision we make is strongly affected by our behavioral tendencies. I believe we’ll see more and more approaches to capture and include the behavioral aspects of people into the big data predictive analytics; this is what we’re trying to do.”

VoiceSense has the lead in the predictive voice analytics space, and we have the Founder and CEO, Yoav Degani, share his insights into the field. In this interview, he takes us through the technology behind predictive voice analytics, the pain points, the future and how businesses can prepare for voice analytics. He also shares the metrics to track ROI, and the overall future of BI and predictive analytics.

Let’s start with what you mean by predictive analytics through speech. What is the technology powering your capability?

Predictive analytics through speech is an objective and accurate way to forecast individual behavior. It determines a person’s speech patterns and links these patterns to typical behavioral patterns, thus providing behavioral personalization. Understanding these behavioral tendencies enables the technology to anticipate future behaviors of that person in different areas such as their consumption, work culture, social habits, entertainment, healthcare and more. This analysis focuses on prosodic voice patterns, which are the patterns of a person’s speech, such as pace, intonation and emphasis. These speech patterns are language- and culture-independent, and ensure objectivity. The patterns can be analyzed during a real-time phone conversation to provide actionable guidance or offline on recorded calls for classification purposes.

What are your customers telling you? What typical objections or pain points do you hear from potential VoiceSense users when it comes to them deploying this sort of voice analytics platform?

Organizations worldwide want to make sounder decisions. Here is a common list of examples we hear from customer engagements:

  • Enterprises want to understand prospects’ tendencies and predict behaviors in different scenarios, such as purchase behavior and loyalty style — is the person ready to buy or just kicking the tires? Enterprises also want guidance on how to proceed with sales opportunities — is the person price conscious or brand conscious? This approach results in increased sales by optimizing the sales staff’s work hours, leaving them enough time to focus on the best prospects, anticipating their needs and adjusting their approach.
  • Banks and insurance companies require an assessment of the behavioral risk associated with their prospects — are they impulsive, risk-takers or conservative? They use this analysis to predict loan default, collections success, future claims and to improve their decision-making accordingly.
  • Human resources also need this technology for recruitment, assignment and employee management.
  • Healthcare use cases involve patient tracking and mobile health as well as population screening for risk groups.

We live in a multi-channel, multy-device world that generates so much qualitative data. How would a business leader map and connect the voice-based analytics you provide to all the other sources of qualitative and unstructured data?

The integration is straightforward; enterprises communicate with their customers and their prospects. VoiceSense integrates the streaming audio in real time to the call center and provides targeted go/no-go indications to the agents on when to initiate an upsell script, retention script, risk assessment script and so on. Moreover, our data is stored in the enterprise’s CRM and incorporated with the rest of the data on customers and employees.

How can an organization leverage this capability responsibly? What questions should the technology or business leaders be asking about data security, considering most employees would have access to this BI/analytics/reports? Especially in industries like healthcare or verticals such as HR, which have strict regulatory environments?

We have two touch points with the organization, the technical structure — receiving the audio input, which is usually from the call center, and then returning our analysis data into the enterprise databases. We provide APIs for both interfaces and support cloud-based deployments as well as on-premise installations. We are fully aware of and sensitive to all data security and privacy concerns and of course, compliant with the regulations and privacy policies required in each country and company.

The world is certainly moving to voice, voice-based everything – from search to IoT commands – is something on the agenda for all business leaders. What should a CEO be prioritizing when it comes to developing an IT strategy or even readiness for the voice-driven future?

Indeed, voice is becoming an increasingly important part of how organizations interact with the external environment. The vision is that any interaction, whether with customers, prospects, candidates or even in internal meetings, holds significant information that could improve the business through speech-based AI.

As such, the CTO must be prepared to see voice and speech analysis as part of the office and computational landscape. Repercussions on the physical network, on privacy and on security will challenge the CTO, who will be tasked to ensure accessibility to any such audio interaction, enabling these technologies to run seamlessly.

What metrics could a business use to evaluate whether the analytics platform is delivering the outcomes/ROI they need?

This is an important point — the data in existence is endless; and without a practical approach, enterprises can sink in different analytics types. We believe that analytics should first be validated versus the stated objectives, with clear business criteria and ROI attached to the project. Usually, it takes time to see that the outcomes are as expected. In VoiceSense’s case, we overcome this problem by applying a proof-of-concept on mature speech data — historical calls where the outcome is already known to the enterprise — so that we can prove the effect of our predictions concerning ROI, before a client integrates or purchases the system.

Data visualization seems like a more user-friendly way to present insights and analytics to non-tech functional leaders. What have you learned about data visualization over the last few years? What works and what doesn’t? What innovations have you seen in the data visualization space that are of interest?

Data visualization is certainly an important factor in turning hidden data into actionable business intelligence. In real-time operations, dashboards are the leading trend — finding ways to take the small bit of information, integrate it with the larger picture and hand it over instantly to a decision-maker. In our case, we turn our predictions into real-time go/no-go visual indications to guide a representative within an ongoing interaction.

What technologies or trends in the BI, predictive analytics and voice space are you tracking as we head towards 2020?

Analytics is certainly a key area. Organizations are just starting to touch the edge of the huge ocean of data. Data collection is difficult; however, turning the collected data into valuable procedures is even more complex. One important direction is integrating data from different sources to create the big picture. Another crucial direction, in my view, is capturing the behavioral aspects of business interaction.

Most predictive analytics still focus on demographics and customer history; however, every decision we make is strongly affected by our behavioral tendencies. I believe we’ll see more and more approaches to capture and include the behavioral aspects of people into the big data predictive analytics; this is what we’re trying to do.

RIQ: Thank you for this very enlightening conversation, Yoav. We learnt a lot and we hope to talk with you again, soon!

About Yoav Degani

Yoav Degani is the founder and CEO of VoiceSense. Yoav has combined his experience in clinical psychology and signal processing for intelligence systems to develop the company’s flagship behavioral speech analytics solution. Yoav holds an MA degree in Clinical Psychology and a BA degree in Psychology and Education, both from Tel Aviv University.

About VoiceSense

VoiceSense leads the behavioral predictive speech analytics market, letting organizations improve their decision-making, using speech-based personalization. Our language-independent approach analyzes voice patterns, links these patterns to behavioral characteristics and provides accurate predictions of future behaviors. This approach is used by organizations worldwide for customer analytics, finance, call centers, HR, healthcare and digital assistants.

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