SalesTech Star

SalesTechStar Interview with Rana Gujral, CEO at Behavioral Signals

Entrepreneur, speaker, investor, and CEO at Behavioral SignalsRana Gujral joins us in this interview to discuss the growing use and importance of new technologies like Voice and Emotion AI in finance and sales, catch the excerpts here.


Can you tell us a little about yourself Rana? Could you also tell us a little about Behavioral Signals, how is it especially useful to technology sales and marketing teams? 

I am an entrepreneur, speaker, investor and the CEO of Behavioral Signals.

Human communication is a complex process that depends on not only the words being spoken, but also the way they are expressed. At Behavioral Signals, we excel at unraveling signals from speech data with our proprietary deep learning technology. By capturing acoustic cues, intonation and other interaction signals, we discover emotions, behaviors and intent, empowering our customers with predictive behavioral modeling and business insights.

Traditional NLP and Voice Interaction offerings focus on ‘What’ is being said. We introduce the ability to understand ‘How’ something is being said in addition to what is being said. We understand human emotions, deduce speaking styles and assess behaviors from audio by focusing exclusively on tonality..

Read More: SalesTechStar Interview With Jake Sorofman, President At MetaCX 

How have you seen Voice and Emotion AI play a role in new innovations across the salestech / fintech landscape?

Emotion AI is still a novelty for Financial institutions. Conversely, we are seeing some intelligent implementations within Sales. We’re seeing a serious effort to incorporate voice assistants for customer service. We’re also seeing usage of some advanced capabilities such as voice biometrics to authenticate customers. Enterprises are utilizing AI for analytics, customer loan approvals, etc. Emotion AI is well suited not only for agent coaching but also helps agents real-time during live calls with alerts and cues on how the call is progressing. Deep Tech companies are building specialized tools on top of core engines geared towards specific use cases. For example, in a recent deployment at an EU-based bank, we leveraged our emotion recognition technology and advanced machine learning models to create behavioral profiles of agents and clients by analyzing their recent audio interactions. Using these profiles, we matched a group of clients that were better suited for a particular agent. As a result, the debt restructuring applications were increased by 20%, with 7.6% fewer calls. In absolute numbers, these results correspond to $1.5M upside per agent per year or a total of $300M annual upside for the bank! Sounds like magic, but it’s really just leveraging AI to understand people’s interactions and deploying these capabilities in a non-invasive and scalable way.

How according to you will newer innovations in these segments look like or evolve in the coming years, given the latest advances?

We’ll see AI solutions being integrated within many of the day to day business processes. This applies to both B2C and B2B businesses. This does not mean people will be replaced. It just means that they will have an advanced tool at their disposal that will help them make faster decisions. Analytics has shown us a way to improve our efficiency and decision making. AI can analyze tons of data and unravel insights that are typically impossible for a human to process. Humans are smart but have their limitations. When AI is combined with different modalities such as Voice, possibilities are endless. AI has the ability to screen thousands of calls in a few seconds where a human would need hours and hours to listen to them all. Leveraging deep learning, a company has the ability to process all customer interactions, learn and improve their services while offering new ones, such as banking without human presence (which is very relevant these days).

Read More: How Early-Stage Companies Can Approach Selling B2B

What are some of the top features tomorrow’s business to business companies will look for in their fintech and salestech stack?

Deep knowledge. Every person in a company needs access to information in order to do their job well. When you give a person all the information they need, they can perform better. That information could include customer background, history, needs and even desires. When is it a good time to reach out? How do you speak with a client? Or with which client should you be speaking to? Communication doesn’t really differ much between whether it’s with the consumer or a business. It’s still at the end of the day, a connection between real human beings and one that needs tools which can help sell or achieve an outcome. So we need tools such as AI, machine learning, and other technologies, that can help us capture and analyze data from not only all inside company channels, but also discover 3rd party public data that don’t belong to the company but is still relevant; such as what is your client posting on social media or how are they doing in the stock market; what did their CEO discuss on his last interview with WSJ, or what are they looking for on the market. One needs this information to be predictive and effective.

When it comes to the way B2Bs use salestech and fintech to ease an array of processes, there must be some challenges you’ve seen them face that affect the optimal use of their capabilities: can you talk about these?

Many companies are still struggling with digital transformation. They are trying to integrate different solutions that can help with basic day to day operations, from serving front-desk customers to analyzing data within their CRMs. Incorporating something as sophisticated as analyzing emotions and behaviors from tone of voice, or leveraging abilities that predict intent, such as will a customer buy or not buy or if a debt-holder will pay or not pay, is still ‘exotic’ in many company executives’ minds. And we get it. It’s not easy and no-one has done it before. A key challenge most deep tech companies face is with educating the market about the possibilities and helping people understand the capabilities of new tools at their disposal.

We’d love to know about some of your future plans, for Behavioral Signals / any other venture in sight?

Our particular focus as a company has been around deducing emotions exclusively from the voice aspect or the speech aspects to it and the way we do it is through our focus on the tonality, not just what you’re saying but how you’re saying it and the emphasis behind the words and the specific pitch and focus around how you’re emphasizing a few things.

Today, we use these capabilities to augment human-to-human interaction and deliver new use-cases and KPIs that optimize these interactions, such as: sales acceleration tools, first-call optimization via real-time feedback for agents, behavioral coaching and training, AI based audit engine etc.

In the future, we plan to model the dynamics of human-to-human interaction within human-to-machine interactions such as emotionally and socially aware voice assistants and social robots.

When it comes to virtual assistants like Google Assistant, Alexa and Cortana, we’re literally treating this inanimate entity as a human substitute. When we’re interacting with machines through voice, we need to understand how we interact with all humans. When I’m interacting with you and you’re saying something to me and I’m saying something back, I’m not just cueing on what you’re saying, I’m also cueing on how you’re saying it and trying to empathize with your cognitive state of mind, your feelings or the emotions behind the words you’re using.

Today, that interaction is missing between a human and a machine, and as a result, a lot of these interactions don’t really have superior user experience; they’re just very transactional. Our goal is to provide ability to these machines to be as good as humans when processing affect and the emotional state of mind so that they could be more relatable and have a much more user-engaged experience with a fellow human.

Listen In -> To the Latest Episodes of The SalesStar Podcast!

Tag (mention/write about) the one person in the fintech or salestech industry whose answers to these questions you would love to read!

Steve Kirsch is doing some amazing work with digital money, cross border payments and improving dated banking processes. I’d love to have him answer these questions.

Your favorite FinanceTech / Salestech quote

“At the end of the day, customer centric fintech solutions are going to win” – Giles Sutherland.

Would you like to share specific finance or business tips for Marketing and Sales teams struggling through this uncertain time due to the global pandemic?

It’s a great time to think about and discover new ideas. Innovation is birthed from crisis. This is the opportunity ahead of us.

Behavioral Signals (Behavioral Signal Technologies, Inc.) develops technology to analyze human behavior from voice-data. Using the Oliver API for Emotions & Behavior Recognition, their flagship product, enterprises can track emotions and behaviors in conversations and get a complete view of related key performance indicators.

Rana Gujral is an entrepreneur, speaker, investor, and CEO at Behavioral Signals, an emotion AI, and behavioral recognition software company. Rana has been awarded the ‘Entrepreneur of the Month’ by CIO Magazine, the ‘US-China Pioneer’ Award by IEIE and listed in “Top 10 Entrepreneurs to follow in 2017” by Huffington Post. He is also listed in Inc. Magazine as an “AI Entrepreneur to Watch”. His writing has appeared in publications such as Inc., TechCrunch, and Forbes.