‘In an exclusive interview, Rana Gujral, Chief Executive Officer at Behavioral Signals, discusses how the emotion AI tool developed by Behavioral Signals opens new opportunities for businesses by leveraging AI-mediated conversations that are customised and aligned with specific customer profiles.’
From technology to optimise the business processes for speciality chemicals with your startup TiZE to AI for analysing emotions at Behavioral Signals, what are the similarities and differences in your journey as a techpreneur?
Rana Gujral: I have had the fortunate experience to be a part of several iconic product journeys. At TiZE, we set out to improve upon archaic business processes for a very well-established industry. It was an opportunity to create immediate value in a sector screaming for innovation. The value creation was instant, and we enjoyed the rapid growth that came from that. At Behavioral Signals, we are looking out into the future and spearheading the transformation of conversational AI using emotion and behavioural science. We apply our new age behavioural AI to the existing Natural Language Processing (NLP) and Natural Language Understanding (NLU) technologies, and in the process, unravel experiences and capabilities that significantly improve human-to-human and human-to-machine interactions.
Whether you are a techpreneur or a tech leader, you have to stay focused on the ‘why’. The ‘why’ is your vision statement. It’s your North Star. Leaders may change, founding members may leave, and products may evolve, but the established vision guides your team forward. As a leader, it’s your job to articulate and own this vision. Building a product, solving a problem, or innovating a service category is not a ‘why, it’s a how.
What are some of the industry sectors that Behavioral Signals caters to?
Gujral: Our technology can be implemented in any industry that uses voice for communication Our current customers are in finance and sales. Behavioral Signals has developed an AI technology that, while being language agnostic, allows us to understand the underlying emotions in the flow of a conversation. Our technology is called AI-Mediated Conversations, or AI-MC for short, and it’s an automated employee-to-customer matching solution that uses Emotion AI and voice data to match the customer to the best-suited employee to handle a specific call. This match is based on profile data and our AI algorithms developed from years of research and experience in NLP and Behavioral Signal Processing.
Tell us how businesses benefit by integrating Oliver API into their existing systems?
Gujral: The Oliver API is an interface protocol that empowers enterprises to use our technology outputs and develop their custom software solutions. On the other hand, AI-MC is offered as a complete software solution that can be integrated directly over existing telephony software. Businesses don’t have to hire developers or machine learning engineers to integrate it, they just have to add it over their telephony system. We are actually now integrating with major telephony software providers such as Genesys, among others, to bring AI-MC to all companies via their marketplaces.
So, either a company uses our API directly or AI-MC via their provider’s marketplace, they have the same benefits. Up to now, communication with customers usually involved random pairing between employee and customer, regardless of a customer profile or employee skillset. Often that did not work well, causing frictions, diminished customer satisfaction, lost revenues. Regardless of the type of business communication, a sales call, support call, or a revenue collection call, it will always be an interaction between real humans, where rarely is the affinity identical between two pairs of people. We have specific behaviours and traits that help us get along with some people better than with others. This is where AI-MC comes in. It automatically matches each customer to the best-suited employee, using Emotion AI and Behavioral Signal Processing, in order to empower employees to perform better in each call.
Also Read: How Chatbots Can Enhance Customer Experience Using NLP
How do AI-Mediated conversations ensure enhanced privacy? What data sets are processed to ensure customers are paired with the right customer service agents?
Gujral: We don’t see privacy as enhanced or less-enhanced. For us, it’s absolute, and that means making sure no human can identify who the speaker is. In order to achieve that, we have in place very specific processes, both in how we handle data and which engineers handle this data. Each customer is different and has specific requirements that could lead to deployment on-premises or in the cloud. Data is always treated for the removal of personally identifiable information (PII), and we use strong encryption in all transfers. We are GDPR and SOCII compliant, and only specific certified employees have access to our customers’ data.
That is the data handling part, but the interesting part is how our technology works. By nature, it protects speaker privacy because it’s language agnostic. We listen and analyse how something is being said, not what is being said. To give you an example of how AI-MC works, in human terms, our brain can understand anger or enthusiasm when we hear someone talking in a foreign language, even if we don’t understand a word of what they’re saying. Our brains have evolved to grasp the emotional state of other humans, regardless of context. Similarly, AI allows machines to do the same thing just more extensively and in a fraction of the time.
What are the common challenges that some of your prospects aim to resolve with your tool? What industry sectors will emotion AI most impact?
Gujral: There are several aspects of business that AI-MC impacts, mainly customer satisfaction and revenue recovery. Working with optimal customer-agent behavioural matching means building great rapport between two humans, which can lead to less handling time and first-call resolution. Financially, AI-MC allows an enterprise to guide the conversation dynamic to increase collections or sales by predicting which customer-agent match will yield the best chance of a promise to pay or buy. Beyond the improved performance, positive conversations do end with satisfied customers and fulfilled employees. A business should treat each customer uniquely and provide them with the best service they can offer to achieve the results they want.
Managers can learn from their best-performing agents and focus on positive emotional and behavioural examples to guide everyone’s conversation dynamic and reduce employee attrition. We should not forget that a happy customer means brand loyalty and great word of mouth. Everyone wants to tell a good story of a conversation that went well and might lead to a good outcome without frustration and bad feelings. Good conversations accumulate a positive reputation and love for a brand.
What is that one leadership motto you live by?
Gujral: Manage the ‘now,’ but don’t lose sight of the ‘goal.’ To be a successful leader, you should be able to process vast amounts of data as you navigate the challenges at hand but continue to see the forest through the trees.
