Francesco Stasi: AI-Driven Insights From Historical Customer and Employee Conversations: The Next Frontier for Data-Driven Businesses

Awesome. Awesome. So I will say that, yeah, working in the chatbot, in the chatbot space with, with some very big enterprises, we, we got to understand that data. They have a lot of data, but somehow it's difficult to access this data and get a knowledge and insights out of it. So that's why this year over, over the summer, we, we deliver, we release the new product, which is called bot supply insights, which is the story that I, that I'm going to, to explain in, in the next, in the next slide, starting from the point that conversations are everywhere.

And especially in, in a COVID post COVID world, a lot of, a lot of these conversations have moved, have moved from what could have been, you know, a physical store to a digital store. We've seen the boom of eCommerce. We've seen the growth of, of delivery services, a lot of different industries and, and use case, which actually also exponentially grew the amount of conversations happening between brands, customers, and, and their own employees.

So, so when a, the, the, the main thing about, you know, machine learning, artificial intelligence and Chatbooks has always been data, right? The more data you have, the better systems you can create and deliver for, for your customers. Somehow in some, in some cases, the issue is not really whether you have data or not, but whether you're actually able to make your data talk. Okay.

And I just wanna share a case, a conversation that we had with, with, with one of our customers, they, and this is a very, very focused HR use case in the sense that this customer, they have two, 3000 conversations per day with, with their own employees across different 70 different countries. And, and the head of HR transformation was, was telling me, you know, Frances, it's not that we, we never wanted, we never wanted to build, to build a chat bot.

The problem is that I have so much data in so many different systems and these systems don't talk to each other that actually don't know where to start in building in building my chatbot. So this is the problem that we have seen in, in the industry. And this is the problem that we, that we are addressing now with, with bot supply insights, with the overall idea of thinking, what if your data could actually speak? What if your data could, could tell you things that you don't know already about what your employees want about what your customers want?

So, so we've been, we've been working on, on, on, on this platform that basically unifies and collects conversational data from different sources. And let me define conversational data. A conversational data set is, is basically an email, a phone call, a phone transcript, a live chat, existing chat bots, all of these interactions happening within these interfaces within this channels basically are basically conversational data.

So, so what we, what we are doing right now is, is a system that basically collects data from all these different sources. And then we use, we use very advanced, deep learning models that can extract knowledge and insights out of this data.

So, so you could have better insights about, you know, questions that your employees are asking. So you have a better understanding of what are the problems that your employees have, and you can now act on, on these challenges on these problems and, and solve these problems from a customer perspective, could, could also be useful to understand what problems they may have with, with your system, with your services.

And once we have collected all of these information, once we Dai have crunched analyzed and extracted insights out of, out of this data, then, then it's very much up to you how you want to use these, these new information that you didn't have access before we are, as mentioned before a chat bot, a chatbot company. So, so we tend to use this, this data to help our customers and to better identify in in less time, what are the, the topics that a chatbot should cover? Okay.

So, so let's give an example. If we understand that your customers are always complaining about delivery time. Okay.

But, but you didn't have that information before. Then we can use these insights to actually build a chatbot that automates in between 40 to 80% of delivery of the delivery requests that they have on a daily basis on, on a different use case could also be that it also be detached. So to speak from, from a chatbot application, just for, for you to get better insights and, and knowledge out of, out of this, of, you know, what what's, what's happening within, within your business.

So, so I wanted to, I also wanted to list a couple of a couple of reasons why this is important for, for businesses. Once again, when, when linked to a chatbot application, having better insights on the conversations that you're having with the world around you allows you as an enterprise, as a business to drive faster ROI when building a chatbot once again, because you already have a, a better defined, a clearer overview of what are the main challenges, topics, questions that customers or employees have.

So you can use that, that data set to basically build your initial, your initial chat button and drive ROI from day one, instead of from day 60 or, or night at the same time, we've seen with some of our customers that they could actually identify from, from these repetitive questions. They could identify new ways of, or new solutions, new products that they could, you know, bring to the market, addressing those challenges, those questions that customers, where, where we, you know, showcasing in their conversations.

And, and so we believe that using artificial intelligence on either exist in conversational data that you might have in, in different systems, but also using AI on, on top of data that are generated, you know, on a daily, on a daily basis, really have really give you as an enterprise, the ability of, of having more information, more insights about your business act accordingly, whether it's about daily delivering better and faster support, or whether it's about creating new services and new products.

Then of course, that, you know, depends on, on, on a very specific case to case getting started is, is very simple. I wanna share, I wanted to share my, my contact information the way, the way we usually do it.

You know, AI, when we talk about artificial intelligence, digital transformation, sometimes it gets very, very, very flat field, all these huge, you know, two years program timelines. So we tend to make it very simple for our customers to get started analyzing their, their data. And we usually start with a small pilot project where we get access to a limited set of, of data that you might have. We showcase what, what insights we can deliver to you based on, on the data that we, that we analyze.

And, and from there, we then get into production production system. This is it in the sense that, in the sense that I would really like to spend the next five, six minutes on, on a Q and a hopefully, hopefully the attendees have questions that, that, that I would love to, to address over the next minutes. Thank you.