
10.25.23-Digital-serviceMob

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Video details
Ontology Based Analytics Cross Industry For Service/Support Centers
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Interactive transcript
ANUJ BHALIA: Hey, there. How is everyone today? Doing all right? My name is Anuj Bhalia. I'm the Founder and CEO of serviceMob. I was a Sloan Fellow here at MIT. Got my MBA in 2016. And today, I'm going to talk about AI-powered customer service analytics-- everyone's favorite topic. So let's get this party started.
If you can't measure it, you can't improve it. This is the state of customer service today. This is why, in 2023, customer service is not nearly as good as it should be. We are talking about quantum computing, large language models, era of GPT. We're wondering, why does our customer service suck so bad? Why? I mean, it's amazing.
Let's see if we can figure this out. So quickly, just a quick description. serviceMob is the AI-powered analytics command center that displays the true performance of a customer service operation. We'll get more into what that means. So lack of observability-- the Franken stack is what I call it. There's a lot of different systems that make customer service work. And as a result, they're all disjointed, disaggregated. They're disparate. They're all over the place. And it's hard.
Each one of these has their own data ontologies, their own data models, their own identification verification. They have minimum viable connectivity. As a result, people who are running customer service are operating in the dark. It's like trying to put a jigsaw puzzle together blindfolded. This is literally what they're doing.
And as a result, that leads to everything you hate about customer service-- the long wait times-- that's a data failure-- unproductive agents-- data failure-- higher cost to serve, repeat contacts, calling in, contacting four or five, six times about an issue. These are all the symptoms we face as consumers. And I think we've all had it. I think we need to figure out a better way to do customer service in the era of AI.
And why that's important-- that leaves $4.7 trillion on the table. Qualtrics did a study just not too long ago. The switching economy to move from taking your business from one company to another because of bad customer service-- $4.7 trillion. That's a large, large [INAUDIBLE] folks.
So what are we doing at serviceMob to fix this? For the first time ever, we are actually modeling the customer experience. We're using Gen AI. We're using large, mathematical models to actually compute, take all that data, all the metadata, all the disparate data from all parts of your Franken stack, your CRM systems, your chat bots, your engines, your co-pilots, your knowledge management, each one of your agents on the floor-- we're taking all of that information, and we're putting that into a model.
So for the first time ever, we are modeling the customer experience. Now if I asked company owners, how many customers do you have? That's a quick fact that people can come back with in their CRM. If I asked you how many touch points do you have with customers yesterday or last week, that's a fact that people can pull out of their database.
But if I asked you how many customer experiences did you have yesterday or last week or last year, people are like, well, it kind of depends. What do you mean? How do I look at customer experiences? We all want to improve the customer experience, but we have no way of measuring it in our own data. It's like we all want to move the needle on customer experience, but we have no idea where the needle is. It's buried somewhere in the haystack until now.
So serviceMob is taking all that information. We're combining that together so that we can actually measure experiences for the first time ever. And we are the one and only company that truly does this in the market today. We are helping companies go from disparity to clarity, putting all that together in a single, unified data ontology that allows companies to now see experiences, measure it, and actually improve it. This is the true performance of customer service. And that's going to allow folks to finally improve what they previously couldn't measure.
We're working with some of the coolest companies in the world, companies like Roblox. They're working with us. We're working with publicly-traded entities, fortune 500's. This is a case example from a publicly-traded travel and hospitality company. We were able to save them 5 million to their bottom line against a cost of only 130,000 was what they paid us. That's a 37X ROI.
The CEO in their quarterly earnings call said, hey, you couldn't find a 37X ROI in the last year with anything, not Brent crude, not S&P 500, not gold, not Bitcoin. serviceMob-- you couldn't even put the right number on the roulette wheel-- that's only 35 to 1-- and get the kind of return you got from serviceMob. So again, we stand on real value, using AI to drive value for companies.
We want to partner with companies out there that are looking to improve their service. Come see us in the booth outside. We're near the water cooler. Happy to talk service with anyone who wants to improve it. Thank you.
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Video details
Ontology Based Analytics Cross Industry For Service/Support Centers
-
Interactive transcript
ANUJ BHALIA: Hey, there. How is everyone today? Doing all right? My name is Anuj Bhalia. I'm the Founder and CEO of serviceMob. I was a Sloan Fellow here at MIT. Got my MBA in 2016. And today, I'm going to talk about AI-powered customer service analytics-- everyone's favorite topic. So let's get this party started.
If you can't measure it, you can't improve it. This is the state of customer service today. This is why, in 2023, customer service is not nearly as good as it should be. We are talking about quantum computing, large language models, era of GPT. We're wondering, why does our customer service suck so bad? Why? I mean, it's amazing.
Let's see if we can figure this out. So quickly, just a quick description. serviceMob is the AI-powered analytics command center that displays the true performance of a customer service operation. We'll get more into what that means. So lack of observability-- the Franken stack is what I call it. There's a lot of different systems that make customer service work. And as a result, they're all disjointed, disaggregated. They're disparate. They're all over the place. And it's hard.
Each one of these has their own data ontologies, their own data models, their own identification verification. They have minimum viable connectivity. As a result, people who are running customer service are operating in the dark. It's like trying to put a jigsaw puzzle together blindfolded. This is literally what they're doing.
And as a result, that leads to everything you hate about customer service-- the long wait times-- that's a data failure-- unproductive agents-- data failure-- higher cost to serve, repeat contacts, calling in, contacting four or five, six times about an issue. These are all the symptoms we face as consumers. And I think we've all had it. I think we need to figure out a better way to do customer service in the era of AI.
And why that's important-- that leaves $4.7 trillion on the table. Qualtrics did a study just not too long ago. The switching economy to move from taking your business from one company to another because of bad customer service-- $4.7 trillion. That's a large, large [INAUDIBLE] folks.
So what are we doing at serviceMob to fix this? For the first time ever, we are actually modeling the customer experience. We're using Gen AI. We're using large, mathematical models to actually compute, take all that data, all the metadata, all the disparate data from all parts of your Franken stack, your CRM systems, your chat bots, your engines, your co-pilots, your knowledge management, each one of your agents on the floor-- we're taking all of that information, and we're putting that into a model.
So for the first time ever, we are modeling the customer experience. Now if I asked company owners, how many customers do you have? That's a quick fact that people can come back with in their CRM. If I asked you how many touch points do you have with customers yesterday or last week, that's a fact that people can pull out of their database.
But if I asked you how many customer experiences did you have yesterday or last week or last year, people are like, well, it kind of depends. What do you mean? How do I look at customer experiences? We all want to improve the customer experience, but we have no way of measuring it in our own data. It's like we all want to move the needle on customer experience, but we have no idea where the needle is. It's buried somewhere in the haystack until now.
So serviceMob is taking all that information. We're combining that together so that we can actually measure experiences for the first time ever. And we are the one and only company that truly does this in the market today. We are helping companies go from disparity to clarity, putting all that together in a single, unified data ontology that allows companies to now see experiences, measure it, and actually improve it. This is the true performance of customer service. And that's going to allow folks to finally improve what they previously couldn't measure.
We're working with some of the coolest companies in the world, companies like Roblox. They're working with us. We're working with publicly-traded entities, fortune 500's. This is a case example from a publicly-traded travel and hospitality company. We were able to save them 5 million to their bottom line against a cost of only 130,000 was what they paid us. That's a 37X ROI.
The CEO in their quarterly earnings call said, hey, you couldn't find a 37X ROI in the last year with anything, not Brent crude, not S&P 500, not gold, not Bitcoin. serviceMob-- you couldn't even put the right number on the roulette wheel-- that's only 35 to 1-- and get the kind of return you got from serviceMob. So again, we stand on real value, using AI to drive value for companies.
We want to partner with companies out there that are looking to improve their service. Come see us in the booth outside. We're near the water cooler. Happy to talk service with anyone who wants to improve it. Thank you.