Digital Customer Service Made Easy

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ANUJ BHALLA: My name is Anuj Bhalla. And I am the founder and CEO of serviceMob. We are a cloud-based analytics platform using AI and machine learning to make the customer service experience better.

Prior to my time at MIT as a Sloan Fellow, I was with Accenture, where I led the service analytics practice, helping, generally, Fortune 500 companies make the most out of their customer service information. I'm a data scientist by trade. And when I came to MIT, I realized that this was a bigger problem than I had even experienced in industry, and that a lot of the disparate data problem that I was helping clients with over time was just a problem that was growing as more and more venture technology came into the customer service ecosystem.

That said, we started out of Bill Aulet's class in New Enterprises, and Anantha's class at Start MIT, and really got to thinking about how we solve this problem, especially as more interesting technologies have entered the landscape, but at the same time was causing more and more of these disparate data sets that made it harder to see how a customer moves through customer service. That said, through MIT, we were able to crystallize those ideas and really launch kind of the first version of what serviceMob really ended up becoming.

In a word, customer service, the current state of customer service, is painful. Running a customer service operation, especially in this day and age, is harder than it's ever been. If you're a VP of customer service, you're dealing with so many different systems, a Frankenstack of systems that make your customer service operation work.

And as a result, it leads to a huge problem that customer service operators face. It's working with that Frankenstack, working with that those disparate systems to really see that coherent view of that end-to-end customer support experience. And as a result, these are symptomatic of the issues that we see or face as customers today when we contact a call center. We're waiting on hold for 45 minutes for the next available agent. Agents don't know who we are. Chat bots that don't understand us. These are all based and have that root cause to that customer service data issue.

At serviceMob, what we're trying to do is help companies go from disparity to clarity. How do we take all this information, make the most sense of it, and give these operators the best possible information at every level of the business to make better decisions day in, day out, hour in, hour out? That's where we try to make a difference here at serviceMob. And at the end of the day, what we're trying to do is make sure at every level of the operation we're using that intelligence to make better decisions to improve the customer experience and reduce the amount of effort, which, in turn, reduces the cost that's involved in providing excellent service to your end customers.

The tech platform works by taking all this information that's across the entire customer service ecosystem. Customer service, modern customer service, generally has 5 to 15 different systems that make it work in modern day. This is not our parents' customer service, where you call the 1-800 number, and that's the only way you get into talking to a company. Now you can call, SMS, email, text, tweet your discontent to a company. And as a result, all of these represent different systems of record that companies have to reconcile in order to make a coherent picture of that customer service journey.

So a lot of what we do is our algorithms take all this information from these disparate systems. We're able to put this into our unique data ontology that actually uses a system dynamics framework, something created here at MIT, to really understand the cause effect relationships by which the operation really lives and breathes every single day.

So what are the cause effects? What are the unintended consequences of an operational action to that customer experience? This is now something we can see through that coherent data ontology.

The idea that we have-- I'm stealing an old Netflix line-- radical transparency. We believe this is very important to customer service, where data analytics shouldn't just be, in other words, accessible to the data scientists. We actually believe that the best recipients of analytics in this specific industry of service are the operators themselves.

So imagine frontline agents that can see their own information in real time. They can see where their performance is, how they're doing against themselves in a previous iteration, how they're doing against the floor, how they're doing against their team, and what they need to do to improve to really drive those customer-centric metrics toward the goals that are prescribed to them from their supervisors, managers, all the way up to that VP or chief customer officer.

So that's important, because what's so important in this industry is that everyone needs to be rowing in the same direction. When you row in the same direction, when everyone has that same North Star, the same metrics, you're going to get to those goals faster. And those are the things that really make a difference at the end of the day in this industry. That's where you see the metrics move in the right direction, and at the end of the day, customer service improving for that end customer.

Today, in terms of our beachhead market and problem that we're looking to solve, we've really focused a lot on agent performance management. So using our analytics to help improve performance of those frontline agents that are serving those end customers, and then utilizing the strengths of how customer service operations are organized.

So customer service, maybe 10 frontline agents will have a supervisor. Maybe a few supervisors will have a manager. A few managers will have a director. So that span of control and that hierarchy of how information flows through a large organization like customer service, which, in some instances, some companies, I mean, it could be as small as a few dozen agents all the way to tens of thousands-- and some organizations, even hundreds of thousands of agents.

So it's very important to get information in in the right way through that chain of command, so to speak. And so that's part of what we're doing right now is really focusing on agent performance management and allowing the flow through of information to work through that chain of command.

Now, where we're going beyond this is we're also providing root cause analytics for product teams. So people are calling, or chatting, or emailing customer service because presumably something went wrong with whatever product or service a company has in market. That said, companies, and product engineers, and folks who are essentially charged with solving problems around user experience or product defects, they need that customer service information to see where are the most prevalent and problematic issues, most prevalent and painful issues, really, that are affecting your customer base today.

And if you're able to see that, if you're able to use service data as at least an input into that, a very critical input, now you can see where those failure points are. And as a product, or service engineer, or a product team, you can address those to make sure that customers don't have those issues in the first place.

We're partnering with ecosystem partners in the customer service industry because we rest-- how I like to say this is we rest in that airspace above those 5 to 15 different technologies that make customer service work. And with that, we could-- we're already forging partnerships with many major players in that ecosystem. So think of CRM providers, telephony providers, chat providers, email tools, workforce management systems, even sentiment analysis, and next best action tools.

These are all things that have been interpolated into the customer service industry. And each one of these players has, I would say, a vested interest to make sure their end customer is using the data generated from their part of the value chain in the maximum possible way. That leads-- that's good business for them, because once that happens, that's where those customers find stickiness as well, because customers are seeing more value in the sum of the parts.

So because we're able to amalgamate that data all together, they see more value in those individual component value players. And so as a result, making partnerships with them is very natural. So that's what we look for is folks that have that common interest in making sure their end customers are successful.

And then we also look at some implementers and some consulting companies, who, in some ways, have traditionally been competitors, but in other ways see value in trying to identify hotspots and opportunities in the business long term. So those consulting companies can take the data and information, identify the hotspots in the operation that may need fixing, may need transformation. Analytics itself is one part of solving the problem, but consulting companies can often see a complementary offering by, one, using our analytics to identify the problem, and, two, helping the companies afterwards by addressing those problems, and really fixing them, and putting a business case around them that's strong.

A few things that are incredibly important are going to be around-- especially for a data company, is going to be around data security. So we've already achieved SOC 2 compliance, which is the topmost industry standard to ensure that if you're giving us your operational data, companies need to ensure that their data is safe with us. So we've achieved that level of compliance, which is a huge industry milestone for us.

We've also achieved multi-tenancy. So one of the big things is we're not building bespoke solutions for every single client. Part of the challenge of what we have to do at serviceMob was to say, if we were going to build a company that was going to be large and successful in this space, one, it would have to work across industry. So whether it's customer service for an airline, a travel hospitality company, or a telecom, can our software work? Do we have to build new instances of this? Or can it work across the board?

And then, secondly, we had to build a technology that was agnostic of tech stack. So it doesn't matter if you have Salesforce for your CRM, or ServiceNow, or Microsoft Dynamics. Can we work with any one of those systems? Can our technology recognize the systems that are there and pull out the right data necessary to feed our universal data model?

And so those are things that we achieved, especially in our first handful of customers. And we represent today a diverse number of industries. Just in our early customer base, we represent companies from SaaS to health care, travel and hospitality to shipping and logistics. So those are the kinds of things and proof points we have to show not just ourselves, but also to be a scalable part of the solution in customer service going forward.

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