Episode #23: Getting To The Root Cause In Customer Experience Analytics With Sid Banerjee

Companies and leaders who really want to listen to their customers put the technologies in place to do so, especially large companies. They listen to millions of people every single day or week and then derive information that will guide them in rapidly innovating and improving how their organization engages with customers or change the product or the services that they provide. In this episode, Sid Banerjee, the Executive Vice Chairman, Founder, and Chief Strategy Officer of Clarabridge, joins Betsy Westhafer and Tony Bodoh to discuss customer experience analytics. By applying data analysis to unstructured information, businesses are more responsive to their customers and can build better relationships with them. Want to discover how you can improve every aspect of your customer’s experience? Stay tuned to this episode.

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Getting To The Root Cause In Customer Experience Analytics With Sid Banerjee

Customer Feedback Forensics

We have a wonderful guest, Sid Banerjee. I’ve known Sid for many years now. We’ve gone back quite a ways there. We’re going to have a great conversation about listening to your customer and technology around that. I’ll let Betsy do a more formal introduction of Sid.

I’m excited for this conversation because it’s exactly what we talk about all the time, in terms of listening to your customer, and this takes it to a whole new level. I can’t wait to have this conversation. Sid, thank you for being here. We’re glad you’re here.

Thank you, Betsy and Tony. I appreciate the opportunity to chat and share a little bit of my passion and connect with yours.

You are the Founder, Vice Chairman and Chief Strategy Officer for Clarabridge. Tell us a little bit about how you got to this point in your career, and then tell us about Clarabridge.

Clarabridge as a company has been around for many years now. We started the business back in 2008. In my prior life before Clarabridge, I had worked in a couple of companies doing data analytics and data warehousing and helping companies understand and tame all the data that exists across the enterprise. The big idea that led to the creation of Clarabridge was we wanted to try to apply data analysis principles and best practices to the world of unstructured information. Specifically after playing around with a couple of different domains and datasets, we focused on the unstructured information that is generated when customers are talking to companies, or when customers are reviewing products and services in communities, forums, on social media or when they’re doing any business over a phone call or a chat or any type of interaction media basically.

We built a product and a set of analytic techniques to collect and transform all of that unstructured conversational and feedback information into the kinds of analytics and insights that help businesses identify what drives loyalty, what keeps customers happy, what product or service issues are causing problems, so that they can be more responsive to their businesses and to their customers and ultimately, to build better relationships with their primary buyers and users of products and services.

Sid, tell us a little bit about each stop along the way in your career and how your journey professionally got you interested in the work you do now.

It’s interesting, sometimes I get asked that question. It’s been very much a journey. When I first started in the world of business, after about five or six years out of college, I actually ended up working with a company called MicroStrategy. They’re a business intelligence data analytics company that was started by a couple of friends of mine actually from college. Over a period about nine years, I learned a lot about how to apply quantitative techniques to helping businesses collect and understand what’s going on in their business by analyzing the data, sales data, financial data, all kinds of compliance and risk data that exist in large organizations around the globe.

What became clear to me after about eight or nine years of doing that is that the real new frontier for analysis was going to be tapping into the kinds of conversations that we’re having right now. 2 or 3 people talking, asking questions, problem solving, learning about things through just collecting and osmosis-ing, if you will, all the conversational topics of a conversation. I recognized that there was no good technology that did that. There were search engines that could help you find things, then there were analytics that were very much focused on data that you’d associate with a spreadsheet or rows and columns.

Let’s try to take language and turn it into data.CLICK TO TWEET

What we’ve tried to do when we started the company, it was me and two cofounders, we said, “Let’s try to take language and turn it into data. Let’s apply algorithms for extracting all the things that we think are important to analyzing in language.” We quickly came up with a handful of what we call features now that we wanted to extract, sentiment or business topics, more recently emotion risk categories, customer intentions. What is a customer trying to do? Agent actions, what is a company doing back when a customer is asking for something?

As we’ve built these models out, we’ve developed essentially a map for the customer experience between a company and a customer. Those maps are what allow us to work with banks and retailers and health insurance companies, any large company that traffics in a lot of interactions with customers to understand what’s going on. To basically replace what would have historically been a human being leaning over the shoulder of an agent in a contact center or reading hundreds or thousands or millions of rows of survey feedback. We effectively automate that process and make it scalable and make it analytically robust and actionable.

That goes back years ago to when you and I first met. The reason we met was because I was working for Gaylord Entertainment, Gaylord Hotels at the time, and was very suddenly put in charge of customer experience and analytics in that space. I already owned all the other analytics for the company, but then they threw that at me unexpectedly. I was like, “I’ve never written a survey in my life.” I have no idea. I avoided the Psychology classes, but I knew technology and I knew I could learn. I met you and some of your team probably early 2007 at a conference.

Within six months, we had a pilot in place. Our first analysis through that pilot looking at three years of past data, we altered a $300 million decision that the company was going to make. It was quite exciting to see that happen. That was in the early days where we’re looking at survey feedback, not even conversations. Sentiment was a bit of it. I think back to those days and how rudimentary things were then, but how effective they were. Then I look to where we are now and there are 3 or 4 major steps along the path. What would you say those steps have been?

Thank you, Tony, you were one of our first, probably I can count on one hand, maybe two hands at the most, who took the leap of faith when we were starting up Clarabridge. You said, “I’m not sure what these guys are doing, but I think it can help me.” We now have hundreds of customers all over the globe, but it was those first innovative people and companies that took that leap of faith that got us to where we are now. I think there were three key milestones that led to a business like Clarabridge actually having traction in the marketplace.

The first was, and you touched on it, back in the early 2000s, we were starting to see the first real wave of digitization of feedback. It wasn’t that much earlier that when you thought about a survey, you thought about an index card at your restaurant that you filled out while you were eating your food, or a piece of paper that the flight attendant handed you before you got off the plane. In their early mid-2000s, a lot of those feedback mechanisms became digitized because of the internet. You’ve got an email invitation to fill out a survey, or you popped up a survey on your webpage. That digitization made it much easier to capture a much larger amounts of data.

The second was social media, which we take for granted now. Social media was just starting to morph into this platform for not just people to share pictures of their cats and dogs, but to actually talk about companies and businesses and to review products at scale. There was this explosion of information that companies had no control over, and they were scared that these platforms could potentially shift sentiment away from being loyal customers. They wanted to get a handle on that. They were spending a fair amount of money to figure out how to tame that beast.

The third big catalyst for this becoming a business was when we started applying the Clarabridge idea and the platform to businesses like Gaylord’s and to airlines, to retailers. What we quickly recognized was that we were driving a fairly clear return on investment. We would be replacing a market research agency that was charging millions of dollars to basically read and code and generate executive reports on customer feedback. We were doing it not 3 to 4 months latency, but literally every day that new feedback was coming in, it was showing up on a report. It was being done in an automated way. It was a fraction of the cost of what you would pay market research firms to do.

We were able to quantify insights and opinions on social media in a way that nobody had ever done manually because there was never a manual way to do that. Most importantly, when we can point out a problem that if you fixed would drive up more loyalty or reduce people canceling service, they would make those changes and we could actually see the impact of that change.

One example that I remember from early on, we were working with an insurance company and we pointed out that a good number of their calls into their contact center were coming from people trying to verify their primary care physician. In the conversations between agents and the members of this insurance company, members were saying things like, “I don’t know why I couldn’t find this. I was on your website. My doctor is John Smith. He didn’t show up, even though he is in network.” It was because the finder application on the website didn’t recognize that John and Jonathan and a misspelled John were all the same person. We made the case to that customer that if they upgraded their search function with a more robust, fuzzy search capability, it might cost a few hundred thousand dollars, but it was going to save them hundreds and hundreds of thousands of dollars in phone calls because those calls wouldn’t come in. So deflecting calls, creating efficiency, that trend has continued now with more and more people moving to digital and away from in-person.

Customer Experience Analytics: Every investment is an opportunity to challenge; are you getting the return you want?

For better or worse, the COVID pandemic that we’re living through has meant that business is now traffic online and in these virtual media like we’re on right now. It’s important to be able to tap into what’s happening on those conversations so that you can improve business operations. To get people to get the things they want done right the first time, that saves time, money and frankly, it makes your customers happier too. Nobody wants to spend two hours doing what they could do in ten minutes. It’s important to shrink that life cycle down and optimize the customer journey. If you can help companies do that, they’re going to get a ton of value from the solution that we give them.

That’s a great example for people like me that learned through hearing examples of how what you do impacts a company. Do you have any other examples of maybe another way that your technology is helping companies?

I’ve got tons of them but let me pick a couple of interesting ones. One that I find is humorous but it points to the impact of making good decisions during high states investment decisions. We were working with one of the big airlines a couple of years ago, and they were merging as it’s often happening in that business. Two big airlines together to create one even bigger airline. One of the things that they were doing as they were going through this process was rationalizing do they take the meals from airline one and make them standard meals? Do they take the frequent flyer clubs and make them the frequent flyer clubs of the whole thing? Even little things like how do you board – back to front or through zones? Each airline had different ways of doing things.

We worked with this airline during a period of about 9 to 12 months where they were trying to figure out which standard was going to become the company standard as the two airlines were integrating. While we were doing that, we noticed that there was a big spike of negative feedback around coffee in the airplanes. It was in the survey, so we were mining these surveys and we recognized that there wasn’t uniformly negative feedback on the coffee. It was only from certain customers on certain planes. We did further analysis and we recognized that it was the airline customers from an airline that used to serve Starbucks that was now moving to a no-name brand. Specifically when those customers were flying on the Starbucks planes, the coffee was doubly bad. They recognized that what was happening was those airplanes were basically calibrated to pour more water in the coffee because it was a stronger brew when it was Starbucks. When it flipped to the no-name, not only were they pissed off that they weren’t getting Starbucks, but they were getting watered down no-name brand coffee.

They quickly recognized this was a major driver of negative NPS, Net Promoter Score, surveys because the feedback was, “Your coffee is terrible. I got up at 5:00 AM to catch this flight, now you’re giving me this dirty water.” They recalibrated those planes to make the coffee stronger. The satisfaction went up a bit, the feedback went down. Eventually, they switched back to another high-end coffee brand. All of this was driven by us mining feedback from surveys mainly, but also from social media. They called it “coffee gate” inside of this company because it was a big thing.

What struck me, it’s like customer feedback forensics, where you have to dig in and figure out what’s going on. I love that story because how would you know that? You have to really investigate deeply.

You’re looking at multiple dimensions of this problem, it’s not just you can find it staring at you in the face. You have to mine the data, and that’s what we love to do.

It’s oftentimes that in this data, you see something mentioned but it may not be the problem that’s being mentioned that’s the real problem. It’s what’s the root cause. One that we actually uncovered back many years ago at Gaylord, we recognized that we got higher satisfaction scores the higher up in the hotels. Those were 5, 6, 7-storey hotels, massive places. If the people stayed on the highest floors, they gave us the highest ratings versus the lowest floors. That changed operational strategies around how quickly do we fill the hotel? We fill it from top down, etc. That changed our satisfaction scores as well as our financial results because we could even charge people more to stay on the higher floors potentially.

I think I remember that particular finding. One of the other subtexts getting back to root cause, we have this feature now we’ve put in a product called Driver Analysis. It does a multidimensional correlation of all the words and concepts that tend to correlate to positive and negative feedback basically. One of the drivers at least in one of the Gaylord properties was it was one of these courtyard-style hotels. All of the inner floors are opened up to this big courtyard and particularly on the weekends, they had live music. The closer to the bottom of the floor, the more you heard the live music. As you got further up, you didn’t hear it as much. Sound proofing and installation was a big driver for that as well. It wasn’t just the view.

It’s funny because there are many different things like that. I could go through a bunch of examples. In the 2.5 years that I ran the program, there were hundreds of studies. Some of them weren’t very effective. In others we discovered some pretty amazing things that really shifted.

That was back then and that was looking typically at purely survey data and satisfaction data. We were able to bring in, with your team, other elements like room number and rates and things like that. We were able to put some dimensions on that data that allowed us to get some good insights. Let’s fast forward. Your company has come a long way now. Clarabridge is looking at a lot of different stuff now than they did back then. Take us through a couple of the evolutions that you’ve seen in the space. Some people may be like, “Wow I’ve never thought of doing text analysis,” and maybe then you’d be at ground zero. Other people are so well-advanced in this space now that they’re looking at a much different spectrum here.

Nobody wants to spend two hours doing what they could do in ten minutes.CLICK TO TWEET

I appreciate the question. One of the things that I think prepared us well to go beyond just traditional solicited or survey-type feedback was when we started the company way back in the 2008/2009 timeframe, we didn’t try to build a survey business. We tried to build basically a platform that you could connect any kind of feedback to. Early on, it was mostly surveys and social media and online reviews and the things that you would associate with what was available then that could be easily mined. Because we weren’t tied to a single platform, we were always looking for other interesting things to connect to. We did bring in CRM data, the agent notes that people type when you’re on a phone call. That led us to recognizing that if we can take audio data from the actual call recordings when you call an agent, and turn those into transcripts, you could then apply the same techniques.

About a few years ago, 2016 timeframe, we actually started supporting the collection and the mining of speech or speech analytics. That’s actually become one of our highest growth drivers for the business over the last few years. That part of the business is growing 50% to 100% a year, depending on the year that you look at. The other thing that’s been interesting is contact centers, we used to call them call centers, nowadays we call them contact centers because they aren’t just about calls. The typical contact center agent could be on multiple messaging platforms, Apple Business chat or Facebook Messenger or WhatsApp. They could have proprietary chat platforms. They’re often the escalation point for automated chat. Chatbots often act as the first intercept of any conversation that’s online.

What we’ve done is we’ve essentially broadened the aperture and we now bring in chatbot conversations. We bring in all kinds of public messenger and private messenger applications that are being used by agents to talk to customers. Those all fall into the category of contact center nowadays. It’s changed a little bit the use case too because now we’re not just trying to figure out what was the feedback after somebody stayed and left a survey response. It’s when you call in the moment. We want to know why you’re calling. If you’ve had prior interactions with that company, maybe you tried to self-serve, you filled out a popup survey, then you talk to a chatbot. The chatbot wasn’t useful to you, so you demanded to be connected to human being. Then you get on a chat conversation and you’re getting somebody who’s probably a hired partner in some outsource country who doesn’t know the product or business. Eventually, you get to a human being who can help you.

That’s a bad experience because you’ve now had four attempts, each one often making you more and more upset. Now you’ve got not just the problem, but the fact that the customer journey has been so suboptimal, you have an angry customer. What Clarabridge tries to do is collect all of those data sources, all of those interactions and stitch them together, so we can identify when there’s truly been more pain created than efficiency with all this explosion of contact center channels. We try to help companies identify the moments of truth that make the difference between a conversation going in the right direction and going in the wrong direction, so that they can optimize those conversational venues and mediums. Then ultimately create more efficient and better outcomes of conversations.

I don’t think there’s much more frustrating than getting caught in a loop and having to tell your story over and over again. It’s such a value-add to the customers for a company to have this service so that you don’t have to do that.

The other big evolution of Clarabridge and I think of the general space of conversational interaction analytics is when we started the company many years ago, AI, as you know it now, wasn’t really ready for prime time. Most of the way that you tried to detect concepts and themes and different types of features for language was you literally hard-coded all the different things that you thought might conceivably come up in a conversation. It was a very laborious process to build the software. Starting about a few years ago, we made a big bet on machine learning and AI training algorithms. Instead of having to teach the machine all the conceivable ways that you might ask for something or complain about something, we now take data set. We run it through these unsupervised learning techniques. It looks for patterns. We pick out the patterns we like and we say we want to train the machine to recognize those things.

I joke about this. It’s like when you have a kid and you maybe show the kid a football and you say, “Ball, and you show him a baseball and you say, “Ball,” and you show him a basketball and then he says, “Ball,” because he has seen the patterns that these are all objects you throw. We’re effectively teaching the computer how to recognize all the different things that customers talk about when they have customer experiences and interactions. That’s allowed us to scale into industry models and to different language models around the globe. It’s allowed us to basically continue to accelerate the pace of innovation and the technology.

You mentioned industry models and that was actually going to be my next question. Is there an industry where they are really embracing this and taking off? Is there an industry that is on the leading edge of this?

There are a couple. When you look at who’s really doing CX customer experience, at least using technologies like Clarabridge, it’s generally big companies in businesses that have a very high count of customers and a high number of channels in which a customer can basically interact or do business. There are 4 or 5 that we always see, retail, banking, insurance, both health and traditional P&C and auto, consumer goods, particularly those that have a direct to consumer channel and way of doing business, and tech companies, think computers and telecommunications companies, those kinds of folks.

Specifically over the couple of years, we’ve seen all of those, but I’d say we see a lot of investment in the US in the insurance space, in the health insurance space in particular. I think it’s because there’s been a much broader appeal to direct to consumer offerings. You now can get Medicare insurance from a private insurer. The Affordable Care Act has created a whole retail marketplace. A lot of the health insurers prior to that had captive customer basis. If you didn’t like your insurance from your company, pretty much you had no choice but to quit your job. Or wait for a year and then maybe re-enroll if they had multiple options. Now, there’s a lot more ability to switch, particularly if you’re retired or if you’re in Affordable Care Act.

Customer Experience Analytics: Computers are only going to do certain things well but other things not at all.

These health insurance companies have been very quickly realizing they have to focus on customer experience just like everybody else. The other ones like technology, technology is always disruptive. People are always looking for better ways. Any company that’s embracing contact center automation, chatbots, messaging as a channel for catching the digital consumer, is recognizing that for every investment you have to quantify are you getting more value or are you creating more inefficiency? With every investment is an opportunity to challenge, are you getting the return you want? Using solutions like ours, you can actually start to calibrate, “Where am I getting efficiency? Where am I creating potentially problems that I need to solve?” That’s the tech companies and a lot of the more innovative companies, digital commerce companies, etc.

Are you seeing the ROI shift away from the efficiency play, maybe more toward improving sales itself? Are you seeing any of that happening in the space?

Yeah. Actually, I’ll answer the question more broadly. There are four drivers that often call the question of, “Should I be investing in this type of CX analytics, customer experience analytics?” The first is, “Do I think I have inefficiency and I want to drive it?” Companies that know that they want to improve margins, they want to rationalize investments, they’re going to do that. The second is, “Am I looking to basically improve market share and grow revenue?” When we find companies that are either dealing with churn that is below a level of acceptability, they want to reduce churn or they’re launching new products and services. They want to maximize that those new services are going to be generating growth and lift in either their revenue per customer or new customers. They want to understand what drives brand equity, what drives interest. That’s all about driving successful growth in the business. You see that with retail, you see that with consumer goods, you even see that with some of the more diversified financial services firms that are trying to go digital and attract a new kind of customer.

The third category is what I would describe as the risk reduction category. In particular in markets that are regulated like healthcare, financial services, banking in particular, where there are regulations on consumer financial protection, fair credit, anti-discrimination clauses. It’s important to know that when your customers are doing business with you, you’re abiding by those regulations. We’ve actually created models that look for the warning signs of a potential regulatory infraction, so that a company can identify it, investigate it, resolve it ideally before it gets to the point of being a complaint with a regulator. Also to show that they have good practices for minimizing risks. It helps them to show that they care, but it also actually reduces the costs of regulatory infractions.

Then the final one is anytime you’re making major changes. The airline example was one where you’re integrating two companies. That’s a major change initiative. Launching a brand-new product, anytime there’s a change initiative, you want to make sure that you’re listening particularly acutely to the customer to make sure that change is producing a desired end result, not a negative consequence of some sort.

Sid, what gets you most excited about the future of this? What are you seeing out there that you can talk about? What gets you really jazzed up about where this is heading?

It’s been interesting to watch and to be part of using all these new technologies that are able to understand humans. Since we started Clarabridge to now, the complexity and the sophistication of the algorithms that we’re using to understand human interactions to pick up on subtle markers of emotion, intention, etc. It’s not even just words. It’s how words are used. It’s silence and other things. It’s actually weird in a way and almost overwhelming. I think we’re not far away from a time when you’ll be able to interact with the computer as naturally as you and I are talking to each other right now. There are 2 or 3 steps that have to happen. The first is understanding, which is the computer understands what you’re saying. We’re approaching that level of peak capability over the next few years.

The second is responding back, which is now training the machine not just how to understand the intent of the human, but the best way to respond. This is like if you have an Alexa or one of these Google assistants in your house. I challenge my Alexa from time to time because I want to know if it’s getting better. I ask it to tell me stories. My wife thought I was super weird, but a few years ago, I asked Alexa to sing me happy birthday because it was my birthday. It did it and it was actually not bad too. It’s becoming more human. As it gets better, it’s dynamically generating responses, which is something that we’re doing right now. We’re researching how to create an environment where Clarabridge doesn’t just provide analytical insights. It provides human adaptable dynamic responses to questions.

At that point, we hit a point where I think we could imagine computers as we know them disappear. They are no longer attached to keyboards. They’re basically visual and audio devices that we talk to like we talk to other human beings. I think that companies like Clarabridge who are developing this capability to not just understand, but to detect empathy, intention, emotion, loyalty, are going to be on the forefront of a new kind of computing and a new kind of application space. Where it goes, I don’t actually know, but I know that we’re doing some pretty cool stuff. There’s a lot of potential for this to get even more interesting over the next 5, 10 years.

That brings up an interesting thought for me, one that we didn’t prep for. We’ll see where this goes. As computers become more able to understand, to respond, where does that leave humans from a perspective of the employees? Do you think employees are going to actually get better at listening because they’ve got these devices to help them? Or are companies going to go to the direction of saying, “Let’s use technology to listen because it’s better than training our people how to listen?” I see that happen some places a little bit. We’re not quite there yet though.

To see an idea and turn it into reality is important to help people grow and develop themselves as people.CLICK TO TWEET

It’s an important question. I’d be lying if I didn’t wonder if I’m helping to create the Terminator. In ten years, will these machines take over and I’m going to regret everything I’ve done in my life? I hope not. I think what’s happening is there are going to be at least two primary things, hopefully more, that humans are going to have to do to adapt and to stay in control of this transformation. The first is in any customer facing business, there’s the stuff that computers can handle. If you look back 50 years, it was tracking dollars and cents and managing logistics. It was all the quantitative stuff. The computers are going to add into being able to handle a certain kind of interaction and a certain kind of emotional understanding response. Anything that it doesn’t understand, it has to go to a human. Humans still have to be the consultative problem solvers, the advanced support, the fine things that you haven’t taken the time to train a computer how to do. We are going to have to be more and more involved in the consultative relationship with our customers than ever before. Computers are only going to do certain things very very well, but other things not at all.

The second thing is just like the industrial age ushered in a generation of factory workers, the knowledge revolution of AI is going to usher in a whole new generation of basically machine trainers. You think about a computer is a machine learning algorithm. People have to train machines. We are only on the very edge of all the things you can train machines to do. There will be a whole new generation of work that needs to be done to train machines on all the things that they could do. If you have kids, teach your kids a little bit about machine learning. That’s what I’m trying to teach my kids about, because I think that’s the future. All the physical stuff is still going to be there too. I do think that the two new generations are the consultative, tier-one plus stuff that computers can’t do. Then making sure that those machines keep getting better because they don’t always get better on their own. They need humans to guide them in the right direction.

It’s so exciting to me as a consumer to hear this because I know it’s going to make experiences better. I just thought of an experience that’s just happened. I had switched cell phone providers. My legal name is Anne, so I registered it under Anne but I go by Betsy. When I set up my account online, I put it in as Betsy. Then I had an issue and I was talking to a rep on their chat and he would not give me access to my account because it didn’t say Anne and I said I was Betsy. When I log on to pay my bill, it says, “Welcome, Betsy.” Those two things were not connected, so it locked me out. I had no access through this experience to my own account. I get the security concern and all of that, but if their systems have been connected, it would have been easy for the human to see, “We get it.” I think it’s very exciting as consumers. Some people I think are scared by it because it does feel a little Terminator-like sometimes. For me as a consumer, I think it’s great because it will enhance the experience.

I’m going to touch on a point you made about the bringing the data together. There is an interesting question you didn’t ask, but I’ll go there, which is when you start linking together a lot of this data, you do create an aggregate awareness of you as a person. There are questions that people are asking. They’re certainly asking it in California with the data privacy GDPR in Europe, around how much can a company collect on you and what control do you have over that data that they collect on you? When you start to bring in this subjective information, the conversations, interactions, not just how much you spend and what your loyalty card number is. People do want to know that they have some ability to control that information. I do think that there will be an era that’s coming. Some of the more privacy-focused societies are already thinking about it. It is coming and we should welcome that. People should be able to say to a company, “I want you to delete everything you have on me,” and know that they will do that because at the end of the day, I think it’s your right in a free society. This is something that we track and I don’t think it’s bad. You’re going to see the twin progression of people using machines to learn more and more and to be better at serving you, but also customers need to be able to retain some control over that if they want to delete themselves from a system. They’ll go together.

It never even occurred to me until you just said that, that would even be something I could ask a company to do.

You can if you’re up in Europe and California, but not the rest of the US.

Switching gears, one of the things we love to do on our show is to give an opportunity for our guests to do a shout-out to a nonprofit or some kind of community involvement. I know that you’re very involved with entrepreneurs. Can you share with us a little bit about that and let us know about the organizations that you’re involved in?

There’s one that I’ve been involved for years now. It’s a group called NFTE, which is National Federation for Teaching Entrepreneurship. They were started many years ago by a reformed businessman who realized that in fact that teaching kids, particularly kids in underserved communities, the basics of thinking about the Laws of Supply and Demand and P&L and giving them the basic tools to build a business plan, gave them the confidence to be able to think about starting a business or even joining a business and building their way up through learning and growing. The end event of every year for the kids that go through this program, it’s taught in the high schools around the country, is an annual business planning competition where people submit business plans. They go through a regional and then national judging competitions. The winning proposals end up getting scholarship funding and so on. I’ve been involved initially on as a coach in the business plans. I was on the board of the local NFTE here in the DC area. I’m now more of a supporter from afar as life has gotten busy for me. I love what they do. I think that I’m giving kids the ability to see the value of math, of thinking, of planning and of being able to see an idea to turn into reality, I think is important to help people grow and develop themselves as people.

It’s funny you mentioned that because I’ve been involved in something similar as a mentor in the entrepreneurship program at the University of Dayton. We would have teams of kids and they would have a real life basically consulting project with an entrepreneurial venture to get an inside look at how things go. They would always have one that had a social aspect to it. I read where one of the teams, what they were working on at the very beginning of the concept is now a reality. It’s fun that these kids got to have input into the research and just how is this all going to work and the numbers and all of that. It sounds like a great organization. Thanks for sharing that.

You’re welcome. Definitely check them out if you have interest. They’re a great group.

Customer Experience Analytics: The knowledge revolution of AI is going to usher in a whole new generation of machine trainers.

Sid, how can people get ahold of you if they want to connect and take the conversation a little bit further?

I’m on all the platforms. You can certainly find me on LinkedIn at Sid Banerjee at Clarabridge, or feel free to drop me an email, which is Sid.Banerjee@Clarabridge.com. I’m happy to connect and continue the conversation offline.

Thank you so much for your time. This is fascinating. I think it opens people’s minds. It’s exciting to see where this is all going. We appreciate you being here.

I appreciate the opportunity. Thanks again for a great conversation.

Thanks, Sid.

This is one of the most fascinating interviews I think we’ve had. I say that not just because I have this relationship that goes back years with Sid. In truth, I would not be in this space the way I am if it had not been for my early collaboration with Clarabridge when they were a new company, when I was new to the CX space. What we learned together was interesting and helpful to move the organization ahead. I’ve worked with different clients of Clarabridge coincidentally over time. It’s always fascinated me to understand the difference between a company and the leaders who really want to listen to their customer, and they put the technologies in place to do so, especially large companies. They’re listening to millions of people every single day or every single week. Then deriving this information that they can rapidly innovate and improve and change how their organization engages with customers, change the product, the offerings, the services that they provide. For me, this was an exciting interview and something that was very insightful as to where we are now and where we’re headed down the road.

I agree with you, Tony. When he was talking about how they piece this all together to ultimately create a better experience for the customer, it’s not listening just for listening sake or having stockpiles of data so you can say you have it. It’s really how can we use this information in this data to really transform the experience for the customer, which at the end of the day is what it’s all about. It’s all about the customer. That’s what I love about how Clarabridge blends the technology with the ultimate experience for the customer. I agree with you, Tony, that was a very exciting episode and conversation. We’re grateful to Sid for taking the time out to be with us. If you enjoyed this episode or any of our other episodes, we would greatly appreciate a review on whatever platform you listen to us on. Know in advance how much we would appreciate your thoughts and feedback. We’ll see you next time.

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About Sid Banerjee

Sid Banerjee is Executive Vice Chairman, Founder and Chief Strategy Officer at Clarabridge. Sid provides executive leadership and strategic direction and is a well-known expert in customer experience, business intelligence, and text mining. Over his careers, Sid has amassed more than 20 years of business intelligence leadership experience.

A founding employee of MicroStrategy, he held VP-level positions in both product marketing and worldwide services. During his tenure leading MicroStrategy’s worldwide services division, he grew the organization to 500+ employees supporting enterprise deployments of BI solutions. Before joining MicroStrategy, Sid held management positions at Ernst & Young and Sprint International.

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