Interview
The Gist
- Embrace continuous learning. Staying ahead in customer service requires constant adaptation to new technologies like AI and machine learning.
- AI enhances human roles. It's a tool for improving, not replacing, human jobs in customer service by automating routine tasks.
- Balance automation with empathy. Successful customer service blends AI's efficiency with the human touch for a personalized experience.
In this discussion between Rich Hein, host of "Beyond the Call," and Raj Krishnan, partner of technology at Microsoft, the transformative potential of artificial intelligence (AI) in revolutionizing customer service and support within contact centers is explored. Raj Krishnan shares insights from his extensive experience, emphasizing the importance of leveraging AI to enhance, rather than replace, human workers in customer service roles. They delve into various aspects, including the integration of large language models, the significance of self-service technologies, sentiment analysis, omni-channel support, and the challenges and opportunities presented by predictive analytics. The conversation underscores the critical role of AI in providing faster, more personalized customer experiences across multiple channels, and the need for organizations to build flexible systems that can adapt to evolving technologies.
Table of Contents
- Tech's Role in Customer Service Evolution (0:36)
- AI's Growing Influence on Customer Experience (3:08)
- Leveraging AI for Enhanced Customer Service (4:09)
- AI Enhances, Not Replaces, Human Jobs (6:26)
- AI's Role in Balancing Automation and the Human Touch (8:46)
- Strategizing Automation in Customer Service Functions (10:46)
- Enhancing Self-Service With Customer Feedback (12:16)
- AI's Impact on Customer Feedback Analysis (14:03)
- Exploring Beyond AI for Customer Sentiment Analysis (16:16)
- AI Filters Noise for Actionable Insights in Contact Centers (17:32)
- Refining AI for Impact in Call Centers (19:20)
- AI Streamlines Omnichannel Customer Support (20:55)
- AI Enhances Continuity in Customer Service (23:24)
- Challenges of Implementing Predictive Analytics in Business (25:28)
- Transforming Contact Centers with AI in 2024 (28:02)
Rich Hein: Hello, everyone, and welcome to our very first episode of “Beyond the Call,” a show that's charting the frontiers of customer experience and support within the contact center. I'm your host, Rich Hein, editor in chief of CMSWire. And with me tonight, I have a forward thinking leader in the realm of customer experience. His name is Raj Krishnan, and he's partner of technology at Microsoft. Raj, it is so great to have you on the show today. Thank you for being here.
Raj Krishnan: Thank you, Rich. It's a pleasure to be here.
Tech's Role in Customer Service Evolution
Rich: Great. Well, just for a little context about Raj, Raj is an expert in digital transformation. He has over 25 years driving innovation in areas like AI, machine learning, cloud computing and more. And today, we're going to be picking his brain specifically on the intersection of cutting-edge technologies that are happening within customer service and support. But before we go there, Raj, I would like to ask you, if you wouldn't mind talking a little bit about how you got to where you're at and what attracted you specifically to this space because this is one of those spaces where it's like, you got to be learning all the time, there's constant evolution, the pace just seems to be getting faster and faster. So please tell us about you.
Raj: Yeah, I mean, I’m trying to kind of narrow it down to, particularly with the customer service and contact center. So you know, I've been in this industry for a while. But I started off actually running a call center. About a 400 people call center. I don't know if you remember the days of you ordering checks. And I used to run one of those, both inbound and outbound where, you know, you pick the design, and you order 300 checks, then we try to upsell and cross-sell a little leather cover and all that stuff. Right. So that's one of my earliest introductions to the call center.
I was also with an outsourcing company building the entire back end, you know. There used to be a system called Brock's for all the contact center, campaign management and things like this. But see, that was my pre-Microsoft date. But in Microsoft, I've been doing several things. I started up as a healthcare technology evangelist. And then I've been a, you know, cloud solution architect, digital transformation adviser in order of the things you mentioned. And I've been constantly kind of shifting. Besides the fact I'm a little restless, but it's because I'm always curious to learn things, particularly as newer technology comes. And not because for the sake of technology, I'm not a pure technologist, but how do we apply and make things better? Right. And that has been my big motivation. And I was always there. When the cloud came, I just jumped on the bandwagon. Blockchain, I was there, of course, artificial intelligence. It's an irresistible arena, because it's a — probably, you know, the one of the biggest in my career that I've seen that is going to change the way that I'm going to live, work and do things, right. And in particular, with customer service. So that's where I am now.
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Rich: Yeah, there's no doubt in my mind that AI is just infiltrating almost every corner of the business. And in 10 years, I think the workplace is going to be a very different workplace. It's going to be interesting to see. If I recall, you just traveled to New York, just in India.
Raj: Yes, yeah. You know, travel is also another aspect I do for business. But you know, in my younger days, I used to backpack almost all the continents — South America, Africa, Asia, all by myself alone. I've done some of those things in the past as well.
Rich: That's interesting. And I know that you — from having done some research on you — that you're also a musician, and I will try to get you to pull out some of your musicianship later on in the show if you have the wherewithal for it, that would be great.
Raj: Now, let's see. I don't think I usually keep a guitar behind it. Yeah. No, yeah. Let's see. Let's see. OK.
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Leveraging AI for Enhanced Customer Service
Rich: Hopefully we’ll have time. So let's just jump right in. Can you talk a little bit about some of the specific experiences you've had working with artificial intelligence and machine learning in a customer service and support environment?
Raj: So you know, one of the big opportunities that we have with the large language models, you know, we like I was just reading a report this morning, right? All these web self-service bots, were kind of picking up. I think what these new large language models have dramatically changed is the ease with which now people can actually interact with systems. Right. And to me, that's the biggest benefit, right? So we're, for example, when we used to build a customer service bot, we had to use these natural language models to say what is the intent, what are the different utterances? How, like say I want to book a ticket and how in 10 different ways can I say so that I teach the language model to understand what we say. What has dramatically changed is that I just simply talk the way I talk, and the system understands it. That's the power of the large language model, right? However, the biggest limitation with that, along with it, comes these large language models are trained with a corpus of data, and to grounded to what is relevant to your data. like, for example, if I'm a customer service person with my own data, how do I leverage this ability of these language models to understand what I'm saying, but use my data to be able to respond? So those are the areas where I see tremendous opportunity. Why is it important?
One of the big challenges is these contact center representatives, you know, they all come in with different varying skill sets. Somebody has deep skills about your domain, some of them are new. To me, what the AI is going to do is just set that level so that everybody can leverage corporate knowledge, and then interact with it very easily. I think a lot of the projects that I'm working on are more not about just using AI, but taking a business process and infusing AI everywhere possible to simplify and improve the outcome.
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AI Enhances, Not Replaces, Human Jobs
Rich: So from that perspective, it sounds like, at least for the time being, it's like an add-on assistant to what humans are already doing. It's not beyond replacing people. Do you see that happening down the road?
Raj: I mean, so this is also a misconception right? On through our technology changes, jobs have changed. And some people have, you know, you just think about our auto industry, right? People used to just make $20 an hour, just tightening bolts and nuts. And then robots came, now people are doing bigger and better things, but those jobs can eliminate it. To me, it's — I don't look at it as a job elimination. But it's a job enhancement. And of course, that requires people to be able to leverage those skills, which is going to be a challenge. Not everybody is equipped, not everybody is driven. Those are some of the solution consequences. But to me, what this is doing is removing the mundane aspects of my life, you know, I'm just working with a company. And actually, this is kind of a little bit of a plugin, because I'm really interested in them, what they're doing is, they're like, intercepting an email that comes using AI to analyze what is inside? And what are the — what is the intent or the intention? And what are the like, where is it coming from, apply business rules, use AI to classify that. And now all these things are eliminating one big thing from a customer service representative. If I get 100,000 emails a day, I need to go through each owner and find out which one should I be addressing, right? Now, AI comes and tells you, hey, this is your high value customer, this person is irritated, you better take care of him first and elevate that to your attention. Now, these are the kinds of little things that come and improve that end-to-end process. And then, of course, at the end of the day, once I know what it is — use my company-knowledge repository, automatically crafted responses, and then let the person, instead of doing copying and pasting, go and make those changes, send the email. These are the areas I see AI continually enhancing the way you respond to your customers and the customer service issues.
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AI's Role in Balancing Automation and the Human Touch
Rich: You know, we're going to talk about some very specific areas where AI and machine learning are having a big impact. But before we do, I think it's important to acknowledge that I think a lot of organizations see AI as a cost saving measure in some ways. And I just from, anecdotally, and from the sources that I talked to, in my articles, I feel like the challenge is balancing that automation and human side of things. Can you talk a little bit about how that plays out? And what in your world?
Raj: To me, I think the cost savings should not be the goal, right? The goal should be, hey, the way that I serve the customer and want to serve them faster, I won't serve them with the deep knowledge of what I have in my doing this for over a year, over years. And then how do we use all that and provide the best customer service experience within a short period of time? This is automatically going to result in cost savings, right? It's not about how can we get rid of five people using AI. That's a very wrong approach to AI right? The thing is looking for processes where AI can Oh, like one of the things that I mentioned, is this, right? like, I'm just working for the same company. And one of the things they do is they go and, like, create a knowledge repository by looking at your websites and create a, what we call a vector database for retrieval argument generation. So that when a question is asked, it goes and finds the answer, instead of somebody having to go copy and paste and then craft the response for you. To me, what it does is provide accurate results very quickly, and then you're addressing the customers’ issues and reducing the time. That's how we should look at it. That in essence, it'll automatically reduce costs, and improve customer satisfaction and provide the right information at the right time to the customer.
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Strategizing Automation in Customer Service Functions
Rich: Well, you hit one of the topics, right on the head, which is self service achieved by self-service technologies within customer experience support? How do you determine which customer service functions to automate, you know, with these technologies?
Raj: This is where again, as you can tell, right, there are several ways to do this. One of the things that I mentioned is that as soon as I intercept the email right at the beginning, I can use AI to see, hey, what is the intent of the customer? And what is it that they're trying to do? Let's say that, let's take a simple example. There's one guy who is saying that I'm willing to place an order for 10,000 units, that is a different intent and different purpose, which I'm going to elevate and probably use our automation to certainly extend but not fully. But on the other hand, somebody says, I'm trying to find some information about your product, this is a product. Now that's an automation candidate, I simply go to my repository, craft a response, put in some references and send the email to them. Right. So the ability to classify, apply rules and prioritize what needs to be done. And also, when should humans intervene based on the urgency sentiment, the value of the customer business rules apply. All that is where I see a lot of value with combining AI and humans.
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Enhancing Self-Service With Customer Feedback
Rich: I mean, to me, the way the self service is going, they're turning more into, like flexible resources. And I feel like if you're using these self service technologies, right, they in somewhat, in many ways, represent the voice of the customer, because these are, this is what you're getting from the customer? How do you actually maintain that and keep that up to date and keep that all current and relevant?
Raj: Yeah. So when we say self service, the biggest challenge we have had is that people like to start with our IVR, a bot, right, two or three interactions, you just want to shut that thing and just go talk to a human being because of the poor experience that we have. To me, this is where like we should be able to identify the things that people would emit. like, let's say, I want to get the status of an order, you know, I certainly don't want to talk to a person, I wish I could just go to the system and say, Hey, I'm the customer. And then it automatically uses my voice to verify who I am, and once it knows who I am. And it goes and pulls my orders and tells me Hey, I know what you're calling about because there's an outstanding order, here is the status of that to meet that self service. Right? So service is not a customer asking other questions, getting frustrated, not getting what they want to be proactive and using that, to me, those are the candidates concerns of it. However, there may be certain scenarios where I ask a couple of times the system doesn't respond, automatically escalate to a live agent, bring all the context and then tell the live agent, hey, this person is almost losing their patience. They've tried AI, but now let's let the human in. So kind of blending that based on the context is what we need.
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AI's Impact on Customer Feedback Analysis
Rich: It's so interesting, you just brought up another area where I feel like AI and machine learning are gonna have a huge impact. And it's in a couple areas that pertain to exactly what you're talking about. The most relevant, I think, is customer feedback and sentiment analysis. You know, I know that that is becoming more and more prevalent within the contact center. I'm just curious to know if you could talk a little bit about how you collect and analyze that customer feedback and get it into the system to make it usable.
Raj: This is again interesting, where with machine learning and AI, right, that combination of machine learning, so when I try to differentiate machine learning and AI, I'm talking about the traditional machine learning things — that we use algorithmic models based on input and training to get something right. And a great example is forecasting, like even though the large language models are trying to do forecasting. In the typical sense, what we do in forecasting is repeat the historical data, train it, and then ask it to predict what .. you know. And in the same way in the scenario that you mentioned, the translation is a well-proven machine learning thing. So machine translation, right. So as I speak, my things get translated. Now, I interject AI for summarization. So I may have today, I just talked to you about music and all that. But end of the day, we want to get to the matter and, you know, the crux of the thing. So in summary, I'm not going to talk about rock music interests. Somebody, I'm going to say, hey, this guy wants to buy insurance, right? That's the summary. And now the contact center person says, anyway, I'm going to get you a quote, in the next two days. Now, I automatically interpret that to find the intent, create a task, in my task system, send a reminder, so that all those mundane things of me having to remember to go create, oh, I told this person that I can. So we combined the machine translation for the speech to text, create a summary, create the task, and automated a lot of those things, combining AI and machine learning there.
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Exploring Beyond AI for Customer Sentiment Analysis
Rich: Are there tools outside of, you know, AI, the large language models that you discussed here, that are specific to the customer sentiment and analysis of technologies?
Raj: Yeah, I mean, a lot of these, there are so many options. Now for sentiment analysis. You know, one of the things that I'm noticing is that people are just kind of a little bit obsessed with ChatGPT, but then as the technology is developing just for sentiment analysis, there are several models that you can use. There are open source models that are available. Just for entity extractions, there are a lot of models available. So I think the people who are going to be very powerful in this domain are the people who are constantly looking for which technology to use, and how do you reduce costs? And how do you keep up with the evolution of these models, like today, I just read a leak from Mistral, they said, Hey, we have now GPT for a powerful model, and in what they call a quantized model, which means you don't need a huge NVIDIA hardware. They can run it on a CPU hardware, like these things are just happening every day. So if you want to be in this field and be successful, is this ability to pick the right technology and apply it to the thing at the lowest cost possible.
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AI Filters Noise for Actionable Insights in Contact Centers
Rich: Interesting, OK. So I mean, obviously, if you're running a contact center, there's got to be a multitude of different sources, and just tons and mountains of data that you have to go through. How is AI helping differentiate between the noise and what's actionable?
Raj: So that's the beauty of this, like, for example, you know, we I, you know, I could even show you a demo, like the one system that what we did is that the entire call logs of speech, and everything is put into the log. Now that a machine learning model goes, parses that, logs and extracts the entities and create a slice a bowl thing, I can go and say, OK, I want all the calls that was about a trip to Europe, let's if it's a travel agency, it is immediately able to get the data and to say where they were 15 queries about trip or airport trip to Europe. So being able to get into this huge amount of data, which was traditionally not possible, you know, even the database systems have changed, right? We always relied on relational databases. Now, think about the data lakes that are out there, and the ability to run things like Spark to analyze data at a subsecond, and extract all that, all those things that are being leveraged and then once you have this data, interpreting that using language models, right, so all those technologies are now available for you to say that, hey, I collected these millions of conversations and logs in terms of even the call center logs, when did the call start the average call time, take all that and make some meaningful predictions and suggestions to the operations as well as to the agent.
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Refining AI for Impact in Call Centers
Rich: So there's a lot going on there. And it sounds like you're constantly working to, you know, refine and improve. How does that work with within your organization?
Raj: So one of the first things is like, what are some of the things like, the way I do it is that, yeah, you can do so many things with AI. I always look for not saying hey, how do you start GPT? That's not the approach, right? Let's say in my call center, where are the areas where I could have the most productivity impact? Let's say, hey, you know what, training agents seems to be one of the biggest challenges? Can we use a knowledge repository that allows a new agent to ask a question and get the answer and almost talk like an expert? Is that an area that I want to do? like, let's say I would focus on that. On the other hand, I remember my early call center days, the biggest challenge that we had was call drops. Right? And that's because of the lack of understanding of the workload. Now, how do I use forecasting to level my workload so that I can trade off between my cost of labor versus call drops? Right now, that's a more of a different side of the technology. So the first thing you need to look at is where is your biggest opportunity in your contact center to have some impact that both financial gain or cost reduction and customer satisfaction, and now see what technologies can help you address that solution?
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AI Streamlines Omnichannel Customer Support
Rich: OK, that makes sense. So, you know, I think every organization is having to deal with the proliferation of channels, they're just everyday, there seems to be a new channel, a new place where you have and you know, I think if you're, if you're, if you're delivering a great customer experience, you have to meet your customers where you are. So it's not something you can sleep on. How is AI impacting the way organizations handle omni-channel support?
Raj: Yeah, to me, this is one of the biggest advantages of AI, right? So as I mentioned, with the company that I'm working on, one of the things that they've done is they created a knowledge repository by scraping the website, taking the document, and then being able to query and get an answer. Now, applying that to multiple channels is now so easy, right? What they do is that an actual, as a matter of fact, email comes from the email, they extract the intent, query the knowledge repository, craft a response now that for email channel, the same back end is now used for a voice. So what happens now, just like email, you make a call to the voice, immediate speech to text. And the speech to text goes through AI to analyze intent, and what is going to do is use the same repository, bring the response into the sending an email, convert that text to speech, and talk back to this person. So your back end remains all the work that you've done in terms of the knowledge repository, and everything remains the same. All you have is a different connector for the input right there. Now with so many advances, like take WhatsApp, right WhatsApp in the African, Asian countries, that's one of the biggest channels business. A lot of people believe it or not people transact millions of dollar businesses using WhatsApp particularly like places like India. And imagine this, now all I need is a connector when when I type something in WhatsApp, it almost treats like an email because all it needs to do is understand that a connector, get the intent but use the same back end the back end and respond back in terms of creating the text now, but connect to another channel, which is the other omni-channel, right? So if you build a system right, where your input is always the customer wanting something, your output is always a response to that converting to the multiple channels is no longer a major issue now.
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AI Enhances Continuity in Customer Service
Rich: OK, well, that's good to know. You know, it sounds like we've resolved some of my biggest problems with call centers, which is A) me screaming, operator, operator, operator. And the other one, which is having to repeat myself over and over again, when you go somewhere. I mean, I think that's one of the most frustrating things we've all had to deal with is, you're on the call, you're talking with somebody, they're like, Hey, I'm gonna connect you to somebody that can help you, you get to that person. And you have to go through this whole rigmarole again, where you're explaining the problem, and then they're asking you to do some of the same things that the last person asked you to do to get resolution. It's crazy. So it's good to see that AI is going to be tackling some of this and, you know, that's a, there's a lot going on with AI and machine learning in 2024 I mean, we're gonna it's just, it's about to blow up, I think.
Raj: Yeah, I mean, it's not only AI, like some of the challenges that you mentioned are the reasons for self service not taking off, bots not taking off, IVRs still having a struggle, right, AI is not going to solve all of that. You need other subsystems. like for example, call escalation means that when I'm talking to you, or let's say I'm doing a self service, I should have the system to capture the state and what happened, and then be able to as a transfer on abled, it's clear that that's not purely an AI thing. There are systems that you need to build to have continuity and then leverage AI. Hey, like, like, whoa, a great example. I tell you, I sent you an email, right? You don't respond to me, and I send you another email. But what he can do is, it can look quite similar. And you know, this guy already asked this. And imagine I craft a response, I apologize, it looks like you approached it a couple of times, and we didn't respond. And here is the response. Imagine you're doing that right, the impact that it has, AI can help out. But you still need somebody's infrastructure to track and see how you get all the context to AI to be able to give you that right response.
Challenges of Implementing Predictive Analytics in Business
Rich: This is great stuff. The next area that I wanted to talk with you about is an area where I feel like there are a lot of issues going on. I feel like a lot of organizations are struggling to use predictive analytics. I think that that is a major challenge, at least from the people that I've talked to and the sources that I've done stories on. It's like, they're all like everybody wants to get on to the predictive analytics train. But getting to where you get actionable insights that are actually worthwhile is quite a challenge.
Raj: I mean, so you know, the like, actually, that's a more of an established machine learning area, right? But surprisingly, I'm seeing more and more, the AI is almost taking over everything, right? I used to think that if I needed to do a forecast, I needed to get historical data, and then feed it and all that. But now I just saw the other day, where you tell the thing, hey, can you just predict the, you know, sale of appliances for the next four years. And you're asking a ChatGPT, or this thing, this thing is able to actually come without me providing some of these things, looking at patterns and the existing data, whatever it has, and be able to actually create that Arriba model, the forecasting model, right. So AI is almost creeping in into more of this. But I still believe that traditional forecasting, having the right input data, modeling it and knowing the variables, that the whole hyper parameter, the tuning the model, that is still an area that, you know, it requires a bit of a machine learning capabilities. To me, the contact center is one of the major, major things, right, in terms of predicting your load, how many agents should I have? And what times and what like, you know, there was a project that I did I remember, we were just trying to predict the inflow of customers into a fast food restaurant, there are so many things that goes into it, one of the things we did was a real time camera, watching the inflow of vans, right to say, on an average, four people in the van, now I started five in the van coming and I'm seeing a trend go because there's an event. Now that's a real time impact. So when you talk about particular call centers, right, something is happening, there is a, you get a lot of, so modeling and all that it will always remain a complex thing. So you're going to need some solid machine learning capabilities and scientists, data scientists to do those types of things.
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Transforming Contact Centers With AI in 2024
Rich: Raj, I think we are just about out of time, I think that we probably could have gone on for another 30 minutes. There's some areas I didn't even get to that I wanted to talk about. But thank you so much. Before we go, what do you want people to know about what's gonna happen in the contact center in 2024.
Raj: I think you know, you know, it'll be really a missed opportunity if people don't take advantage of like, there are several areas that you can take advantage of things in the customer improving customer service, using AI for creating a knowledge repository, and being able to use things like ChatGPT, or queries, leveraging your own data, you know, like supplementing the language model with your own data to make customer service easier to meet, that is a tremendous opportunity. I think it's going to change the way that we serve customers, higher customer satisfaction, less time to resolve issues. I think this is the time to act, the only thing I want is don't get stuck with a single product, don't get it because the things that are happening is so rapid, try to build a flexible, cohesive system where I can let’s say, tomorrow if I want to remove the Llama 2 with something else, you should build a very flexible system so that you can be evolved with the technology.
Rich: (29:25) Raj, thank you so much for joining us today on this very first episode of “Beyond the Call.” I would love to follow up with you more where can people find you and follow you?
Raj: Oh, you know, I'm not a big Twitter guy or anything. And I sometimes I feel guilty I take advantage of everything. But I don't seem to give back enough. But you know, you can find me on LinkedIn and if you want to get in touch with me, I'd be happy to connect with people. If you have any questions. I'm always happy to share my thoughts.
Rich: Great. Well, thank you again for joining us. This has been great and we will see you next time on “Beyond the Call.”
Raj: All right, thank you. Thank you for the opportunity. I enjoyed our conversation.
Rich: Me too. Thank you.
