In this episode, our host Anika Zubair chats with Declan Ivory, VP of Customer Support at Intercom about how AI is transforming the customer support department.
The future of customer support departments is rapidly approaching. So, what changes can we expect in 2023 and beyond? What skills will support agents need to keep up? And what key performance indicators should we be tracking when proactive support becomes the norm?
Podcast enquiries: sofia@planhat.com
In this episode, our host Anika Zubair chats with Declan Ivory, VP of Customer Support at Intercom about how AI is transforming the customer support department.
The future of customer support departments is rapidly approaching. So, what changes can we expect in 2023 and beyond? What skills will support agents need to keep up? And what key performance indicators should we be tracking when proactive support becomes the norm?
Podcast enquiries: sofia@planhat.com
Hello everyone, I'm your host Anika Bert , and welcome back to the next episode of the Customer Success Channel podcast, brought to you by Plan Hat , the Modern Customer platform. This podcast is created for anyone working in or interested in the customer success field. On this podcast, we will speak to leaders in the industry about their experiences and their definitions of customer success and get their advice and best practices on how to run a c s organization. Today we will be speaking with Declan Ivory, who is the VP of customer support at Intercom. He's an experienced senior leader with a passion for building and developing high performing teams and applying digital technologies to support organizations through major business transformation. Prior to his time leading Intercom's customer support team, Declan has held senior support leadership roles over the last 10 years with Amazon Web Services, Tableau Software, and Google Cloud. And today we will be chatting with Declan all about the transformation of customer support in 2023 and how AI will be changing the landscape of customer support in the future. Welcome Declan to the podcast. I'm so excited to have you with us here today. Before we get into today's topic and into the depth of our conversation, I would love for you to tell us in your own words a little bit about yourselves, what it is you're doing at Intercom, how you started to work in support. Just give us a little bit of background about who you are and what it is you're doing.
Speaker 2:Hi Anika , delighted to be here and thank you for the opportunity to share some time with you. Uh , a little bit about myself. So Declan Ivory, I'm VP of support at Intercom. So I kind of maybe talk a little bit about Intercom at the end, but talk a little bit about how , how I got to Intercom, which is kind of how I got into customer support . So I'm an engineer originally by profession, but went to work in the tech industry straight from college and pretty soon I kinda had a realization that ultimately I only get paid at the end of the month because of customers. And if we don't keep, if we don't keep our customers happy, then you know, it , it's bad for the business. So from very early on I've had this kind of very strong customer obsession, customer focus , and generally I've had support related roles literally for the , the entire 35 years that I've been in the tech industry from kind of being, you know, a technical specialist originally, then moving into kinda managing internal support teams and then managing external teams initially in the telco, but then moving on from there to work for the likes of Amazon Web services, Tableau software, Google Cloud. And I've ended up in an intercom at a pretty interesting time. I think in terms of intercom's evolution. So Intercom is a , an AI first full function customer service platform. And for me it's really interesting cuz I get to use the same tool that I'm supporting from my customers. So to some extent I experience a lot of the, the challenges and issues firsthand that my customers experience and it means that I can be a very genuine voice of the customer back to the rest of the organization and really influence the , the product roadmap. And that's what really makes this role in Intercom I think probably more interesting than any other role that I've had. Cause I can have a very significant impact on the product roadmap.
Speaker 1:Awesome. And I love what you just said, that you realize really quickly that keeping customers happy is how you get your paycheck at the end of the month. I think that that needs to be like a , a poster that goes up into the offices. But you mentioned that you had an engineering background and you kind of just jumped into that right after school, but then you've now ended up in customer support. What kind of spurred that transition? Why customer support? Why not into the backend side of things? Why, why customer facing ? As
Speaker 2:I say , I've always been kind of obsessed by customers and realizing that, you know, any business doesn't have any value without their customers. So I've always wanted to make sure that, you know, delivering well for customers. So I really prefer customer facing roles than non-customer facing roles. Uh , apart from anything else, like when you deal with customers on a day-to-day basis , you really get to understand what are the real challenges and issues that they have. And because I'm an engineer, I've kind of a , a very strong problem solving kind of background. So I love to solve problems for customers. That's ultimately what gives me the most fulfillment and and satisfaction. So that's why I'm in customer support. Cause every single day there's problems to be solved for customers. <laugh>,
Speaker 1:I completely believe you and I've been in a number of organizations where the onboarding of a new employee has been to work in customer support for a day and to actually see tickets and what it looks like from a real perspective of what our customer's real problems. And I think it's a huge learning and I think that, like you just said, there's a problem to always be solved and you're always upfront and close with what your customer actually wants from your product, which I think is key. And you mentioned that you also use Intercom at Intercom, which is great. I've been at a a few organizations where we do that so we can really understand and live and breathe our, our product and what our customers are doing. But tell us a little bit more about this organization at Intercom, what you're building, what, what your remit is. I'm sure it's quite large Intercom being a support tool as well. But let us know what is it , uh, that you're doing there and, and what does your team look like?
Speaker 2:Tell me a little bit about Intercom. So it , it's an Irish founded company, about 11 years old and its mission , uh, was to to make internet business personal , uh, but very focused on how businesses engage with their customers with a particular focus on customer service, customer support. And at the moment our focus is to really build an AI first , uh, full function customer service platform that is considered best in class in the market. And you know, the philosophy in Intercom has really been to use innovative technology throughout its history. So a lot of, you know , uh, the , the Messenger technology was quite new and uh , Intercom product to the market. Uh , Intercom was very quickly into chat bots and , and evolving there , applying machine learning techniques as well within the product and has obviously adopted some of the recent changes in ai, particularly generative generative ai and a particular , uh, chat gpt and GPT four . In fact, we were the first commercial organization to announce a product after GPT four was launched as would error . We , we had a product announced, so we'd be very focused on innovation within the , the product set . So for me then, from a customer support point of view in heading up the customer support team, which is a global team, it's based out of Dublin, Chicago, and Sydney. So we can provide full follow the sun support for our customers, but we, you know , get to use the technology, which is great. You know, we , we uh, can be early doctors of the technology but we also then have to make sure that we have all the skills in place to support the , uh, complexity of the environment. Cause it is a complex environment but it's complex because it provides a lot of functionality, a lot of capability. And again, it means that I have to really focus on uh , on the skill sets within the team, making sure that we can handle all of the various kind of , uh, complexities within the product itself and also how our customers integrate that product with other tools and other systems that they have. So it's really interesting space in that point of view. Uh , there's a wide variety of kind of , uh, customer questions and issues that we handle on a day-to-day basis. So, you know , part of the role is, is ensuring that we are upskilling the team all the time and that we have, you know , full visibility and knowledge of all of the product changes that we're bringing to market.
Speaker 1:Awesome, awesome. And customer support is something that isn't new but I, like you mentioned, there's so many different facets to it and it's evolving so much and there is a real transformation happening in customer support, whether it's with AI or with other parts. But I know we're gonna talk more in depth of that, but I think support has always been seen as support agents answering tickets, triaging tickets, you know, reacting to something that's gone horribly wrong within the product or you're always fire biting and you're always trying to like just answer something just in time before a customer really explodes at you. Which <laugh> I think is traditionally what everyone thinks of support. But I know that we're here today to really talk about the transformation of support, what it is becoming, how AI is changing that, how your organization is changing. And you've obviously seen some of that change happen at Intercom recently, but I'm sure with the time you've been there, what are some of the first steps that you took when you moved from let's say reactive to more proactive support? Cause I know that's what you guys are doing there. The
Speaker 2:Big challenge is to change your mindset. And you kind of touched on it, you know, very much traditional support is around almost a transaction driven environment. Like you've got an issue coming in, you gotta solve it , you're trying to hit an average handle time target, you're trying to just close out the , the , the issue. And when you have that kinda transaction focus, you're not really looking at it well what is the overall customer experience? What is this particular customer experience in using my product or my service? Right ? And that's the big switch that I've tried to make. You know , try , try to make sure that people are very much focused on what is the overall customer experience and , and one of the benefits of a platform like incom, you do have full visibility of all of the customer interactions and once you have that visibility, you get a really good sense then of what has the experience been like for this customer. And, and really getting people to take that customer perspective rather than a transaction perspective is probably the first thing that you need to do in moving from reactive to proactive. Now moving from reactive to proactive, you also gotta make sure that you're enabling your support team to understand well what's the next kind of bit of advice or guidance they can give to a customer that's going to be kinda proactive. And as an example, we have implemented what we call customer milestone framework where we actually track how well customers have adopted our product and you know, there are kind of what we think are best in class steps around how a customer should be using the , the product. And we actually make sure that that information is available to anyone on the support team when a customer opens up an issue or a conversation. So basically we have an app built into our platform, it bends all this information around where the customer is and the customer milestone framework. We have predefined actions that a , a support agent can take to say to the customer, Hey, I noticed you're at step, you know, two outta three, oh , here's what you need to do to get to step three in terms of enabling this function or this capability. And that kinda almost consultative practice support is really valuable to , to the customers. Cause in providing it, you're very often, you know , ensuring that there isn't gonna be an issue down the line. So from our our point of view, we are probably avoiding a contact down the line. We're helping the customer accelerate in terms of adoption or use of the platform , which ultimately delivering value to them from a business point of view. Cause you know, they've made an investment in the platform, they wanna use the functionality and sometimes there are blockers to them doing that and having this kinda more proactive approach and particularly where you're looking at the overall customer experience, you can really add value in that context and you move from this transaction mindset to actually a customer experience mindset. That's
Speaker 1:Such a great thing to share, but also so simple in its nature. I think that we always think of like support, like you said, transactional like problem solution. And I think that that happens naturally when you're in that mode of like, I'm presented with something, I need to figure out the problem and the solution to it. But I think that the full customer team now I'm used to working on the customer success side of things, but the full customer team, anyone who is customer facing , if you are looking at the full journey, like you're mapping out and really understanding, hey, if you are with our product for six months, these are kind of the tendencies that you should have. These are the use cases you should be doing. Understanding that whenever you're customer facing , whether you're an account manager, a customer success manager , uh, a renewals person, an upsell person, a support person, I think it just, it makes a big difference to change that mindset of, okay, I'm here to do my job versus I'm here to make the full experience flawless for the customer. And I think we get so fragmented in the customer journey and and and in within an organization as well . Well if you think about it, we're all, you know, there to do our day job and we almost forget what is that customer journey or like you said the customer milestone framework. Can you give us a little bit more insight? I know it's hard to just talk about things without examples, but what does that framework look like? What are those milestones? How did you guys come up with that?
Speaker 2:Again, it was strong collaboration across the organization. Like , uh, you know, a lot of it was driven by the customer success team who, you know, deal with our customers on an ongoing basis. And also we have onboarding specialists as well. So a combination of onboarding specialists, success managers, and support people, we were able to kind of take a view of well, you know, what does good look like in terms of how a customer is using our product and what's the expectation of what features and capabilities they should be using after, you know, two months, three months, four months, et cetera . So you can actually map out very clear, you know, to activate the product then to get value from it, then to have a kind of mature implementation. And we've been able to build out a whole framework around what's the expectation of where a customer should be at a point in time. So it's really looking at, you know, it , it may be what particular features are they using? So have the , you know, in the first few months there'll be very basic features you'd expect them to use like the inbox, the messenger, et cetera . Then over time you expect them maybe to start to use some of our AI capabilities to drive automation. Maybe some of our , our more complex features like workload management, et cetera . So you have all that mapped out in terms of what's the , the timeframe for our customer, like what level of activity should they expect after a period of time based on the number of seats that they have . And so there's a whole lot of different metrics to feed into , basically say this is what a healthy customer looks like versus this is what basically, you know, unhealthy looks like and we probably need to help this customer and , and , and encourage them to move further down the the adoption path. Cause if say ultimately it's about them getting value from the investment that they've made, right? Uh , and you know, if they're not using features and capabilities, they're not leveraging the value that they've made the investment for . So, you know, it's really important to give value to the customer through this process.
Speaker 1:Yeah, I love that. And it comes right back to that customer journey that we're talking about and it , the importance of all of that and just the importance of coming back to that, it's as simple as that. Sitting down with cross-functional teams mapping out what the heck your customer is doing with your product and what you would expect them to do in an ideal world, how would they perfectly use your product and then work backwards from there. Or like you said, fill in the gaps based on each different department and how they can help reach those goals. And I think that if you ever get confused or if you're ever unsure how to really be proactive with your customers, really map out the perfect journey for them and then work in that way. And um, I think that you've talked a little bit about how you've shifted that reactive to proactive side of things, but I think the traditional way of delivering support is like get a ticket, enter a ticket. That is, that is what I think of and that is all , most of my career. How support agents work. Is there some ways that you guys are changing that mindset at intercom or doing different ways that are supporting customers that are outside of those ticket answering ways?
Speaker 2:Maybe take a step back. So you've described, you know, the whole transaction piece and you're waiting for things to come . It's almost relentless from a support point of view is the term I use. And part of what you're trying to do is how do you free up time for your support team to actually spend time with the customer , uh, in a more consultative way. So for us that has been really looking at what workload is our team doing, what workload is so valuable and important that it needs the skill and expertise of a human to handle it and it needs that level of empathy and interaction with the customer to really, you know, give value versus what part of our workload should we be actually solving within the product ID issues should never occur in the first place or what part of our workload can we actually automate away ? Whether that's true kind of , you know , traditional machine learning techniques or whether it's using AI kind of advances that have happened in the last few months. So we've focused a lot on basically taking work outta the system, you know, through eliminating it originally or handling it in an automated way. So we actually free up cycles for our support team to spend time and , and focus on understanding, I say where the customer is and being able to think about what's the, the next best action to recommend to the customer to kinda , you know, get , get them more value of the product. So freeing up time has been the most significant thing that, that we have done. Cause you know, it is relentless and aligned with that . We , we are now , we're in the middle days , we haven't done a bottom yet. We need to change our whole view of metrics as well for the support team. Like if you just leave your traditional metrics in place, then you're not really getting your team into a space where they feel they can spend time and effort consulting with the customer and maybe taking a little bit longer than they might have done in the past. So that is really important that we think about metrics differently. And I know we're gonna move on to talk about AI as well. But you know, the other thing that's really coming to the fore as well is that you want your support team actually to build a lot of knowledge as well based on how they interact with our customers. You know, ultimately they should think of every customer interaction they have, ideally will be the last time that particular issue occurs in that you drive through root cause you develop either knowledge or a solution to make sure the problem doesn't happen again. But again, to free up your team to do that means you gotta get bandwidth, you gotta make sure that they have time to do that and the reward is for it as well. Like the metrics should take account of that. And then, you know , there are some skill sets as well that they need to be able to do both the consultative piece and the knowledge management piece as well. So there's a lot changing in this environment and a lot of it is driven by some of the advances that we've seen recently in the whole AI space.
Speaker 1:Yeah, and I wanna get into that, but one more question around just support tickets and how you're freeing up time for your agents and , and what you guys are doing there. Is there a way that you are being consultative when you're either closing out a ticket? I'm just wondering because even a customer thinks of a support ticket transactionally, are you doing anything different when you're closing out a ticket, whether you're answering a ticket, any, anything that you're doing to also shift the customer's mindset? Because I think it's, it's a two-way street in the sense that a customer sees it as transactional. How are you changing the customer's mindset when it comes to support ticket and reacting to, to those tickets?
Speaker 2:So I , I think the customer milestone framework that I mentioned earlier, it's a two-way street. Like , you know, the customer has to engage with, you know, the, the interaction from the support team member as well. So it is actually encouraging the customer to think slightly beyond the current problem. Uh , you know, so that's how we're changing, you know, customer behavior over time as well. Uh, like we typically have always closed out our conversations with a very open-ended, is there anything we can do? Is there, you know , any other issues that , so we do invite the customer to go beyond the current transaction. We've kind of done that historically, but now we're actually putting some meat on the bones for what a better way describing it where we're actually sharing with them, hey, here's something that we think you, you might wanna look at or that , you know , maybe you're looking at already, can we help you with it? So you're beginning to make it a little bit more specific then for the customer cause of this information that we can now provide to the support team . Something like the customer milestone framework is a two-way street . Like the customer has to engage with it , has to understand it , and I think it opens up their mind as well then to really think beyond the current transaction does take time to sh to change people's minds. Like, you know, we don't get full engagement with it from all our customers. Uh , we're all the time thinking about how , how can we make that engagement a little bit better, get more of our customers to , to uptake , you know , the recommendations we're making. We still got pretty strong performance outta it, but we're all the time trying to tune it and see, you know, how can we get our customers to engage more, more proactively with us as we engage proactively with them.
Speaker 1:Awesome. And just one other question on that, as you guys are being more consultative, I'm just thinking how that's almost crossing a little bit into the CSM land, but I'm sure that there's boundaries around what support is doing versus what a customer success manager is doing versus the onboarding specialist you mentioned. Is there ways that it almost crosses boundaries when you're becoming almost too consultative and you passing it back to another team member? How do you guys manage that so that it's not, let's say confusing both to internal teams but also to the customer of who they should be interacting with? Yeah ,
Speaker 2:It it's a , it's a really good question and I , I don't have a complete answer for it to, to be honest, right? Because I think there are blurry lines between customer success, onboarding and support. I think as we move into this particularly AI driven world, I think there there's more activities from those three domains can actually be automated, can potentially be handled by a single human, you know , somewhere along the line. But then what it's doing is it's freeing up other people, whether it's an onboarding specialist or a customer success manager to actually undertake the activities that are really value adding from the customer perspective. Cause again, if you look across those three domains, there are activities that are kinda more transactional in nature that are , you know , very open for automation and very open for, you know, part of the activity to be driven by someone in a , in that consultative support role as opposed to having to be handed off to a onboarding specialist or a success manager. But it doesn't really undermine the, the value that those two roles provide for our customers. But it is a little bit blurry and I think as you know, predict this technology evolves. There will be activities that can be centralized into a single human, you know, whether we call them a support person or what our service person, I'm not sure what we call them , but they'll be able to undertake maybe more activity than they can do today. But there's still this high touch , high value piece that success managers deliver and onboarding specialists deliver.
Speaker 1:Awesome, awesome. And we've touched upon this already a few times and I wanna dive much deeper into it because I think when everyone thinks of AI or machine learning, they think of it almost replacing support or you know, what's the point? We can have something like a bot or an AI machine learning person that's gonna come in and really transform support. Now I know you have your views on this, but how are you guys utilizing AI at intercom with the support team where it's something that's supporting the support team rather than replacing? Yeah,
Speaker 2:No re really good question and , and maybe a little bit of context. So like I've been involved in support for a long time and I've been really frustrated by the slow pace of the support industry to adopt technology, particularly when it comes to , uh, machine learning and , and AI et cetera . And I think what has happened in the last few months, the the technology has matured to a degree that you can now actually think about implementing it pervasively, but it's not implementing it to replace humans, it's implementing it to complement the human support experience. Cause it's different dimensions through which AI can be applied. And I'll can talk a little bit about what we're doing here at Intercom, but they're , they're three dimensions for me. And the first dimension which gets most focused is customers dealing with AI via some kind of bot uh , experience, right? And , and that's definitely a , a really good use case for , for AI technology and there's lots of very simple transactions that can be automated through this approach and actually customer gets an answer far more quickly than they would ever get if they had to go through to a human support person. So that's a real advantage for the customer and it's a real advantage for the support team cause it's freeing them up from all those kind of mundane routine questions that they kinda get, they they by day that aren't very fulfilling to answer and are kind of consuming bandwidth that they could be using more beneficially for the customer. So that's kinda the AI from the customer lens. Then there's AI from the , uh, teammate lens as well because AI can assist teammate . Even simple things like summarizing a very long case or a very long conversation, you can use AI to summarize it . Really important, if you're doing a follow the sun model and you're handling handing over from one support agent to another, it can change the tone of response. You might craft a response, but you might wanna make it more formal. It can change the tone of the , the response to be more formal. You might wanna make it more casual, you can change it to be more casual. You can take bullet points and expand them out into kinda a , you know , a more comprehensive reply for a customer. You can use what we call smart replies where, you know , AI can suggest to a support agent here's, you know, possible answers to the customer question, not exposing directly to the customer but allowing support agent to take that as a guide and help them craft the final response to the customer. So AI helps the, the support agent as well. And AI also helps, you know, people who are running support like myself, support leadership and management in that, you know, using AI for example, you can do very comprehensive analysis of all your cases or conversations. You can gain a lot of insights, you can help pinpoint where you need to drive improvements. You can generate things like sentiment analysis across every single case, every single conversation you have and not just relying on CSAT responses. So it does have the opportunity to really transform how you deliver support. And people do focus a little bit on, well, you know, it's gonna drive cost efficiencies the way I term it is it changes the economics of delivering support is what this technology does. Uh , but it actually frees up a lot of time and bandwidth for your support team to actually add a lot more value from the customer perspective and actually become really value adding from a business point of view as well, as opposed to many organizations which view support as a little bit of an overhead today cuz it is transaction focused . So that's kinda a , a kinda philosophical view of , of where I see the technology and and where it can be applied here at Intercom. You know , we, we've done two things. So pretty soon after chat g PT was launched, we kind of assessed the technology and kind of said this is actually a fundamental change in terms of how AI works and it's something we should really try and understand where does it play within our own product and how can we deliver value to our customers? And the first place where we looked at it was what we called AI in our inbox. So inbox is where we managed conversations with our customers. So we launched AI features, I think it was back in February this year, which included that text , our case , uh, summarization, our conversation summarization piece, the tone change , uh, tools as well . And I was really delighting, my team didn't view that as something that was inhibiting them in any way. In fact they, they felt they were being empowered by having these tools available. They actually felt it , it made them more productive, et cetera . So we got really positive feedback from our team in using the AI in the inbox tools. And then we decided to launch an an AI bot based on GPT four technology. And again, my team were very instrumental in testing the technology before we ever delivered it to, to our customers. Uh , we were able to provide a lot of feedback to help shape the technology and basically what it has done is it has allowed us to, because we, we were kind of basically the first beta customer for it . Uh , we were able to deploy reasonably quickly cuz this is the only thing that, you know, customers have. I want to be kinda , you know, very considerate about how, how I apply this. And yes, you do need to be considered about how you apply it , but in our case, like we re trialed with one customer segment, we saw the positive reaction from our customers. We saw the positive benefit in terms of resolving a lot of our mundane work , uh, automatically. And we rolled it out literally within a matter of days to the segments . Now the one thing about the way we've deployed technology is like , you know, AI or Chachi , bt it can hallucinate, right? It can make stuff up and clearly in a customer SupportPoint or uh , environment , you don't want to make stuff up for your customers. So we've kind of constrained our AI bot in a positive way. It will use the knowledge space that you have in your system. So in our case it's our help center. All of the information in the help center is used by the AI bot to determine an answer for the customer. So we've constrained it to a knowledge base that is already verified, you know, that you know , is high quality and ensures that the information being provided to customer is actually accurate and it , you know, basically reduces or close to eliminates the opportunity for the technology to hallucinate. So that's one of the, you know, the key guardrails that we put in place. Cause you know, I'll say when chat BT came out and the large language models it , the , they do invent stuff up, right? It's just the nature of how the technology works and putting those guardrails around it has been really fundamental from our point of view. And I can very simple terms like we've deployed the technology and , and without too much tuning and without kinda really thinking about , uh, augmenting our knowledge base in , in , in any kind of comprehensive way, we've taken out about 25 , 30% of our kinda mundane workload literally out of the box. So that is transformational. You know, the stuff that we'd done before was kind of , you know, you take out two or 3% of your work, you know, through an initiative, but to have an initiative where we've basically been able to deploy something in a matter of days and take out 30% of your work, that is transformational.
Speaker 1:Definitely. Definitely. And I love how you've already highlighted how ai, machine learning chat sheet pt , like everything that is happening in our world right now is something that's complimentary, something that's helping , something that is transforming the way we work, but in a way for delivering support in a better way. I would say, I think when everything came out around ai, especially when chat c p t like, you know, went viral and everyone's like, what is this? What are we doing with this? Everyone was so fearful that this is gonna replace our jobs. It's going to suddenly take away from our day-to-day or make our jobs redundant or just, you know , fear of, you know, technology replacing humans. But like you said, you have to put up the guardrails, you have to decide how AI is gonna work with your product, with your customers, with your team in order to see it successfully transform your department. Which I think is so critical for anyone who's trying to implement AI or thinking about using AI in their customer support organization. I think it's something that's complimentary, but something that you have to decide how you use it and how it's gonna empower your team.
Speaker 2:Absolutely. And I say it changes the economics of support as well. You know, an example in the past, like I've been trying to scale support teams and literally you could not hire people fast enough, right? And yeah , and with this type of technology you can scale your business without having to scale your support team at the same rate. Like that's a really fundamental change in the economics of support as well.
Speaker 1:Definitely. Yeah. And I think that it's something that is very relevant in today's economy in today where businesses are really looking at every dollar spent and also how can you optimize every dollar spent? And when you have something like AI driven support and AI behind the way you're interacting with customers, like you said, you can be bringing on tens of thousands of customers across the world, but you guys are lucky enough to follow the sun model, but some people might not be. And how can you implement AI to be able to compliment that as you grow? It's not something that'll replace everything down the line, but as you're scaling and growing have like a backup plan with AI in place,
Speaker 2:<laugh> and , and the other thing , at the end of the day, the technology is only as good as the knowledge that it's fed, right? And we still need people to develop that knowledge, develop that expertise, we need to have subject matter experts and there are many situations where customers will want to engage with that subject matter expertise. So at at one level the support role is actually going to evolve to be more technical, you know, you're gonna need more subject matter experts, you're gonna need people with a stronger troubleshooting skills, stronger customer engagement skills. So yeah , like the , the , the transformation is impacting the human support role as well in a very positive way. The always gonna be far more fulfilling for people going forward.
Speaker 1:Definitely. And you've kind of already segued into another question I had, which is around the support agent skills that are needed in 2023. I think, you know, a year ago very different skills were needed definitely 10 years ago, but I think a lot of people see support agents as someone who's transactional, someone who can problem solve, someone who can quickly answer a question. And we've already talked about some skills that are, let's say, newer or transformative to a support agent role like consultation and thinking beyond the ticket itself, what would you say are the key skills needed, especially with AI involved that a support agent needs to have in 2023 ?
Speaker 2:Really good question. So I , I think the first one for me is troubleshooting skills. Like the, the issues that are gonna come true to a support agent in any business are gonna be more complex, more nuanced cause the easier stuff has been taken out. So trouble, sorry, troubleshooting skills are gonna be critical. Uh , and any way that you can help your team to hone and improve their troubleshooting skills, that's gonna be pretty key. Uh , the second thing is that people are gonna become subject matter experts in particular areas of a product or service or your , whatever your , your business offering is. That's the second thing. You know , rather than being generalists, I think people are going to develop subject matter experts in , in components or parts of , of your service. And the third area is really having, you know, I call it kinda almost like a curiosity mindset like tr trying to think through why did this customer have to come through to me in the first place? What was the issue and how can I ensure that issue doesn't happen again ? And a lot of that will be around building some kind of knowledge artifact that goes back into your help center that then helps ai, you know, resolve more problems down the line. There are kinda three of the key areas that I see it's around troubleshooting, becoming subject matter experts and having that level of curiosity that you can actually build and develop the knowledge that ultimately feeds the AI machine in the background.
Speaker 1:Yeah, I love those skills though because I'm just smiling to myself thinking, wow, that's something that I hire for in customer success too, like curiosity, subject matter experts. So I think coming back to those blurred lines, it's interesting to see how we're all becoming one customer organization and how we're able to service our customer but with different like skill sets in mind. So I really, I do appreciate you sharing those and as support evolves and AI machine learning gets more involved, I'm sure some of the KPIs that you guys are tracking are going to change, like, like you said, to be consultative. It's no longer important to answer a ticket as quickly as possible close out or resolution as clo quickly as possible, but at the same time you do wanna find a resolution as quickly as possible. So what is it that you guys are tracking from a K P I perspective when you're thinking of proactive support?
Speaker 2:It's evolving. Like we're we're in in the kind of the process of changing your KPIs and kinda more traditional to, to different KPIs, you know, as , as an example when it comes to that proactive piece. So we measure if the customer that was engaging with the agent, what percentage of the time where there was a clear kind milestone opportunity, did we actually provide that opportunity to the customer, right? So we , we track the percentage of time that the agent will make a recommendation. Now there are some situations where it's not appropriate for the issue to make the recommendation cause the customer might be very alright , right ? May not be in a kind of a , a mindset that it's actually gonna be productive to say, Hey, did you think about implementing this particular feature? So there is a judgment called by the agents , but we do track what percentage of time where there was an opportunity to make a proactive recommendation. Did you make a proactive recommendation? So that's the one KPI that we track and then we track of those customers where you made a proactive recommendation, what percentage actually took action based on that recommendation. So there are kinda two metrics that we track for that proactive piece. Now we still have the challenge as you say , like you still have to worry about average handle time and productivity because you still have dimension, you're , you're supporting. So we're actually in the process at the moment of reevaluating all of our handle times. Uh , because I say of the work that we've taken out, the mix of work that's coming into us is different and we're actually relining all of our average handle times right here, right now at the moment where we're in the process of it. And we're gonna start to build up with what what does our new capacity plan look like as we look into the future and what does it mean in terms of , of how we might pitch the metrics for our team . And really, you know , we're gonna end up with a bundle of metrics . There are many support organizations have like almost like a key metric, like, you know, how many conversations did you handle per hour? How many cases did you close per week or , or whatever. And what I'm trying to move is we have a bundle of kind of metrics for our support team that will be a mix of some of that transaction kind of mindset because there still will be things coming through that you , you wanna handle in , in , as you say , in a faster manner as possible. And then you wanna encourage the proactive piece as well. So the metrics have to, there will be some tension in the metrics, but naturally where you're allowing almost agents or support team members to make autonomous decisions around, okay, I'm gonna consciously help this customer . I know it impacts one kpi, but it's actually gonna improve another kpi and I know, I know I'm gonna be measured on the bundle of the KPIs now it's gonna take a lot of time to tune that and get that right, but we gotta move away in , in the support world from a single metric that we drive our team off to a bundle of metrics where you're allowing people allow a level of autonomy to do the right thing for the customer and it doesn't impinge on their performance.
Speaker 1:Yep . I completely agree and I think I love those metrics about looking at the holistic approach rather than the quickest response. But you have to find balance, like you said, there's no way that you cannot have a support to get go unanswered as quickly as possible, but you at the same time have to think of the bigger picture and that framework that we talked about earlier. But again, just thinking about how can we slot into the bigger picture of customer journey, how can we provide better value and service and how can we make our customer use our product even more by answering this ticket in that way? So thanks for sharing all those pieces. I wanna ask one more question around the AI piece that I just came to mind, but you were saying how AI supports the, the customer agent and how it's something that you have to put guardrails around something that you have to really build, but a lot of people also see bots and AI as something that's unfriendly service or very un like something that's not human, something that you're interacting with that is truly a bot. You're, you know, that you're not gonna get really good interaction with. I think about it all the time. Sometimes I'm chatting and I'm like, can I just talk to someone? This is this , this bot is really not gonna do much for me. But how are you guys finding that balance of AI is helping personalize customer service rather than make it more transactional? Yeah,
Speaker 2:It's a , it's a really good point and I think it's one of the big benefits that's happened with the whole large language model and the kind of G P T technology that technology can obviously search a lot of information and bend , you know, a response. I think the key change in that technology is the way that it engages with a customer. Like our , anyone engages with technology, it is very conversational. It will build the conversation with you. So if you ask a question, it can ask clarifying questions, right ? So straight away , e even without thinking too much about it, the , the nature of the technology is the conversation or the interface with the customer has radically changed and it does feel more natural than traditional chat bots. So that's just one , one of the , the benefits of how the technology has evolved. However, you've gotta look at the customer experience end to end , uh, because it can be a combination of a bot, it can be a combination of human support and it's not a case of, you know, today in the support world we quality assured the agent performance and we say, did the agent do a good job? Like was the tone right? You know, did they , uh, were they polite to the customer ? There's a lot of things that feed into how you look at the agent performance from a quality point of view and Peter saying , yeah, we now need to quality assure the bot performance. Yeah, you do, but let's look at it differently. Let's quality assure the customer experience end to end . And let's think about from the time the customer decided to engage with you to the time the issue was resolved, what was the customer experience? And one we've gotta find mechanisms to measure that, you know , uh, cause it's not just around the csat , you know, if it goes through , through human support, like what happens if it's just handled by the bot? What was the CSAT for that part of the journey? Um , also, what was the flow like? Like was there a natural flow from, you know , the bot to human support? Like that needs to be seamless, it also needs to retain context. So everything that the customers provided upfront should be available to the human support agent. And you need , uh, you know, an ecosystem behind , uh, these tools to do that. Like you can't just , uh, you can't implement an an AI bot isolation and not think about how it links each your ecosystem. So all the context of that customer is available to the bot available to human support. And in terms of that interaction with the customer, like we've hired in a new role, which we call conversation designer. That particular role is looking all the time at that end-to-end customer experience and tuning, you know, so we, we make some changes around how the, the customer's prompted or make changes around the conversation flow or the handover, you know, cause it's not just the AI bot we hand over to other custom bots as we call them , you know, that can do further investigation or triage. And we're looking at that whole kinda journey and making sure that it is , feels seamless from a customer point of view and where it isn't seamless that we're tuning all the time and it's not a one and done type effort. Like you've gotta constantly tune it because the nature of your work will , will change over time. So that conversation designer role for me is actually quite critical. I think they can have a huge impact on the customer experience through constantly looking and seeing how we can change the , the way the customer engages with the technology. But the AI bot itself, it's a much more conversational field than the traditional chat bots. And I say it tunes itself, like it will ask clarifying questions, it will get to the root cause, which feels very natural for people.
Speaker 1:Yep . I love that and I love that, again, coming back to that customer journey and really thinking out how are you giving the best experience, whether it's thought , whether it's agent, whether it's a conversation, whether it's something that's been preempted from a conversation perspective or someone live answering, it's all coming back to what is it your customer is expecting and wanting, but also what is it the best way to set your customer up for success when it comes to their full longevity of using your product. So Declan, thank you so much . We could keep talking about this. I could keep going and going, but we do have to wrap up and I wanna challenge you with our quickfire discussion questions where I'm gonna try to ask you to answer these next few questions in one sentence or less . I know it's gonna be tough, but are you ready?
Speaker 2:I'll try
Speaker 1:<laugh>. Okay , awesome. The first question I have is, what do you think is next for the customer support or customer service industry?
Speaker 2:Wow , big question. I mean, o obviously the pervasive implementation of AI I think is, is immediate, but I think longer term it's seems like , um, virtual reality has a role to play somewhere down the line, what exactly it is not quite sure it could be on customer onboarding, education, et cetera , but I think virtual reality has a role to play here .
Speaker 1:I'm excited about that too. I think with all the VR happening, it's gonna be exciting to see what's gonna be the next step of interaction with customers. The next question I have is, what is your favorite SaaS product that you cannot live without as a customer support professional? I'm gonna say not Intercom because I know you're probably a little bit biased, but other than Intercom,
Speaker 2:That was, that was gonna be my answer was intercom. <laugh>, yeah . Cause quite, quite literally, you know , it's , it's been , uh, a product I've wanted all my career and uh , you know , uh, I I just love it . So yeah, I'm slightly biased. I would say Intercom if it's not Intercom. So we use a quality assurance tool from a company called Klaus , and I love their kind of whole strategy and approach at the moment. Again, they're looking at how they kind of can automate and scale quality assurance. So I have to say Klaus is something that I'm
Speaker 1:Awesome. Sorry, I know I threw in a spanner there, but great answer <laugh> . Um, and then what is your favorite place or learning resource when it comes to customer support?
Speaker 2:Okay, so I taught long and hard about this. There isn't one I I , I like to kind of chip into as many different avenues as possible, whether that's, you know, you get a lot of good stuff on LinkedIn, you get a lot of good stuff with various podcasts, webinars, et cetera . Uh , you know, there are some industry people that I kinda would look at and see what their messaging is, what they're seeing in terms of the future. So there's whole combination of things and I don't think there's one that I would kinda just say I , I go to that above anything else. Like there are some of the industry commentators or uh , analysts are really producing some good stuff at the moment , like Gartner , bcg , et cetera . So it's really Forrester as well . Like it's really trying to tap into as many sources as possible.
Speaker 1:Yep . And I completely agree, there are so many sources out there, which is plentiful and great for anyone who is looking for information. And my last question to you today is who is inspiring you correctly or whom do you think we should have as our next podcast gap ?
Speaker 2:<laugh>. Interesting. So there is a kind of a kinda support industry expert guy called J Bar . I dunno where you've come across Jay before, but, but, but Jay is quite an inspirational speaker , uh, very large his life character. Uh, like he has some really, really good kinda grounded stories around what good support is. He focuses a lot on speed of response, like he's got some really good perspectives on, on the whole support industry. So , uh, I'd say Jay Bar would be well worked , uh, time on your podcast.
Speaker 1:Awesome. I haven't met Jay before, but I will reach out. Thank you so much Declan, for your time, your expertise, your insights on everything on how support is transforming in today's world. So thank you so much for your time. Really appreciate it.
Speaker 2:Thanks Nick . You really enjoyed the conversation. Thank you.
Speaker 1:Thank you for listening to the Customer Success Channel podcast today. We hope you learn something new to take back to your team and your company. If you found value in our podcast, please make sure to give us a positive review and make sure you subscribe to our channel as we release new podcasts every month. Also, if you have any topics that you would like me to discuss in the future or you would like to be a guest on the podcast, please feel free to reach out. All my contact details are in the show notes. Thanks again for listening and tune in next time for more on customer success.