The Age of AI-Driven Customer Delight

Guneet Singh: Same agent who was going to provide that instant resolution to the customer now is free or is available to now drive more empathy on the high complex or high painful experience. There are tasks which are highly emotional because we don't give our agents enough time to show empathy or to engage in a very emotional way with the customer. So you're creating the time.

Kailey Raymond: Hello and welcome to Good Data Better Marketing. I'm your host Kailey Raymond, and today we're discussing AI driven customer delight. Now that we're firmly planted in the AI era, CX pros are automating more tasks than ever before. But automation doesn't mean replacement. It's still critically important that we find the right balance between automation and human touch. For example, if we can remove some routine tasks from agent workloads, they are now free to engage with customers in more emotional or strategic ways because they now have the time to solve those more complex or painful experiences customers come to us for. On today's episode, I sit down with Guneet Singh of AppFolio to discuss the role of customer experience in an organization, implementing effective feedback loops and balancing AI and human interaction.

Kailey Raymond: Today I'm joined by Guneet Singh, vice President of Customer Experience and Care at AppFolio. Prior to AppFolio, Guneet built CX research operations and business transformation functions to enable scale and meet the hypergrowth needs of companies like DocuSign, GE and ADP. Today he leads AppFolio CX strategy, building and driving sustainable experiences for consumers and businesses by accelerating their value realization. Guneet, welcome to show.

Guneet Singh: Great. Thank you for having me, Kailey. It's great honor to be here.

Kailey Raymond: I am excited to hear from somebody who sits in the center of it all. So, you know, you've led some of these really large transformations at Teams towards, you know, a really valuable CX strategy. Now you're at AppFolio. Can you tell me a little bit about your career journey and kind of how you got to where you are today at the center of all of this? 

Guneet Singh: Absolutely. Happy to share that. My journey in the customer experience actually began very early on with deep passion for understanding what exactly is the need of being there. I started working pretty early on at the frontline of the back office with large insurance providers and reinsurance providers, basically processing claims for customers who had property and casualty losses. So that instilled the feeling of like, gosh, why am I doing this work? You know, what is the need? You know, why people are raising claims? 

Guneet Singh: So understanding the emotional aspect behind that triggered at that time, I knew that this is customer experience. At that time, everything was operational excellence or process improvement, or it was more around how do we streamline our processes. Quality was pretty much at the center of everything in the corporation of CX which we used to do. So that was the early part of kind of my life story in terms of beginning towards customer experience.

Guneet Singh: From there, started progressing towards, you know, understanding what are the operational elements which are necessary in different shapes and size of the organization. Worked with the Bank of America, worked with ADP to transform, and that was a very pivotal movement when I joined ADP and then later on DocuSign, which was what about like, how do I shift focus from focusing on inside out, improving processes, improving baselines, improving capabilities and capacity within the organization to now let's shift and understand what do I really need to do to fix the experience so that I'll, I'm hesitant at using the cliche word, like outside in, but it literally shifted my focus.

Guneet Singh: So I'm like, let me build my capabilities and strengths and also give back to the company from an outside in perspective, that was a very pivotal movement. And then from there on currently really happy that it took that shift. It was a perspective shift from typical inside out operational view versus let's look at customer angle. And over the years I've held various tools, you know, from analytics to research, to customer experience authorization and frontline. That's kind of core of what I do. Current role at AppFolio, I've been, it's a combination of everything that I've done over the last several years itself focuses on how do I now integrate all these new tech solutions that I come again to impact that every customer interaction should be meaningful. You know, how do I make sure that every customer is successful and every interaction [0:05:28.3] ____ with the company itself. So that's kind of where my journey started and where I'm today.

Kailey Raymond: There's a couple of things in there that really struck me. First of all, you used the word emotion, which I think is so spot on to the way that people actually experience these interactions with companies that you're doing business with. And so I love that you're kind of taking that perspective and really making sure that you are centering the customer in the customer experience, which sounds like a no-brainer, but to your point, the kind of operational perspective of how many tickets can somebody actually answer in an hour? You know, some of those kind of goal methodologies that you might, you know, be familiar with may or may not be have the best interest of the customer mind. So making sure that you're kind of bringing the customer back into that. I was really struck by a lot of what you said there. Really cool.

Guneet Singh: That's kind of the key is we do get lost so much in operational practices that we forget end of the other line or end of the ticket is a customer. That could be a B2B, B2C but there's a reason why they aren't connecting with you. There's a reason why they're at your system, in your product, at your help center. At your product support center as well. Because they're stuck or they have a problem to solve for. And are you providing them solution? Are you trying to measure their experience? Or are you trying to measure your productivity? There's no right and wrong but it's the perspective.

Kailey Raymond: Really smart and training that perspective. And as somebody who's I'm sure seen thousands if not millions of support tickets in your day I'm sure you've seen a lot of the frustrations or the use cases the excitement from customers. I'm also sure you've seen a lot of these trends of what's going on related to customer experience having been in the profession for quite a while now. So what are those big trends as it relates to customer experience that you're watching out for right now? 

Guneet Singh: A couple of things I'm really very curious and really watchful about is, one is not necessarily a new one which has been there in the industry from as long as I can remember is Omni-channel. And I wanna be very careful about the use of the word Omni because over the last 10 years I've seen it's been commercialized a lot where it's oh we're going to give you Omni-channel capabilities, Omni-channel experience. I'm just using it in the perspective that customers now expect a very seamless, very effortless interaction across your various channels, whether it is self-service, with old-school bots versus new AGI-focused bots versus your custom bots maybe it's your social media maybe it's your chat response channel maybe WhatsApp maybe your messenger, support channels or AppFolio. So that's something that's very crucial that it is especially the more tech is coming into these channels the expectations are now moving fast.

Guneet Singh: Gone are the days when you were thinking you know what we'll give an SLA of X on a phone channel versus email versus chat. That was a basically just basic layer. Now it's like they expect the same level of effortless interactions across even if they connect with you on the bot versus phone they want that to be a wow experience. If you're not able to deliver it, that's the secret sauce. And then another piece which I'm noticing is customer sentiment. Customer sentiment has regained popularity or the analysis pertaining to that has regained popularity because again it's all about understanding and anticipating those customer needs.

Guneet Singh: It got a lot of wind 10 years ago or 11 years ago when Target came into the whole ad we know before you know, whether you're expecting something or not the whole advent of predictive analytics huge commercialization of natural language processing capabilities. And now fast forward to 2022 and 2024 AGI [0:09:23.8] ____ has brought emphasis on, how do you actually look at the data? Because there is an increased gap in actually using these tech advancement to make any meaningful change. There's a lot of promises, a lot of real utilization also that has taken place.

Guneet Singh: But at the same time it has also increased a lot of noise which people like middle manager, a manager of a midsize company is struggling to actually use it in a meaningful way. Unless they open up the big checkbook and start bringing in huge consulting companies or they sign up for a huge platform. And then they go through the pain of six, seven, eight months of implementation. And then to end like, oh gosh, you need another two people to run this things versus me sitting at my table doing kind of basic coding and saying let me understand the real sentiments. Or me just kind of picking the phone and talking to five customers and manually classifying and coding what is the sentiment for these customers? So that trend is coming back where there is a real gap in understanding what are the customer needs? What is the customer sentiment? Because it is getting disillusioned with this whole tech needs and we can do analysis and we can tell you macro trends, micro trends but they hide the real needs behind that. So that's what I'm seeing is coming back to the surface.

Kailey Raymond: That's so interesting. I haven't heard that one recently and I want to dig in a little bit deeper there. So sentiment related to social listening related to the customer data that's coming in. Tell me a little bit more about some of the ways in which you might be able to start measuring that and some of the applications off of the back of that. What would you be looking for as a CX professional related to these sentiment trends that you are now listening for? 

Guneet Singh: So one of the big piece is customers are increasingly asking and valuing speed again and again. Speed of getting back to us. Speed of connecting. They have an issue, they want it solved now. They want it at their fingerprints, which has been the need over the last... But again it gets sandwiched across different platform use or you have to first get on board and use it versus or maybe you don't know the right way of putting a question across versus now we have AI bots. But bottom line is they're increasingly asking for speed and personalization. And I want to be very careful about the use of the word personalization because there's a whole industry on personalization there.

Guneet Singh: Simple speak, they're expecting quick resolution and they're expecting the experience which is specific to their unique needs. What I have seen and what I've been doing is understanding what is a customer? Why they're here? What they're trying to accomplish? What is the outcome? What is a transaction? What is that one activity they're trying to complete which they struggled and they stop and they lift their finger to now connect with support.

Guneet Singh: And when they said connect with support, are we measuring that? Are we understanding what is that gap in your service experience or in your product experience which forced them to pick up the phone or hit the send button or engage with your AI bot to connect with you? Are you capturing that? Are you understanding that? And if you're understanding it, is this overlaying on your roadmap? Is this something because it's a lack of education or is this because you truly have a gap in your product and are you connecting it back to your R&D organization? Are you connecting it back to your pricing or your service strategy function or are you actually doing something about it? Or you just gonna say, you know, yeah I expect there're gonna be 10 disgruntled customers who will always not, but again, that's where the spaces are where you see a lot of startup coming in and start segmenting into market for that.

Guneet Singh: So for me it is like, let me put actions on those inputs on those early warning signals that can help you avoid the need of customer using to support itself. That's something is really, which I value and I always tell my team, let's go into... Why even they're connecting with support? What is stopping them? You know, let's understand they were trying to pay a payment or they were trying to kind of complete a transaction, they stop because they didn't knew how to do it or they got stuck or they got error. So do we have a clear understanding and visibility into what all those reasons are? If not, then let's not go forward. Let's take a step back and understand what are those. That's the starting point.

Kailey Raymond: It's interesting, you're really talking about this like feedback loop that you're creating and making sure that, you know, whenever somebody is kind of coming into any of your channels, you're creating that listening loop that will then be able to, you know, send them in a a specific direction and then kind of calculate whether or not that's something that needs to be built directly within the product or it's something that a human being or an AI bot or whoever might be able to kind of take care of. That's a really kind of, you know, interesting thing that I do think being able to automate and scale in a way that makes sure that you're delivering that feedback back to the right people in a timely manner is such an important way to be able to drive ROI for your business and it's something that's often missed. So tell me about some of the ways that you might be implementing those feedback loops throughout your career. Like what have you seen be successful? 

Guneet Singh: So what I've seen being successful is just defining what the problem is without jumping on to a fancy system or a fancy software. So for example, one of the biggest need I've seen over the last five to six years, I wanna keep it general. One of the biggest need was that customers were interacting, but we didn't have a capability to aggregate all the touch points into a single system. Now you can say gimme, that's gonna take like a whole level of body of work. You can establish data pipelines between different systems, then you're gonna have a new platform which aggregate. But that's the basic layer work we have to do as a company. You know, so bring in all the data points, all the data sources, whether it's direct data sources, that's what I did. I've brought all my direct sources, indirect data sources into one single system.

Guneet Singh: When I say let me double click direct sources, anything that has to do with you going to the customers asking feedback, you're looking at their telemetry, they're doing likes, dislike, clicks, touch clicks, using different telemetry systems. You know, whether it's X different, without taking names, you know, one or two or three different platforms. Other direct sources are they're giving you complaints, they calling up, they're giving feedback, escalations, president office, you know all those direct feedback sources. Indirect feedback sources is typical like your case interactions, your tickets, your chat engagements, your phone calls, that indirect data. Let's bring everything into one system and then say what are we trying to get? What are the top three customer issues? Why customers are calling us? What are the top three issues why customers are not using product A, product B, product C. Let's start generating insights using that aggregated view.

Guneet Singh: So that's something in which I've been very bullish to kind of start using it in database. Now, that can require a whole dedicated team or you can choose to say, you know what? I'm gonna bring a software, I'm gonna aggregate everything else and I'm not gonna enable my product organization. I'm gonna enable my frontline management to dig and curate insights as they go based on the use case in their day-to-day operation. Because what you cannot do is you cannot just keep creating insights and start shipping or pushing it to different parts of the organization. It may click, it may not click, but the adaption is when you're trying to say what is the problem? 

Guneet Singh: A team leader who is managing a team of 13 reps on the floor, they struggle is why my agent A is taking more time or is struggling with the satisfaction of a particular case type or a particular customer type, you know, versus my agent B. So you have to kinda empower them to dig the insights. And so, and they can only do it if they have visibility to 360 degree of the customer interactions. You know, so that's kind of, you know, what I have done over the last several years is, you know, in different shapes and form, bring that data together because that is the base guarantee in which you can build multiple things.

Kailey Raymond: It's really interesting too, what you're talking about is something really powerful, which is making sure, of course you have that clean data to be able to actually get a real view of what's happening, you know, within customers. But you're also talking about this ability for different parts of the organization to self-serve. And for you all to kind of be that center of excellence that's really driving a lot of the use cases and the ways to actually make sure that folks know how to use the data that's in front of them. That's no small feat. And I'm wondering like the work that you're doing, we've talked about even a few different use cases today that touch upon products, that touch upon customer service and lots of different parts of the organization are involved here. If you have a succinct way to share with us, what do you think if you would be able to share what the role of customer experience is in the organization 'cause it does seem like it touches pretty much everything.

Guneet Singh: It does. Number one thing is customer experience. I can say, give you a very critical answer, which is not against like, it's a backbone of any successful organization. That's a very loaded thing. Everybody understands that. But what does that mean, when you have a customer base, are you leveraging AI or tools to actually solve for the real customer issues? Not only when you're building products, but when they're experiencing your product, when they're experiencing your processes, your services, that's where, I'm solving for. For example, there's a big use case where I have brought in a lot of self-service focus in our customer interaction, not blindly. What I'm gonna saying not blindly, understanding what are the things that really don't need a human interaction, not because I feel it. Let's go to the customers. I understand these are the six things, seven things, eight things or 20,000 things that customers doesn't need a human agent, doesn't need a human voice to interact.

Guneet Singh: Let's classify that. Let's kind of understand, can we train AI to provide that personalized and better customer interaction. So that's one use case which we are investing heavily and I've invested heavily with the last several users. Like how do I enable self-service for high repetitive, mundane task, which does not need a high value human interaction. The reason is not because of the cost, the reason is because customer don't want to wait as I go, I'm gonna go back to what customers are really valuing is speed. How instead of having them wait, can we give them in nanosecond answer they're looking for? If customer is stuck, like I don't know how to reverse a payment I've already done on our financial system, I don't know what to do now.

Guneet Singh: And instead of waiting to hear back from an agent or engage with somebody in the chat system should be intelligent enough to give them that answer back. And that's what we have invested you know, over the last, you know, especially as a professional, that's where I've invested in the last 10 years or so. Like let me build that capability. How do I leverage now with last two years or so with the whole gen AI coming in, it's helping us train better, it's helping us kind of understand what are the right topics in which you know, and what are the right answers. Instead of creating an answer, let become the system be intelligent enough to answer that and engage. Once you do that, is customer happy with that? So I'm measuring like, great, does it really help you solve, how is the effort involved in getting that answer? Because that is the key. If customers say, you know, it helped, but, hmm, it could not understand.

Guneet Singh: Those are the checkpoints that I'm bringing in by validating, I wanna have human validating. Is the AI actually giving the real answers meaningful and are they really helpful for customers or not? So that's one use case. The other use case, which I'm saying from a customer experiences, agents are at the center of delivering their customer experience. How are you making life easy for them? If you go to any B2B organization, there are like six, seven, eight systems that agents have to kind of navigate when they're trying to engage with customers. Now, unless you are a rockstar, you are a universal soldier and you have absorbed everything, which is a very rare feat, you still rely on those system or old school you used to raise your hand and your manager used to come to say, yeah, how can help? I have a customer on call and they're asking for this thing, I don't know how to help? 

Guneet Singh: So that is a use case that I've been solving and I will continue to solve and invest more on that. How do I give knowledge to that agent in that time of need at that moment of truth for the customer. So what I've done is like, you know, we've built over the last several years different agent facing, I don't wanna use the words, but again, agent facing bots or workflows which are helping them level the playing field for knowledge. Garner the needs your level one tiered agent, level two tiered agents, not because knowledge is at your fingertip. Now you know how to ask the right question to the system and it'll give you the right response. And also while they do that, can we now validate the same interaction? 

Guneet Singh: This is useful because sometimes agents are like, you know, I don't know if system is giving me junk. This is not useful. So give that feedback right in that moment to the system. So we need to have that capability and that's what, you know, I've done is sort of build that capability, build that potential for system to improve, right at that time. So that's something, two use cases which you, from a standpoint, I'm very bullish and I'm gonna continue to be bullish in the next, I'll say 24 months or so.

Kailey Raymond: I mean, and both of these are like primetime AI use cases, right? Like making sure that you're building efficiency in the right places into your workflows and using the power of these models, not only to service your customers, but to help your agents as well, get the right information at their fingertips by, I don't know, maybe having your own little LLM that you've fed all of the information about any of the companies that you work at so that they can then, yeah, take that product. Very, very smart. And something that we've seen work really well here at Twilio too is making sure that we are, enabling teams with our own little LLM that they can then ask questions of or write sales sequences for or kind of whatever the use case, if you're feeding that bot, all of the information that's just tailored and specific to your one company. I'm wondering like in a perfect world, what's the, like the blend that you might suggest of AI and agent experience? Is there a place like where should you and should you not automate? 

Guneet Singh: Yeah. Great. So real question, real answer is if you have not done your homework, then don't start with AI. When I say done your homework, which means have you classified your task? Can you look at your team of 100, 200,000 agents or your 10,000 customers or 1 million customers? What are the different tasks? What are the different engagements they're having with my product and services? Have you classified, do you know how to classify? And then if you have not done that homework, don't even try to think like AI will come and help you out. You need to first understand what are those tasks, what are the tasks I can really pick? 

Guneet Singh: And at low risk, if you wanna start with it, at low risk, and you can try and automate that. And I wanna put a disclaimer, automate is a big word. How can you actually make it more efficient for those routine tasks or those mundane work items that are in your ecosystem that exist, or common queries? And the word which I wanna amplify here is instant, because you can say, you know what, I'm gonna look at those out of 10,000, only 200 of those are a really low level risk, but they're high volume.

Guneet Singh: I'm gonna try and eliminate, or I'm gonna try and automate those tasks. In a perfect world, your product should be working to eliminate the need of those engagements in the product itself. But we know product is chasing the top line, and they may not be able to spend that amount of effort on reducing your bottom line related work. So in that, can you automate those? When you say automate, can you now provide instant resolution? Instant response to those customers for those tasks. That is where the biggest kind of inner blend is. And then at the same time, balance now, same agent who was going to provide that instant resolution to the customer, now is free or is available to now drive more empathy on the high complex or high painful experience.

Guneet Singh: You talked about emotion in the early part of our conversation. There are transactions, there are tasks which are highly emotional, because we don't give our agents enough time to show empathy or to engage in a very emotional way with the customer. So you're creating the time for them. So that's kind of the balance you have to figure out. But again, based on the whole world, which means do you know your ecosystem? What are the tasks which customers really don't care about human versus AI? But you're not gonna have an answer to that from day one. You got to test it out, interview, do research, do kind of focus groups, have one-on-ones with the customers to understand what they really care or don't care about, and then kind of build that. So it's, again, a hypothesis-driven approach you have to take to classify that.

Kailey Raymond: And the way you're coming back to, I think, of this cataloging of information and making sure that it exists in a way that is easy to analyze, that you can see the trends, that you can make these decisions about this high volume, low risk scenarios that you're kind of talking about. It's the boring, perhaps, work at first or however you wanna classify it that actually gets you to the more interesting AI use cases very often that we talk about here. And it's like, yeah, sometimes the process is the point. You have to go through that to be able to get the outcomes that you want.

Guneet Singh: Fortunately or unfortunately, process, the P word, has become... Has such a negative connotation if you look at the high-tech industry. It's agnostic of a function. It's such a bad word. People are so afraid of using that. But that holds the key, that if you don't have a process, if you don't have investments early on in the process, it's gonna come back and haunt you. You can use a fancy new word called frameworks. Everybody talks about, what's your framework now? Great, let's do that.

Kailey Raymond: That's so right. You can reframe something that's a really old concept into this new word. Don't worry, 10 years from now, the word process will come right back and that'll be the trendy new thing that people are excited about. It comes in cycles. You're sitting here and you're talking about two of these use cases that you're excited about in the next 24 months or so. I'm wondering if there's a lens that you can go one layer of altitude higher and you can really look at five, 10 years down the line, what does the future of AI in customer experience look like to you? What are you interested in, excited about, curious about? 

Guneet Singh: Yeah. I'm really interested in two years, three years from now, where AI is helping actually not only engage with the customers, but actually do work for them. I know there are a lot of investment, a lot of use of LLMs and Genius. How do we actually use it to take action on behalf of a customer? But our customers are not there yet. With the whole investment and the education and the journey that customers are taking with us, agnostic of industry, that AI will help us actually not eliminate the need of support, but also do tasks on behalf of the customers, which are the main reasons why customers are connecting with you. The opposite way of looking at this is as long as humans are using your product, there will be a need of human support. Now, using LLMs, can you actually empower customers to have LLMs do their jobs, do their tasks? And if that happens, then they don't need support.

Guneet Singh: Then only thing is need is you need infrastructure internally within your company to engage with those LLMs that are coming from the customer side. That's where my future aspirations are. As I look at industry, especially from a service and experience standpoint, how do you actually make your customers more efficient, use AI in their day-to-day routine tasks so that they're not dependent on you as a service provider or as a support organization? And if they need, then yes, your human agents are on standby to help you do that job. That's a little bit simple, but futuristic aspiration that I have that we reached that stage where truly your bots are engaging with your bots on your side of the house.

Kailey Raymond: Bots on bots on bots is really, that's where I get a little bit shaky and a little bit like, we'll see. We need to make sure that we're putting humans in between those tasks first to really trust the outcome there. But yes, I do think you're right is eventually there will be, especially for some of these tasks that, you know. To your point, human beings having a more empathetic or emotional or higher order task, assigning them the work that is more strategic and having bots do the things that we don't need human support.

Guneet Singh: Obviously, we don't want to rely solely on AI for interactions also that require the emotional intelligence or empathy. We will reach that stage, but I don't know how, there's a lot of... I'll give you an example, in the last couple of you know, my professional experience or the work experience, customers either were generation one, two or three. Now that generation is growing, that they're maturing. So their way of engaging with us is changing also. Now that's where you start saying, what are the do's and don'ts with respect to your AI. Do you wanna really kind of look at repetitive tasks and do the data analysis and understand where you leverage AI. Or don't solely rely on AI for interaction that do require that interaction, that personal connection, that emotional aspect that really you want your agents to be kind of solving for your customers.

Guneet Singh: AI can definitely support your agents with all the data, all the insights, real time, enable them to be more personalized, blah, blah, blah. But that synergy has to be established and you wanna avoid that over hyper-personalization aspect of it. Because then you're gonna lose that human touch. And that's what the key is, the how do you balance that automation with the human touch. That is the key. It's like, going back to what I'm saying, identifying tasks, jobs, jobs to be done, which are best suited for AI, jobs which are best suited for human intervention. Regularly, you have to go back to the drawing board and saying, maybe yesterday there was three jobs which were best suited for AI. Now because the complexity has increased of our product or customers has really mature, now we need to kind of relook, and maybe it's now humans are needed for that.

Guneet Singh: So that regular review and adjustment has to be done to your AI LLMs, to your applications. And that's where human intervention is highly needed. You have to manage those fine tuning, which I call it internally in my organization, as validation. You have to interject human connection into those. Right now it is at the early stage of just measuring, gauging, understanding the efficacy and efficiency of it. But eventually over the next two to three years, it'll be more about, let's make sure that we are revisiting our basic hypothesis that these are the three things that are best suited for us versus AI. So that's something you have to be very, very cautious about it.

Kailey Raymond: This idea of automations cannot be something that you set and forget that it's not like that's not really the point here. Like, we wanna make sure we're reducing friction for the customers. And that is something that you're gonna continuously need to revisit and tweak and say, actually, does this still make sense based off of the business context today, I think is perhaps something that can be lost in translation. Because I don't know. The fear that I have is that people, they try to automate every single part of their customer journey and then they kind of lose that empathetic, emotional kind of part, and then they're making decisions that no longer make sense based off of the continuously changing business context that is different seemingly every month, every year. And so making sure that you can adapt and have humans make those decisions, I do think is a really nice call out that you're saying.

Guneet Singh: I want to go back, if I may add to that, is I want to go back to it. It's the fundamental layer, which is do you really understand your customer/ And that is not a historical question. That is a question that should be in front of your screen every morning when you wake up. Are you understanding your customer, because your understanding will change every second. So how do you make sure that you have a continuous understanding of your customer base, their needs, and their habits and their patterns? Because you got to keep phasing for automation, as you're saying, but without understanding what are you solving for? 

Guneet Singh: Because right now I'm saying speed, as I mentioned to you, is the key trend I'm seeing. Customers are really hungry for speed and personalization. Tomorrow, it may not be. Tomorrow, customers say, you know what, I'm already seeing, customers are saying bring the phone back into the service organizations because they wanna talk to humans. And again, one can say, industry can say, yeah, you're not solving for the real issue, you're solving for the effect, because they're not able to get their issue resolved through chats, and that's why they're asking for phone, fair enough. But you have to find a medium through which you connect those needs. So that's kind of the key is going back to understanding.

Kailey Raymond: I'm one of those old school people that is saying, bring the phones back. I wanna talk to a human. [laughter] I'm on that side of it because I do feel like a lot of the time the bots are doing a great job and they are giving me what I need, especially if it's this kind of like not super important task. A lot of what you're talking about here is not easy to accomplish. You've talked about bringing data together from disparate sources to unite to really have that 360 degree view. You've talked about feeding that back to multiple parts of the organization to be able to make decisions around product roadmap and around reducing friction in customer experiences. Tell me about your experience in this industry and where some of the big challenges arise.

Guneet Singh: Simple biggest challenge is simplifying that customer journey without compromising the quality and the experience piece of it. Every company will say, our customers expect effortless experience. Great. Our goal is to remove friction points. Great. But what are those friction points? What is the customer journey that you're forcing your customers to take? Are you balancing digital and human touch points in that journey? Are you actually designing that experience? 

Guneet Singh: Are you actually putting emphasis on those emotional touch points and creating that particular experience that you want customers to take? Now, one can easily deduce, when you say simplify journey, maybe let's remove the friction points, the most bottlenecks and things like that. But the alternate view is, maybe we'll take a step back, let's recreate that journey. And I'm not talking about journey map investment, like you start doing for three months, four months, five months.

Guneet Singh: Break your whole journey into micro segments, micro journeys and say, how do I relook at the journey? Why does customer actually need to connect with the product? Or what are they trying to accomplish? Going back to how do you continuously improve that journey using technology. Understand, anticipate, what are those needs that customers have? Leveraging the data you have over the last several years or several months and say, let's try to simplify their journey. Let's eliminate the need of support. Let's eliminate the need of using that product in that moment.

Guneet Singh: How do we actually get ahead of that? Rather than saying, oh, you know what, support is not an issue. Experience is not an issue. When a customer is trying to accomplish an outcome, let me just, but are you taking a seat back and say, relook at that experience? Why forcing customers to first do this, this and this, and then get to that outcome? And why even customer need that outcome? Let me just try to understand and let me cut all that processes in between, then have customers jump straight to the outcome that they're trying to accomplish.

Guneet Singh: So that's kind of the biggest challenge is that simplification of customer journey is gonna become more and more tedious and difficult with so many systems, so much AI, so much tech coming in. So you got to understand where is that micro segment, which is actually the key for my customer experience.

Kailey Raymond: I think this concept of assessing your why, like really, really asking yourself, does that make sense for the customer? And sometimes you have these processes or programs or things that have been in place for a really long time. And yeah, maybe they are kind of working, but maybe there is a different way. And so I'm constantly reevaluating that. This is like a theme that I think is really coming up here is this evaluation of what are we doing? Does this still make sense? Because the needs of customers are changing always, and you need to make sure you're listening to that.

Kailey Raymond: So how are you adapting to them versus making them fit into the form of your programs? I think that that's something that a lot of folks can kind of get caught up on is this is the thing that we've always done. And so this is what we do every quarter, and maybe you don't, maybe you don't need to do that. There are no sacred cows.

Kailey Raymond: We've talked about some of the ways that you catalog your data to really make some of the decisions around the programs that you wanna run or the areas that you wanna focus on. I'm wondering if you have a definition around what good data is.

Guneet Singh: For me, simplest definition is first level, good data is something that is actionable. If it is not actionable, it is not good for me. Then you start bringing in layers of you have to kind of make sure that yes, it's actionable, but is it established on the pillars of safe? Is it respecting the customer privacy? Is it respecting the customer consent? And then with needless to say, is it even accurate and relevant? Because if it's not accurate and relevant, it's not gonna be actionable for you. It may be actionable for you, but again, if it's not sourced in the right way, so it doesn't respect the privacy and consent, then it's gonna have a lot of downstream repercussions for you.

Guneet Singh: So short answer is like a good data for me is anything that is really insightful, I can take action on it, but it is accurate and relevant at the same time because you wanna kind of drive your day-to-day decision making as well as your strategic decisions that you wanna do. Just like looking at the five case types, let's take an example.

Guneet Singh: I'm looking at what are the five biggest support drivers? Good in that is if somebody comes in, this is the data of all the calls that I've taken, all the chats that I've taken, but I cannot make sense of what are the five categories or five themes which I need to act upon or double-flake into the need of support there. That is useless for me. It's not a good data. It's good, but I can't make any sense out of it.

Kailey Raymond: You have to start with the use case in mind. You have to know where you wanna head and if you're going to start collecting this data. I like that. The action I do as a marketer, I feel that way too. Where I was like I need to be able to do something with this data. I'm wondering if there's anything surprising that you've learned about your customers based on the data that you're collecting.

Guneet Singh: I'll take an example. Over the last several years, there was a hypothesis that customers really, really want support at their fingertips. That means they want immediate actions, which is contrary to what I'm saying. Customers are like really hungry for instant resolution, speed, and everything else. As we started digging into a couple of use cases in the last, you know, I'll say five years, not specific to this company also, we started realizing that when we started talking with the customers, that is just an output or a symptom.

Guneet Singh: Customers were, okay, you know what, I'm fine. You can take 24 hours. You can take 48 hours. I don't need it now, but I want you to give me an accurate answer. I want you to avoid for me to come back to you again after two days or three days or next month for the same question. So I'd rather you take time instead of just giving me that instant answer. So that was very surprising once we started digging into the data. And again, data here I'm talking about is the Quant data was like customers are disgruntled. They are kind of taking a long time and they're talking too much or they're taking too much time on the chats and everything else. When we started digging and doing some more discovery, say, what was the qualitative aspect? We started combining that. That was an insight that was like, no, great.

Guneet Singh: Not one solution fits all. Customers were saying, you know, there are things which they're okay to push back and say, you know, we don't need instant resolution, you can take your time. So that was something really insightful, you know, which I found in shaping the strategy in terms of, you know, what service offerings I bring to the market. What is the service structure I bring to the market? What are the service models that we need to invest on it? And how do I balance investing in AI, in instant resolution versus, you know, balancing and skills, soft skills, you know, for my Frontline as well.

Kailey Raymond: That makes a lot of sense to me. There are definitely things when I'm talking to vendors that I would ask them that I would need to be done today just because maybe it's time-bound, it's urgent, you know, whatever the need. But a lot of the time, maybe even most of the time, I'm willing to wait a bit, a few days, whatever, for them to get me the right information so that I can make a decision based off of it. Most things, I would argue, are not, like, incredibly urgent if you really think about it. Some things are, of course. So I hear you on this, like, you know, this need for consumers and their desire for speed. And I think that's a symptom of just the Amazon effect of it all and kind of, like, the world that we're now living in where everything seems to be on demand. And if you push order on your seamless app and it's not there in 10 minutes, you're getting frustrated. It's, like, these unrealistic expectations that I think we've been set for the past 5, 10 years. But that's not necessary always.

Kailey Raymond: So that's an interesting insight. If you're, like, drilling down, you're right. Probably not everything needs to be this instantaneous gratification that people have really been accustomed to nowadays. Last question for you today, Guneet.

Guneet Singh: Sure.

Kailey Raymond: If you have any steps or recommendations that you might recommend to somebody that's looking to uplevel their customer experience strategies, what would they be? 

Guneet Singh: If you have $100, spend $99.9 on deepening your understanding toward your customer, whether it's your direct data sources or indirect resources, take time to understand your customer. And it's not a cliche. I understand my customers, really, as a leader, as a frontline, as a middle manager. When was the last time you spoke to your actual customer? When you actually had to sit down not to defend, not to deescalate, but just to understand what exactly they're trying to accomplish. So deep your customer understanding through data and feedback.

Guneet Singh: So that's something I'll say. If you're an uplevel, your CX strategy, start from that. Then, yes, you can look forward and say, great, everybody's running after AI, investing in technology, but for what? What is the use case that you're trying to solve? Are you enhancing personalization? Are you enhancing speed? Are you enhancing time to value? That's kind of, you know, the level... There's the thought coming in, but the bottom line is deepen your customer understanding.

Kailey Raymond: I love that. If you're gonna spend the majority of that dollar spent it on understanding your customer, that's great. Guneet, thank you so much for being here. This has been a great conversation.

Guneet Singh: Absolutely. It was a pleasure. Hopefully, meaningful for your listeners. But at the same time, I do find these opportunities really insightful. It gives me a deflection point. You're kind of free to go back and listen. Am I actually living what I'm talking about? Or let me take a quick audit check on that one. So thank you for having me. I appreciate that.