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Unleash AI's power in your Support Team! Ascendo AI's Design Workshop equips you to integrate AI, optimize operations & elevate customer experience. Register now & empower your team for success! Build a Winning Proactive Support Strategy On Demand - Every Two Weeks Empower your support team with Ascendo AI Service Co-pilot's AI design workshop! Seamlessly integrate AI, optimize operations, and enhance customer experiences. Thank you for leading our global support leadership at Veritas through a visioning workshop on the real uses of AI in our organization. Ascendo came up with thought leadership that was unbiased, cutting-edge, and realistic. The insight and material shared have continued to elevate conversations that we will incorporate into our strategic plans. Bill Gargiulo, Senior Director Global Customer Success Hear What Our Attendees are Saying Why This Workshop? We are offering to lead a design workshop to empower your organization with the skills to create a modern support solution which includes the use of AI tools. This workshop offers actionable insights to seamlessly integrate AI, optimize operations, and enhance customer experiences from day one and can generally be completed in one hour. Discussion Discuss trends in support, understand core concepts of AI and automation Grasp AI's Role Learn how AI can elevate your support operations, build a case for funding, design the right architecture Tailored Strategy Customize AI solutions to align with your support model and service level commitments to come up with a high-level roadmap Immediate ROI How to implement AI tools for instant, measurable results. Deliver value to all stakeholders. Our Commitment Experience a transformative workshop with Ascendo AI Service Co-pilot, tailored to maximize benefits for your support team. Delve deeper into specific areas of interest after the workshop, ensuring flexibility that aligns with your team's busy schedule. Our mission is to empower your organization with the essential insights required to fuel innovation within your support structure and unleash the full potential of AI technologies. Elevate your support team to new heights with Ascendo AI Service Co-pilot. Industry AI Experts Our workshop brings together a team of experienced AI experts who have a proven track record of success. They will share their insights and knowledge to help you understand how AI can be applied to your specific customer support scenario. In the past, we have had people from OpenAI, Google, Amazon, Microsoft, SAP, Sun, Oracle, Adobe and many others. In addition to learning from the experts, you will also benefit from the collective wisdom of all the attendees. The workshop will provide a forum for discussion and collaboration, where you can learn from each other's experiences and challenges. Expert Speakers from

Explore how knowledge intelligence is shaping the future of service and support, enabling smarter, faster, and more proactive customer solutions. The Future of Service Support Unveiling the Power of Knowledge Intelligence | Transcription Hey, Brett, it's a pleasure to have a discussion with you on Knowledge Intelligence. Thank you so much for coming. Thank you for having me. It's exciting with what's going on. We really appreciate the time. Yeah. Brett, just looking at your background and your experience, you've actually been serving for very large companies like HPEDES, Dell, Xerox, and Avaya, and now with Veritas. You've led very large service teams and improved service operations quite a bit. So it's wonderful to hear your insight in this session of the experience dialogue. So thank you. I want to start off with having you know level setting about knowledge, right. So knowledge is an integral piece of any services support organization. But lot of times teams go ahead and have a couple of knowledge management, people building the content and then agents and, and customers and experts and product groups, everybody contribute to it. That's kind of how a lot of companies think of knowledge. How do you think of knowledge and think about knowledge intelligence? A lot of the is is all around it's, it's not only the uniqueness of the knowledge, but it's also how do you, how do you unlock it? How do you reuse it, especially in the services services industry, which is where I spent most of my career now in support. Very, very much you find similarities between supporting a a a product and servicing a a customer. Very, very similar in the approach knowledge unlock who, who has it, who has the answer to a particular challenge that you're working on at a point in time you, you find a unlocking it is, is locating it, where's it at? And as we went through REI proof of concept this past summer and now the, the full implementation of it, what we ended up finding as we inventoried our, our knowledge, intellectual knowledge, we had two 290,000 items of knowledge. And then when we started to unlock the organizational silos in a support organization again, we found more content of which much of it was rich. So we started really focusing on how, how do you collect all of this? How do you make it available instantaneously and helping resolve support issues? And then how do you create live articles from closing out cases? So when you do 180 or 200,000 cases a year throughout the course of time supporting customers, there's the creation of all sorts of knowledge. So how do you unlock that? Make it available because it's relevant at a particular point in time, not only for one customer. We found it being relevant for multiple customers. So how do you create that content? How do you get it through the process of publication and available internal? And then what do you use as a standard to finally decide you're ready to make it externally available for your customers to access? And so you, you find yourself moving from a very slow pace of methodically moving to a really high pace and recognizing that if you can unlock that content at a relevant point in time, its relevancy is across multiple cases. So you get a high reuse factor out of it. Yeah, I, I, you know, we've had very good conversations around it. And I can see the three buckets that you know you and I have discussed on. The first bucket is how do you summarize all of the cases and all of the knowledge so it can be reused by everybody, which is the one that you just talked about. The second case is how do you start creating new pieces of knowledge from everyday interactions, agents with agents, agents with product teams, agents with customers, etcetera. And then the third bucket is how do you make this proactive so you can start looking at the list of customer interactions that are coming in in real time and then creating knowledge to map to the interaction. So completely take it to proactive approach. So those are the three buckets in which you and I had discussed earlier. It would be great to get your opinion about how do you bring in, you know, a level of quality and accuracy, search ability and all of that within these three buckets. Well, I, I think like anyone would get and, and most companies all have it today. They have a, a knowledge management team. They have some sort of hierarchy set up for the purposes of, of creating and reviewing and then publishing. Veritas is no different. We have the same in the support organization. The only difference now what used to be days of creating articles and going through that process, we're doing it now in in the matter of hours. So as cases are closed, it's flushing through AI. It's looking at the relevance of content and relationship to what we already have and deciding, OK, this is a unique article and it's predicated on a standard that we've established. Create the article and publish it for the KM team to review. So we, we can now take the, the timing of the closing of a case from the time it's closed and 40 minutes later we can have a full article published internal for the TSE engineering community to use in servicing customers and, and support requests. And the reviewers are very focused now on the quality aspects of the article. In fact, often times when it's rejected, it's because it didn't meet a, a standard that we have. And you know, it goes through the rejection process is either a full rejection and it's, it's no longer a useful content, or it's a rejection for rework, which then requires the, the manual aspects of, of cleaning up the, the part that needs to be resolved. The beauty behind it though, is that we're, we're maintaining a 60 to 70% content rich creation. So that means we're only working on 20 or 30% of the article for the quality aspects of what we want to refine to meet our standard before we publish it. And then it has to be referenced so many times internally by our TSE community before which time we then publish it externally for our customers to access and our knowledge base. So we, we've established a standard we're working toward that. We, we have always had one, but we had to establish some standards around the AI aspects of it. And you know, so far it's, it's, we have an internal what's called a business value index that we rate the articles and the AI content is, is scoring high in that business value index every day. So we, we know we're, we're creating relevant content. It's timely. It's not 100% creation like it used to be, as 70% of it's created from the closed case. And we're finding high reuse, meaning that we're, we're finding that the content is used in more than a one or two KS. It's because of its relevancy. It's used multiple times throughout the course of the week. Yeah, go ahead, please. No, no, go ahead. It's, it's, you know, so tell us a little bit more about the business value index and how it is calculated. Sure. The business value index is, is predicated on the number of times it's used internally referenced and, or how many times it's referenced externally from, from a customer. It also can be a part of the index. Is is is also based on a rating that the customer or the TSE provides A sentiment rating and it is also calculated based on the number of times that AI uses it to produce an an answer and and Veritas support. We took a slightly different approach in that we created a hybrid approach where we have we used working with Elastic Search as our search engine. If you were looking at your screen on the left hand side, one through 10, most relevant to least relevant, we rate the articles predicated on the prompt question on the left hand side. On the right hand side, we have a bot that actually produces ALLM response from the content Veritas content and everything we've deployed is is predicated on our content. So while we're using a three 3.5 turbo and in the ChatGPT engine, all of the content that we do the search with and the creation of the LLM response is coming from Bertha certified content. So that way we know we've got a high degree of accuracy. We have had some hallucination because of, of AI given us an answer. You ask and it's gonna give you an answer. It's you and you want high degree of accuracy. Obviously because we're working with customer data, we want our product to, we don't wanna delete something. We wanna make sure the integrity of the data is always intact. So the quality of the the content is an important aspect of what of what we do. And the BDI index is just a real time view of how is the the articles performing not only internally but externally. And it's weighted like any index. It's weighted business value index. Yeah, so, and it's interesting, you know, because we were talking about knowledge from the old cases, knowledge from the interactions and then identifying what pieces of knowledge, the three buckets of taking it all to proactiveness, right. But what I'm curious is at what point is it useful for training? And I have a subsequent one for product 2, but let's talk about training a little bit. At what point is that knowledge used to train new support teams or new employees or new products or product changes etcetera? Yeah, what we, we tested that too. And in fact some of the content that we have indexed is in fact our training material. It's also included in the in those documents in which it's, it's searching for answers with we took what we have we we call our our level 1 support the the care team. Level one obviously would be the the entry level of of knowledge of, of the technical knowledge of the product and what we found in using the content and helping educate the the use of a prompt how to problem solve iteratively as you go through your question you asked the very first time is that we've we've we've found double improvement in the throughput in in cases. So you're taking a a loved one support who used to do 20 to 23 cases a day and now they've doubled that. You're doing 5253 cases a day using the the prompt and using the content. And keep in mind that most of that content, or at least not most a portion of that content comes from the training material that we use to train the level 1 support. So what does that mean? That means that the folks that that they're getting on on the on the phone faster from new hire time to starting to engage customers and you spend more time teaching now the prompt and the questions and the iterative process you go through with diagnosing the problem upfront and you can immediately start becoming productive. Now we're working on making sure that, that we've got a, a finite way of identifying the boundaries for we've now kind of crossed into a complicated problem, not one that you could, you know, level 1 support could handle itself. So when you get into file movement, file deletion commands of that sort you, you like to move that to level 2 and Level 3 support just to validate what not only the system is saying, but that to have the expert look at it and validate, OK, if this is the right, this is the right answer. So a lot of training has occurred in and around the the handoff in that in that space. We haven't, although it's being worked on right now, we have not started creating content training content from the live activity that we're performing on a daily basis. We have folks working on it. But the the first phase of what we wanted to get done was in the proof of concept. We wanted to prove, we create content from closed cases and we wanted to prove that we could enhance the, the TSC internal to Veritas its capability using AI by getting quicker answers. So those are the first two things that that we wanted to get accomplished. We went live on the KM portion of it in November. We went live on the AAR portion in February and we're up now fully operational on the, on the, on those two aspects, training being worked on. The next thing we'd like to do then is work with Ascendo and, and you guys and, and really start looking at how we take the, the large amount of data and correlate the patterns that we know we have and start creating content out of, out of those patterns. Because as, as, as we get into the patterns, you obviously get into higher reuse. And as you get into those patterns, you can also give a lot more clarity back to the product team with things that we may need to go and and resolve within our product itself, not just solving problems, but hopefully working toward making an even better product. Yeah, I, you know what? I the synergy I find every time I talk to you is because people look at knowledge and workflow as two separate things. You know, be have you know we look at it together, right? Knowledge creating from all cases, knowledge creating from interactions, knowledge created from what the customer interactions are coming in from and predicting what did that would be and knowledge used by L1 knowledge used as a business value index. So knowledge used for training, knowledge used for self-service. So it's knowledge is the integral part of every workflow within support service operations. And that is to some extent that's the crux of the entire thing that you are talking about. It is and it's, it's pretty powerful when you start to move it fast and even more fascinating to watch the prompt queries that we get to go and review the queries and you can look at the queries at the time we introduced AI and you look at the queries today and just in six weeks, there's such a 7-8 weeks. I guess there's such a huge difference from when we started. And then out of that is the knowledge that that's getting created just from the prompts, because you can, you can now start to see, you can look at, you know, 7 or 800 people, 3 or 400 at one time on the system. You can see the queries coming in. You can see the types of questions that are being asked at the prompt. It gives you some real time feel for what your customer base is calling about related to your product. Now how do you convert that into knowledge that you can actually do something with in that immediacy of time Is the next step that we're we're we're looking at as well? Yeah. And we also talked about, you know, how this can be utilized to facilitate product improvements. Do you want to touch on that a little bit? Yeah, we're, we're, so one of the areas that that we're working on right now is logs are the Veritas product like any software has a lot of logs that has immense information. And so the next step of what we're working on with AI is how we ingest those logs that we get from our customers, ingest it, let AI interpret this large amount of data and then come back with an action plan. And early indications are that we can, you know what, it's going to take us minutes. The process, ingest it, process it and come back with a, a set of answers. And So what we're, we're, as we work through the process, we have air conditions obviously, and we've worked closely with the product team at making sure that we, we have the air conditions also documented and in our indexes related to AI. So what we can do is as, as we have, as we solve cases, you can take the content that's being created by closed cases. And you can look at this and you can tell generally what what types of issues on a particular release that was just put out to to the customer base. And as their adoption allows for upgrading the software. As we're getting in the inbound calls, AI is creating new article content related to a release that we were just that we just published. And so you can start to see as as time progresses, customers adopt, you can see the issues that are coming in, what types of areas in the product and the content creation for that new release is again, it's, it's done in, in minutes, it's not days or weeks. So the ability to get relevant information related to a release and the issues that customers have had published and available then to the support organization happens the through the process that we talked about earlier. And at the same time, we can provide the product team a more in depth understanding of what we're working on in a particular area of a, of a, a new release. And you, you know, all of it, it, it, it's just accelerated what we used to do. It just makes it all faster. And at some point you can start to bring solutions faster in, in the, in the product cycle. So you're finding the support organization playing a, a, a more relevant role in helping with the with product maturity, if you will, as you go through the life cycle. That's very, very, very true. Brett, thank you so much for taking the time to talk about how we are revolutionizing the support content creation process so knowledge can be valuable, actionable and can be aligned with customer needs. And thank you for discussing this in depth. Thank you for your time. Thank you, really appreciate it. Enjoyed working with with you and the team so and thanks again. We appreciate the time. Previous Next

Explore the importance of moving from reactive to proactive support operations. Charlotte discusses the difference between metrics and data, and how to use data to drive action. She emphasizes the need to look at data from all angles and use it to improve efficiency, reduce costs, and improve customer experience. Using Proactive Metrics for Support Operations | Transcription Kay - Welcome to experience dialogue. In these interactions. We pick a Hot Topic. That doesn't really have a straightforward answer. We then bring in speakers who bring their skin, but approach it in very, very different ways. This is a space for healthy, disagreements and discussions, but in a very respectful way, just by the nature of how we have conceived this, you will see very passionate wisest of opinions, friends. Having a dialogue. And thereby even interrupting each other or finishing each other's sentences at the end of the dialogue. We want our audience to leave with valuable insights and approaches that you can try at your workplace, workplace and continue the discourse in our social media channels. It's a pleasure for me to introduce Charlotte after,u , having had a few discussions with Charlotte, but we were right off and we were picking up exactly where we picked up from. And The discussions I've had with Charlotte really talked about the underlying Foundation of why we did the experience dialogue. So it's a pleasure to welcome Charlotte here to have a discussion with us. Thank you for coming. I Charlotte - Thank you for having me. Kay - Charlotte and I will be working together and we'll be having a discussion about a framework on how to look at support operations, data from the eye of proactive support shall be talking and shall be taking us through which support data matters. Which ones need which metrics need to be retired. And which metrics need to evolve and where we do need to. Look at this. Data will be taking some very practical examples and on how to support operations teams. Be transitioning themselves to proactive metrics with that. Charlotte, I'll hand it over to you. Charlotte - Thanks so much kay. This is me. I am Charlotte wouldead of support a snow plow. That's a behavioral data platform. That allows you to purposefully create behavioral data AI. I have been in support in deeply technical organizations for a lot of years and doing everything in and around support, for a lot of years. I'm sorry to So, say please years, to say both in technicalHP tech companies,I've been 18 years, fully remote leading, technical sport teams.I look after a little website, little corner of the internet called customer support leaders.com, and that is also the home of my podcast, which is customer support leaders.com,and it's all about support and customer experience. Kay - Hey, so many years of experience here too, I'm Bill, teensscale, businesses, add some Adobe, and also have done startups.I was just counting Charlotte, actually, have done more time with startups.Now,and that time has surpassed the corporate experience. And every time, , in the Adobewe took those Adobe Connect to the cloud. And number, one thing that comes up as soon as a product goes to the cloud is doubled because it becomes a very integral part of the organization. AndI still remember, we would rotate our Architects and we would rotate our senior Engineers to take support calls every month and that was something that we would do just to understand the pulse of the customer. And so I'm actually super excited to have this discussion to really talk about, , how can we get the pulse of the customer in many other ways? Thank you so much. So with Ascendo what we want to do if we want to be able to provide meaning to every interaction. So Charlotte and I are having an interaction here. How can we do the same thing as a company? At the end of the day, it has many, many, many interactions with customers and the interactions happen from website, forums, Community,chatbots emails, phones, And slack, teams, many options, that B2B companies are now having interactions with customers. How can we get provide meaning to all of those interactions is really what we are focused on and that's why this topic is very, very close to your heart. Charlotte - Indeed ,I think one of the things that we often do and support is we want our meaningful interactions to mean something to the business as well. Don't we? We, we talked about, we have these big desires, we have big goals. What support leader doesn't want to see at the table and use those meaningful interactions to unlock the key to customer success. And Through that, , we often So we're off to contribute to the business through driving efficiency and contributing to product and contributing to revenue. These are all really big goals.And so how do we get that seat at the table is something that is often asked for support to do that. We often focus on outward metrics that the business understands.So we talk often about customer satisfaction, our average handle time in terms of efficiency, mean time to resolution and how it contributes to customer success and customer satisfaction. But these are all really lagging indicators.These are all metrics that lag behind our ability to provide more meaningful reactions for our customers, I think. Kay - Yeah. Those are the metrics that most support leaders are looking at today, right? Charlotte. Charlotte - Absolutely. They are. They are. Take my van out of your day and think about not reacting to those external metrics. Those external metrics are very important in terms of being able to give a narrative around the health of what we're doing, but actually how we can use turndata internally within our teams within our business trunk functions to be ahead of the game, stop reacting to those lagging indicators,and actually proactively create data internally. That helps us. Those indicators. AndI think it's really important just to take a step back and understand what the difference is between metrics and data because we use them interchangeably quite a lot and metrics is the word that strikes fear into every support person's heart, right? But let's just be really clear. What we mean metrics are the parameters that we might use with quantitative be all measures in and of themselves. So your average handle time is a metric but it's made up of data points. So what data really is, it's actually the underlying numbers in information that we produce and collect and metrics what we produce from that data. So when we think about data, there's a lot out there, we might have time data,we might have, in fact, data from our health centers. We've got product analytics and we'll dive into some of those shortly, I'm sure. ButLet's just think about data that can be created purposefully. And with a structure that we understand, and I would call that data Creation with snowplow, that data creation or as a byproduct of all of our other systems. So This this term that we're beginning to use of data exhaust data, that is a byproduct that just happens to be there because of interacting with systems all the time though. that's our data at the low level numbers. Kay - One of the wonderful things that during that first discussion,Charlotte isometrics, why doesn't it work anymore? The reason it doesn't work anymore is data has become huge, not all the data that challenges talked about, right? The structured data, the byproduct data, exhaust data, the unstructured data, they all have become large and focusing just on metrics means focusing only on customers who have filled in some of those cervix and that me be only a percentage or a sliver of a customer population that's number one. Number two is, we are not getting the level of color that when we don't include all of the interactions, we are only getting a biased view from it. There is squeaky customer or from a high paying customer or something like that. Instead of everybody and getting the feedback or getting the insights, from all of the customers becomes very important, not just for SAAS, but also, Also, for non SAAS for even traditional come. that something? What we have seen. So what this data and whatCharlotte is alluding to, with respect to the difference between the metrics and data is seems to be very or knowing the difference between metrics and data, seems to be the underlying Foundation of always supporting moves from proactive to from reactive to proactive. Charlotte - Absolutely and how understanding the difference and how you react to and operationalize around metrics versus data, allows you to do the things that you very kindly outlined on this slide. And That is like begin to look for patterns, begin tomine, our customer data, make it better, use it to interact with our customers in a more meaningful way. As you said at the touch but also the, , internally again driving Operational excellence. If we concentrate on the operational excellence of our business functions. Then it hasan onward effect in terms of driving value. For our customers in improving the customer experience and therefore, in improving. All of those lagging indicators that we outlined at the start, our customers' satisfaction are handled times and, and everything else. Kay - Yeah. That's it was wonderful to see this report from Gardner on top priorities for customer service and support leaders actually just the 2023 report, and the number you can see that it's mining customer data is important primarily from helping out representatives from providing that intelligence that's needed for taking in that seat that Charlotte was earlier talking about at the table, or the support leaders to be with the rest of the leaders, to be the word true, what the customer, and to get Rest of the organization to be more customer centric.uh So I think it's really important.Therefore we've already defined metrics and data, data can feel like an almost, will it really is an inexhaustible landscape of numbers and information. And it, Charlotte - I think it's quite often difficult to know where to start. Particularly, when we talking about actually driving actions from it.So one thing that I like to do is think about data in three different ways.There's some snow plow. language in here and there's some Meyer language in here, but I Think it's really important to think about The quality and the usefulness of the data that we have, and what we can do for it, and what we can drive from it. So first of all data, that is best for light work, the less reliable or anecdotal, this is actually not that actionable,uh it's valuable. If you are able to take it and appreciate what you can do with it,and I would say the anecdotal data,or data that's on reliable data. That's about feelings and everything else. Is really an inspirational thing. So those the things that might trigger a research project or might drive you to go and collect more accurate data and more structured data and so on the stuff that can really Drive action exhaust, data might actually be accurate, you might have a whole landscape of numbers at your fingertips. But If they are, nearly the byproducts without any thought given to exactly what then the meaning of those numbers, It is of everything you're doing. They can be unfocused and disparate, and really, that's what we need to think, not about research projects but about brain structure andAnalysis. And finally, the most positive end of this is the data Jizz that is really created specifically for a purpose.This is where I love to play around because I love creating data knowing what the question is that I want to answer. South thinking about data is, what the how its structured allows you to create action. If you have the question in mind, what data you need to collect, , how to structure it. And you can use that to answer your questions and therefore driver actions. Kay - That's so beautifully said because one of the things that then these targets and do the reform of we were talking to initially, its customers. The first thing was hey what we have these these questions that we could just ask those questions and we get those answers, right? And provide the patterns for the Soma, it's that questioning that Curiosity, that's coming in,not to just look at the metric but to say okay, what does the data say? How do I need to carve out the story? What, , that Curiosity stemstarts in this entire exploration? So I love how you said it,Charlotte and I think thank you. Charlotte - And I think what's really nice about sending these three layers is that you can approach this from either end, , you can, you can you have a kind of idea, but you have to begin and go and see what you, what date you have. So you might start at the anecdotal and like, this is given Confront him, , I'll go find some things that sort of support it and then I'll dig the Diabolical data we've got and then I need to bring some some structure to really answer the question or you've got a really specific question and you can answer it because you've invested in that structure already, which is great, but you might enrich it with a bit of exhaust age. But very particularly with the anecdotal and get more narrative from the, from the fuzzy are end of the data spectrum of your life. I really like that part. and so, I think in,Helping other leaders out there. Thinking about this is really important to me because it's not clear often I think at the start, when you think about your data Journey,exactly how you approach all of those things. And one thing I've asked other or other leaders from other organizations in the past to do is just take this two step approach literally list everything out that you have Even dated something that you don't think is data, it is Data. So, including all of those, your slack conversations, people's feelings comments that you see in survey responses, this is all data,but list it all out and then just take the take the time to categorize it so that you give it the appropriate way so that, , where you can start to ask questions andwhere you need to bring data on in this, , down the ladder if you like to. To actual action at the bottom. So you might have this anecdotal but we've all got it. We've all got buckets of slack conversations about our opinions and everything else there, but they do Inspire the research, don't they? Kay - Yeah, that's what. Yeah. Gear Generation. Yes. Yeah, one of the customers actually had an outsource there at one Gmail's Royal one team.They still follow theirL1 model 0, of the swarming model. But it's fascinating that they were talking about people. Who has posted notes in their computers and all of that is data, right? So, all of that is knowledge that is sitting in somebody's and unstructured and that needs to be coming in and there's a wealth of information from the front end of people who are talking to customers that can be piped in all the way up to the escalation. Charlotte - Yeah, yeah, absolutely, absolutelyPostit notes.uh I love a screen surrounded by post loads. It tells me. to pay attention to it, take it to a whole nother level. Then like, we again in help centers, we're producing data all the time, but we don't necessarily pay much attention to most of it but we are generating time stands. We are replying to tickets, we are resolving tickets. Actually, most questions have an answer and that's a fairly good answer. It's not. It's not structured well enough necessarily to mind immediately. Idiots lie, but usually answers are having an attachment to the question, ? So, ticket resolutions are what I sort of consider to be exhaust data as well. What you need to do with exhaust data as I said before is really just spend the time bone to give it further shape to , analyze it and decide. What's useful? What's not what you can, what you need to do is little to as possible to make actionable it and What needs further work and this is where the analysis comes in is on all that. Seoul station. Now, if you're very lucky and you work for a company like smoke now, or we spend some time with me, you'll know how passionate I am about creating data that asks, where the answers questions from the get go. And so, the last one here is where I spent most of my time and that is creating data and pulling data points together to really specifically answer questions and therefore Drive actions. And the structure comes in all sorts of ways. , it's around. It can be around understanding how the different data points that you've got fit together and what, what narratives and actions you can drive from bringing two pieces of data together that never existed together before or it's, or it's also possibly bringing structure to something that didn't have stretch before. And for me I'm deeply passionate about having a pretty straight ticket tagging taxonomy.So that's one that we have a very structured approach to a snowplow as well. So it's very many of these data points, what we can drive actions from Kay - Absolutely. And what you're really talking about is getting the data ready as time series data, right? So then it's time data. What happens when at what given point across the interactions that can be mined for AI, Rich, right. So,I'm just going to tag onto what Charlotte was mentioning and call out the various types of data. So we have the transactional systems that the CRM,the bug, tracking the knowledge pieces and all of that. On top of it, we have everyday interaction that comes in from the various channels. And on top of it we have the data exhaust that comes in also from all the logs and the product usage in all of it. What is interesting? Here is Tithing in those pieces of information, trying to find those patterns to answer those curious questions. So what kind of problems are really happening? If you can,what parts of the product right now? Or a month ago, where is it? Increasing,which part of it is increasing, who isMooney? Who within the team is an expert in these kinds of problems?What? And which of these Solutions are being most effective? Or a customer. And how can we take that piece of knowledge that is in a human's mind and used to resolve or train somebody else who's coming in on board, right? So it's really, if systems would be, it doesn't we call it human,human machine interaction, right? So, essentially what we mimic is how humans solve problems. So it's very exact Charlotte said which is, yes, there is a solution for every kind of problem. And even if there is no solution, how do we humans? Think about it, right? Oh, this is very similar to something. I did three months ago, and of course a little bit of the solution, let me dig a little bit more, right? So, it's that mimicking of the data that it provides to the agents, to be able to solve things faster. Charlotte - Absolutely, absolutely. And that's what we want to do. All three solve things faster,better, more efficiently and with more value to the moment. Yeah. So I thought it would be useful to describe briefly what this looks like in reality. I will say that the charts on the right are anonymized and fictional but there are taste of the kind snowplow. of things that look at And so how we, how we think about this and how we've operationalized around the state today,which is subtitled talk came from,it is really about what we, what data we have, how we structured it, how we bring it together and what we added, actually,to answer the questions, the big part of going through that process of understanding. What data you have is understanding what data is missing. Then you need to answer those questions. Sowhen I joined smoke, now, one of the first things that we did was begin tracking time and support, which I know is controversial. I know it's controversial, but it's important to me and to us because it's a very, very complex ecosystem to support and it's a really, really valuable and insightful data point for a number of reasons. And so, in tracking our time, I was filling one of those missing gaps. It's one of those missing actionable data onesand beginning to drive data. Better created and joined up data. So I love this phrase,which I buried in the text here. But, , you can validate hero hypotheses or calibrate emotional readout which is take take, all those feelings about all of the pain. We're feeling and supporting our pain. Our customers feeling and actually make them validate them .Is this hypothesis that there's this thing? This PostIt note is this bit of feedback valid in the great landscape of things.uh It of course it's valid actually because it's somebody's opinion or feeling. But is it actionable can actually do anything with, , and in and in calibrating and being able to compare one thing against the other, you can drive actions. So that's what gets teams out of the firefighting mode. It really does because it's very empowering actually. You're absolutely right knowing that you have information on how you can draw on any coin and we repeatedly come back to this. And even if we have a question from six months or two years ago,the data stays there, we don't throw away. We can because we're a data company, we love dashboards, everything is live, everything is continually updated. So we can go back and ask the same question and see how the landscape has changed and to You That we have more of a dashboard and this is a little taste of it as a service, all kind of fictional.But it just has things. Very visually, it's important for us to be able to and hotspots spot patterns and allow us to dive in very quickly. So, to that in turn going back to do the process that I mentioned, we create all of our data very intentionally drums sources together, outside of the CRM.So we do have Salesforce then just data, we've got our product and their tits.We've got ourtime tracking Key. And a number of other pieces that we can pull together. Other. So some examples I've given here or, ,how much, how many not just, how many tickets are we getting an objective taxonomy, and what's the Applefrom my team and beginning to resolve, a certain type of problem? And by effort, I mean, ours, I don't mean elapsed hours for a ticket. I don't mean resolution time. I mean actual effort because we all know resolution time is elastic. Weit depends how responsive your customer is, , you might have to go off to another third party. But effort is a really good indicator of the complexity of a problem , I think. And so I could become more and more important as groups of people are working together to fix problems like this warming model.Right. Exactly. Exactly. So we can respond to that. We can respond to ing if this is more complex than we think it is weird. Again, it's calibrating emotional reader, something feels a bit painful but actually is it five seconds out of all day and it's just not worth, like engineering around or We do something different actually quite a lot of the way I approached a chore, it's should we be doing something different? to that end we look at things like which of our customers I dug out some fictional customer names,they're from The Simpsons and everywhere the organization's there. But this perspective of like, again, the effort involved in supporting customers is really insightful. Cuz it tells us if a customer is, it's about being proactive. It's about again, getting him ahead of the game. We can see when customers are starting to need more of Time, need more of our support, we can get ahead of the game by these visual clues. That said, I should probably spend some more time investinglike, more quality interactions with a particular customer. For example, maybe I get on a call with and maybe we just do a few more coaching or Kohl's or something like that. Customer, I get them. And that, and the, the chart underneath the kind hand in hand in that respect because, , a big overhead is tickets wandering around your team, looking for the answer. So I love to drive independent ticket resolutions. So how many of my tickets are sold by one? That's awesome. And can deeper dive and say which of my people on my team are seeking more help, ,which people are providing more help. So it's beginning to identify Stars mentors and people who do need just maybe a little bit more knowledge and support which in the early days we know that it's supertight onboarding is really critical particularly in ain a very technical environment and then of course in terms of operational excellence understanding stretched, my tears or isn't on any month a year. How, how am I matching my resourcing against my incoming load is really important manage, operational excellence. That's a sample of some of what we've actually doing day to day. Kay - I was actually just hearing you did actually help you say as Already better, right? So whether it is the operational aspect of it, whether it is going to an agent and saying here is the reason why, ,I would love you to take this training. It becomes very collaborative with the rest of the organization to March forward on this customer mindset, right? Charlotte - It really is, and more than anything, it helps you tell those stories before. the customer tells you, though, before the customer says to you, I didn't have a great experience. Yeah. And that's already more than getting ahead of the game driving. Those proactive interactions are proactive actions from this data because you can respond to it very quickly, and this misleading information, not lagging emotions. And so I think, those actions, I just wanted to throw out a few ideas and I know you'll have some thoughts on this as well because we taught quite a lot about this slide when I was pulling them for your support driventalk. And after two so I eat. This is awesome. Yeah, please. I went full everything on here but I think what's really interesting is just just how Butuh how how many different parts of your operations,you can touch with this approach and you can improve with this approach,and that you can, , you can modify and positively, and proactively modify opt is where we're getting to ultimately with all of this work. In terms , the technical side reduces humans in the loop. So reducing the tasks that could be Automated away and given the team better quality of their Professional Day,improving internal, tooling, and reducing friction. These are all really positive experiences. You support me organizationally managing your customers better efficiencies creating and getting the right people the right problems. , I think these are all really good ways of really good things to think about in terms of how you interact and contribute to the rest of the compliment or rest of your own organization. And then from the point of your business growth you'll help your customers by reacting. And adjusting the way you solve problems and as you said before, adjusting what knowledge, you use it, that definitely contributes really well and your ability to get bored quickly. Kay - Yeah. I actually would love to shallot. start seeing if I'm a Charlotteto all the support leaders. Start being curious,So it's the questions that you are asking here. That's making you look at the data and coming up with answers. And the interesting part is there is a question that someone has asked saying how much data is actually needed and will start ups, and or companies with new products will they have enough data to do this kind of analysis, and from an AI aspect? Absolutely. Yes. Because until you start thatCuriosity and questioning you don't even know, there are always going tobe. This is a journey, there is always going tobe data gaps, son. You will encounter those data gaps only when you start on the journey. So when you start in the journey is when you would recognize, oh, here are the gaps and I can fill those in. In models are also when it is done, these are proprietary models you have done for support. So they are very good at looking at even smaller pieces of data and coming up with answers for a lot of these questions. So Charlotte, do you have any comment on the amount of data before we go into the slime? Charlotte - I think it's a super, super interesting question and I think there is no single answer to that. I think it's exactly as you said and as was describing before, you just have to get started because until you understand what data you have and how you move it along that chain. So well structured questions, that question answering data,, that drives actions. You just don't know. And I think, , in terms of the number of data points, you don't need much. I think you'll like it, but I think you have to get started and I think the important thing is beginning to data along that move your German. Kay - Yeah. Charlotte you talked about, which is,um here are the types of questions and just extends into the type of questions across the various support teams, what the leaders are looking for, were the agents are Looking for and customers or even the supply chain and the logistics teams are looking for course, hardware, and software companies. So across the board, there are all these curiosity and all of these questions and we had the discussion with and last last time, and she was alluding to an example and she was talking about an example where even with a small amount of data, she was able to get answers for a lot of these questions. It's looking at the similar data from various angles and looking at the patterns across those, to be able to come up with good arguments. That can help say a story, right? So that's all I wanted to cover here. Charlotte - Yeah. It's about looking at small amount of data from different angles is all about structure.It's like what is the thing that I need to extract? And how do I fit this together and you don't need a lot of data, two or three. Disparate points is Entity to give you lots of different pictures. So, , the final thing and I know we both, we probably both want to talk this Like A and B, but but B me be the key takeaway me is the leavers column, ,it's what actions can. Try it. And I talked about some of it. When I looked at my dashboards, we talked a little bit about it on the following two slides where we just drew out. Some of the kinds of things that we'll be looking at. These are really the questions aren't they? How do I, how do I do this thing? How do I improve this thing? How do I? And, ,and I think for me the Believers, the actions that you can prolonged a bigare all in that column and they're allHave to be, they all have to have a pounding and strong data and in a strong approach to data and well structured data because otherwise if you pull a Lie by you without knowing exactly what you're pulling, you're not going to get measurable outcome for it from it and you're not going to see.mean, every one of your impacts on those on the right hand corner as a number by to it. You can't apply a number without founding in data. And for me, that's what I take away from this, that the passengers Civil actions are great, but you need data to be able to measure the album. Kay - Yeah, so there are a lot of questions here. I'm trying to peel some of these questions too. So there is a couple that is appropriate to what you're talking about here. So can you comment on what data may become important, given the projected, , Global recession. That's our way. Charlotte - I mean I think unfortunately we're all having a little bit. Operating more efficiently efficiently and there's big term of operational excellence huge part of that is operating efficiently and therefore to I mean it's the age old story don't think that the while we are in the throes of a recession I don't think this story really has changed a great deal of support ever since I've been doing sport which is how do you more with less and that's that's what every support leader will. It will be dancing but just more so now than ever before. So I think that in terms of Creating Efficiencies In your business function.Unfortunately, sometimes that means people, but, but actually, it doesn't necessarily mean lay-offs. It means how can you provide a good or better service with what you have? How much time do you invest in improving things operationally, , taking team pain away so that they're able to provide a more valuable experience of all of these things. And I think it just comes down to doing more with less, and more. Can mean many things. Kay - It's not just about load exactly. And it's also not just what, , makes existing people work hard, and it's also making them work smart by providing them and empowering them with tools and techniques that makes their job easier so they can do more with less in a smart way, right? So Absolutely absolutely. The other question is,there are so many ways at the end of the day, , even this chart Talks about increasing customer support experience, right? So because they enter your company's marketing towards increasing some customer experience,but if there is one leading indicator in here, that you would like to pick for the support team. I think that's what this question is about. Does it just see it? I need to pick one forward looking indicator. What would that be? Charlotte - I would look at the value that your team can add to every interaction. That's going to vary so much organization to organization. But I think that surfacing Information data,uh actionable data,from across your business, to your support team is really critical in maximizing the bag. You are your customers. Our been for every interaction with that support team.And so I think you have to figure out what value add looks like to your organization.And I'm sorry, this is a little bit of a wooly answer but, but that's just so different, .It can be, , how do, how do we process returns faster? Or how can I help a customer to a next to use case or anything, in between, where I,I think figuring out what your team can do to add value to customers into action. Ins. And what that looks like to your organization. Kay - I would agree with. The reason it's different from what you're saying is because organizations are in different stages in this journey. So that's why it is different, right? So for some, we are starting off with,,bringing inuh collecting pieces of knowledge. For some it is I'm starting to do some service, for some it is I want to empower my agents first. Or something that eats it. So it's different for different companies. So absolutely. Yes. The I know we have a few more slides to go through so we should do that General pet because I didn't get the next question. I guess the next one just asked everybody else out there. 2023. Sofor Or in five of them are looking at customer value and enhancement. And that's the one that Charlotte was talking about: what is the customer value? I can provide support as a teen and how can I tell a story about that to the rest of the organization? And how can I make the rest of the organization March along with me? That's really the Crux of what a support leader should be doing, and with that I think that pretty much speaks to the slide. Charlotte - Absolutely. And I just got one thing to that, which is that, as we said before, understanding what customer value looks like to an organization.how you tell those stories back in the business, relies on you being able to measure what customer value is as well and believe, as we can pull it. So it's super that I learned portables. Kay - What I learn from this conversation, Charlotte, starts with the Curiosity of a question, right? So, and then align the data, and what data can answer those questions then that data in itself will come up with a story on what needs to be done to improve support operations. And then how do you move forward with that story to bring the rest of the Ization and the team on board, right? So that's the path that you clearly laid out in this conversation.Thank you. Thank you very much for providing that insight. And thank you very much for having that framework for all support leaders. Like I said, I would love for everyone to be your shelter. So I'm starting to ask questions. Charlotte - That's great. Thank you so much for having me case and pleasure, and very happy to continue the conversation with you or anyone else who happens to be listening. Kay - Thanks Charlotte. Previous Next

Explore how cultural shifts are reshaping customer experience (CX) in Africa, focusing on proactive service, innovation, data protection, and cultural understanding. Cultural Changes that we see within CX in the African Continent | Transcription Kay - Welcome to the experience dialogue. In these interactions. We pick a Hot Topic. That doesn't have a straightforward answer. We bring them. Bring in speakers who have been there, done that, but approached it in different ways. This is a space for very healthy disagreements and discussions. But in a very respectful way, we have been justified by the nature of how. We conceive that we will, you will see the passionate voice of opinions, friends, having a dialogue and thereby interrupting each other or finishing each other's sentences. We wanted to make sure at the end of the dialogue, our audience leaves with valuable insight and approaches that you can take. Try and take in your workplace. We have been having deep and long conversations for the first time. We're going to be having a short and sweet discussion. And the topic today is primarily around cultural changes in the African continent. And with that there. Is so much that Covid has brought out in various ways. Except for the globe. There are individual differences that have been there in various continents and we want to focus specifically on the African continent and see how we'll see as different in Africa and then dive into it. And we cannot find any better speaker today than Ifeanyi Welcome. Welcome to the show. Ifeanyi - Thank you, Kay, for that introduction. Kay - The main reason why it was very interesting to have you in the show. If only is, you have to the success just for you, but you're also bringing other people along with you and bringing you into it. So that was excellent for us to be able to Showcase that in.our webinar, sopost quickly, we can start off saying, I would love to be would love to hear what are the actual cultural changes that you see within the African continent that people outside of it need to be mindful of. Ifeanyi - Thank you for that question. So when in terms of cultural change, right? As you love us might be, you know up, you know from my interactions with folks in Europe and Asia, you know, I've had you know interactions around what you know CX means, you know to Advocate General African consumer and you know, a lot of folks believe that when it comes to customer experience people generally here. In Africa, I would expect, you know, Quality Service. They would expect time to their queries. But, you know, a lot has changed in the last 1 - 2 years, especially with the Advent of covid, right? consumer customer expectation, you know, I would like to highlight, say, if you so in terms of, you know, customers Expedition generally, so people here in Africa now don't just rely on reactive service. They expect, you know, brands of businesses to be proactive with the savings. Not necessarily waiting for them, you know, to reach out to the bronzer. Say he looks, we have, I have a problem, they generally expect. You know, Brands to be Innovative and intuitive with the approach to support and, you know, trying to identify areas where they're having challenges and you know, practically resolving those issues and also in terms of, you know, the customer Journey customers expecting sort of like it connected Journey map in terms of you know, how it dissolves ax approach, right, you know, it's no longer or you just use spoken to our sales department. Okay, Our customer success department will return as it is divided, you know. Which to customers, you know, trying to, you know, ensure that businesses can break, you know, that silence in between their process and show that, you know, clients have that seamless experience throughout their journey with the brand. So these are some of the expectations of customers and then when it comes to,you know, that Choice point, you know, customers also expect personalization, right? Is not just one General approach to, you know, reaching out to customers throughout select torture. you know, send a blast email to every customer. Say, look, we have this update, you know, trying to personalize interactions from support to General Communications, making sure that customers feel valued, making sure the fuel value. Because people here in Africa tend to say I want to speak to people from my country. They tend to appreciate, you know, Brands when they recognize their look and Mr. Social, so I am this,so they would appreciate bronze, you know, try to personalize the approach to Interactions. And also one interesting thing. I also found that you know, customers. Also, you know, expect customers are Brands to be Innovative and you know, too fast thinking in terms of their product to services and support. Not just, you know, deployment program Series where we are and will continue to improve and then they are not seeing Improvement. Do we want to reach out to you? To see? Look, I've seen such also in Social business. So in a social environment, why not have this right? Customers now come to you to tell you, Look, we need this. They know what they want and the demand for it. So they expect Brands to be very Innovative across their product offerings. And then one last thing in terms of data protection, right? We've also seen interesting historical data. We are customers, you know, from the force sales Soviet 2020 customers. Now expect, you know, you know, expert Brands to protect their data. So for us lie to us as payment, Payment Integrations. I've seen scenarios from, you know, consultation experience, with startups. I've seen scenarios where customers feel hesitant to, you know, use their card, their cartoon mobile apps, or maybe your web application because they are scared of losing their money. So bronze needs to, you know, sort of enhance their, you know, their approach in terms of, you know, making customers know thatof their, there they are on top of data. Protection policy, ensuring that they are keeping their data safe. And that any information entered in the system. I kept seeing the need that you need to know that, you know, that you need to end that trust. now, we are browsing to antitrust. Some customers and you do what is required at different points in time? A day. I think these are the key five major cultural changes. I've observed during my interactions with Brands and customers and also from, you know, so very portable so saying Kay - yeah, thank you for that. Couple of things that I'm noticing from what you just said, value. Bringing people value.um Bringing customers value seems to be Global. um, The second thing that you mentioned is also, that being proactive is something all customers are expecting right nowbecause part of it is an instantaneous response that they get in everything else in their lives. They wanted to see proactive outcomes within their customer support and customer success team. So, I think that's a global phenomenon where I see that the Africancontinent is evolving from what you just said, it seems to be around data and protection and all of that. So in terms of,if prime, a, CSif I have a serious, Global operation. And if it's any scale, Global serious leader, when they Bri have teams to see what Kind of things that they should look for from an employee experience perspective. Ifeanyi - So when you say, what should they look for you, talking about, know, characteristics of the employee or the skilled trades. So what are you Kay - I am primarily talking about? How, what is the? What are the things that leaders need to do to make sure that their employees are well? Sustained from within the African continent. Is there anything else? That's culturally different, that support leaders need to do Global support. It does need to be done specifically for their African employees. Ifeanyi - So yeah, I would say yes. Why? Because you know, when you say Global sales operations, you talk about, we talk about, let's talk about Africa and India Normandy. So Africa is a regional culture. We have to serve a diverse culture. We have diverse cultures and we have people from different countries, including South Africa, and people from Nigeria. And you know, and we call today Petitions. In terms of interaction with Nadia various companies are also different because what they expect, how they expect to be treated also differ by for instance, in Nigeria out, speak from my own experience working for an international organization. I would expect that, you know, sometimes in terms of update timing by climbing to work, how many hours should I do? I need to work, you know, in other countries like in the example United States people work shift different tasks. Slightly in Africa, different people value their religion, right? People to remember, you might want to deploy to enforce new corporations. And then someone would say, look more on today's Sunday. I have to be in church. I can't afford to, you know, to, you know, to leave my religion and be at work, right? They value religion here, people, take it that seriously. And so if you, even if it's a global sales operation special needs, use to consider, you know, treats that they need to put in place then it will be around, you know, ensuring that. No, the Devalue the loop in between the look, the above the contents process and also look at the current environment, you know, and ensure that the ability to incorporate, you know, what people believe their lives of all these I can for instance and idea we have a lot of worries these you need to be able to appreciate how you know, give run this and make sure, you know you are taking note of that. Kay - let them honor the local cultures and local things while providing Global support, right? So let me also add this recently. Ifeanyi - I was talking to a Founder in Newfound of the West part of Nigeria and was helping to set up the sales operations and then because there are too many holidays in Nigeria. And how can I keep my Customers and say, look, this exists? This is what you should be looking at in Nigeria. And this is what is obtainable you have to key in? Otherwise, you'll be employees. Because people want you to recognize that today's a holiday and you have to grant that, right? And if you need to have someone, you know, it has to be that has to be open. Thinny shouldn'tbe something that you have to foreign Force, right? We should have the choices. It looks, I'm going to walk you're pay me extra for today. And you know, all those things that no matter to ensuring that you're able to keep your employees are motivated and engaged. Kay - Yeah, so there is actually a follow-on question from our previous comment that you made, concerning proactiveness. So, I would love to, you know, like to just bring that question up now rather than later. So in terms of being proactive,what are the things that support leaders need to look for? And you can answer it generically and you can call out something very specific to the continent itself. Ifeanyi - So, in terms of proactive, proactive, Outreach or proactive strategy in terms of the engine supporting your customers, I would give one good example, right? So let's come to less constant support. So you need to do it as a business leader. You need to be able to level data. Because from CRM yours, if you have a walk-in too well, automated you have all the trigger setting the right properties and you all constantly update those properties and ensuring that you're able to make sense of the coming in, right? I'm sure you would get a login. Inside. So you need to pay attention, you know bees are leaders who also need Keen support in looking at what the data is saying on a week-by-week basis. What are the top trends? What are the top issues and challenges? Our customers are facing user experience issues. It could be, it could be poor support, you know, from the dissatisfaction Matrix. It could be, you know, it could be product stability. It could be anything, right? So you need to, constantly, look at your data and see what data it is telling you, and then we'll take it back internally to meet with your engineering and product team and see. Here's something that you can resolve internally and ensure that you know, the custom expenses are smooth. So I'm pretty much sure that data will give little insight into you know. Go ideas of productivity and then the second part would be around the customer success approach. Like when you have died in the service business world, where you have a CSM, managing an account and then, you know, perhaps, they are different users, you know, currently using the particular, you know, a particular software and maybe they are some set certain goals that you have. You put it as a benchmark to see that within this given time. Different users should be able to have achieved ours and accomplish different tags. And then after me, You one week or two weeks that you have said and you go through the profile on the portal those and you're just to have to elect review of what the performance of your customers, and then you realize that your customers are performing today represent below what your, your actual expectation? Then you have to be proactive, you need to, you know, engage, you know, you need to further engage your customers and see new ways to understand why they haven't changed at any challenges that you have been and see how basically help them to achieve, you know, achieve those tax. And that way they will Be able to achieve their objectives. Kay - I love how you tied in the proactiveness to the value. You know, part of being proactive is not just, you know, providing that value. So, you can take anproduct, a little girl to a customer LED growth. So, right down there. um, Do you see any differences between B2B b2c andin this area? Ifeanyi - Sure, because when you talk about B2B and b2c, you know that they are different personalities like business, you know, your approach to supporting consumers, who, because early is different from one when you're supporting business businesses. So I'll also speak from my experience working at this half. Our Solutions are C customer success specialists where you know, I have to interact with decision makers, you know, senior managers and you know, ensuring that we can deploy our solutions to meet their daytoday operations. So the Expeditions are usually different from the way the expertise to support them is quite different. From the way customers expect you to support them. So, in terms of business, businesses are looking, at know, the listicle to their business.And, you know, customers are looking at, you know, what do I get right now? How do I have you? Make this happen? Right? But businesses are looking at, how can you, how can you, how can you drive value Falls in on a long-term basis, making sure that our own business, you know, not just supporting us making sure that we are also achieving their desired outcome of, you know, subscribing to your product, right? Not just some tiny thing. Perspective. But from a broader perspective. So when you are talking about the support approach, it has to be like an animalistic strategy like a bigger broader plan strategy to assure them that, you know, you can effectively engage different channels and ensure that they canmeet up with their own business demand. Then when it comes to customers, you know, the Apple 2 would be different because, you know, when it comes to customers saying no, it depends on your business model, right? So if you are looking at say regular or toxin, We'll talk like a regular customer, you know, it's just like taking a total purchase, engaging and interacting with them. And then, everything you know, in technology to ensure that you can keep the interaction going, but if you are talking about, you know, High net worth individuals, or you know, key accounts or, you know,High Revenue customers, then you upload to be different because you have to come up with an personalized approach, like, you know, having like it CS a is CSM, you know, engaging that that customer to ensure that they can get there. Observed outcome. Kay - How do you know there's a lot of discussions around see us being responsible for Revenue, right? So growth through supporters who are how we call it, right? So, where do you go? You see that phenomenon happening in Africa? Ifeanyi - So, yes, when it comes to revenue rights, the expectation is that customer success owns revenue. And also from my experience. Stations where sales will say they are also part of their revenue generation team and then it becomes like a one-sided thing. Maybe they take a few thousand customers and then CS 50%. But ideally, I would say that customer success operations or team wins Revenue. Why? Because they are the ones, you know, you know, after let me, let me, let me take it back. So when your cells bring in you, do the customer success, I expect to provide ongoing support and also, You know, ensuring that you are able to manage that relationship because and by managing the relationship, they are saying historical Behavior pattern that the CSM understands about the customer. And the third point from the service. Might, you know, maybe the last time, this salesperson reached out because it was the last time they requested for a renewal, or they wanted the invoice, but the CSM was a customer. Success managers keep abreast with customers and understand the customer and there has been no, there's been a relationship, you know, you know, in terms of the Of times, they've been with Equity, understand the customer better. And also, you realize that within that experience, you know, people might have changed how you manage it. You know, when you come to change management, people might have cleaned. And then the person who has the buying decision, the decision-making power might have left and if someone else and maybe the CSMalso developed an approach to managing that place on ensuring that you can retain the person. So I would say that the CS team needs to continue managing the relationship anyway. , When it comes to revenue and ensuring that you are able to renew their contract, then you can leave you at the admin lot in the long run bringing a sales team, you know, when they are, you know challenges with you know, baleen, Etc. Kay - A lot of support leaders underestimate the amount of change management that's required. So, you know, I'm glad that you called it outum the other, you know, if you think about the flywheel, see, as kind of comes in the middle. You are tying into sales. We just talked about it, right? Marketing, product development. And I think you did mention on the product development where you talked about the data where we can, we have to harness the key issues that come out and provide it as feedback back into the product. So we have that continuous innovation Loop coming in from support to product. So we talked about that, it would be wonderful to where you think that flywheel is moving towards the continent. When I'm talking about the flywheel. the flywheel of Cs being in the centerand all the other teamstying in into the CSS teams. And recognizing that support is important for the growth of the company, right? So that's the flywheel I'm talking about. Ifeanyi - So, I'll say that. We see a little bit behind from my experience, especially with you, with your new startup. you know, in Africa. So they still believe that you know, more of that, more of the, you know, that up is no Sport and in an customer retention lies with, you know, products, you know, having a great product, having a great marketing team, having a great still stream, right? So but, you know a lot of love of effort for the with love, being made to ensure that, you know, Brands understand the importance of support and you can see that you know, in terms of what I do personally as customer success and failure is I try to talk about about the importance of customer support and raising their awareness to the startups. Can start leveraging understanding the importance of customer success to business and how, you know, how to ensure that, you know, customers are retained. I often give this as an analogy to starting herbs. I always know when I meet with them, and we have this like argument, there's always fallout. And then I make him understand that you know, I look at the customer phone like a basket. It's just like you have the sales. the marketing team bringing customers into your bust into the basket and you don't have any protection on them. Customer success is that protection that keeps your customers and ensures that the engagement is ongoing. Interaction is smooth there getting the prompt service and, you know, they're getting everything that they need to, you know, too, be do remaining loyal customers and also make them, you know, and advocate of the brand. Kay - So, there are a lot of Youth Watching this because you have beaten their leader, and I'm sure,You know, they want to hear the words from you. um, What advice do I want to rap with this question. What advice do you give to those who are aspiring to get into customer support and customer success? Ifeanyi - Yeah, so from experience, you know, I'll take it back to the business and the industry generally, right, you know, customer experience. Most expectations are constantly changing. What you used to know yesterday, might not be what's obtainable today? So you have to keep updating yourself, you know, keep top of the, you know, current industry strengths. Not just saying. Oh, I just got fired yesterday. And now it's 256 presents tomorrow. What, you know, yesterday might be obsolete today. So you need to constantly, you know, engage with cs folks, you know, out there on LinkedIn and ensure that you know, you join those workshops webinars, you know, try to learn new strategies, new approach to CX and keeping yourself. If up to date as to, you know, what happened in the field of customer experience Kay - be they're being proactive to that's what you're saying. So sure that you get to see us so you can be proactive with your customers. So Ifeanyi, It's a pleasure to have you in and share your here, your background, your experience, and get a glimpse of the African. Tenant, super excited to have you on the show. Thank you for your time. Ifeanyi - Thank you so much for having me today. I really, really appreciate this time. Looking forward to more of these conversations.Absolutely by now. So can we confirm that the light is starting? Yes, it is. Okay. Soif I only thank you so much. I appreciate the time you took. Previous Next

Learn how Noelle Jones-Ramzy advanced in tech leadership using entrepreneurial thinking, critical skills, and AI-driven support strategies—success without a degree. Leadership, Growth, and Success Without a Degree A Conversation with Noelle Jones Ranzy Welcome to the experience dialogue. In these interactions, we pick a hot topic that doesn't really have a straightforward answer. We then bring in speakers who've been there, seen this, but have approached this in very different ways. This is a space for healthy disagreements and discussions, but in a very respectful way. Just by the nature of how we have conceived this, you will see passionate voices of opinions, having a dialogue, and thereby even interrupting each other or finishing each other's sentences. At the end of the dialogue, I just want to make sure our audience leave with valuable insights and approaches that you can take it to your workplace. And of course, continue the discourse in social media channels. What I wanted to have is there is so many other events and stuff that we will be having, and we will be actually putting that down in the comments section so that you can be engaged in. And welcome to our guest, Noelle. Noelle, thank you so much for being here. Thank you for having me. Noelle, a brief introduction of yourself would be awesome, both from a professional standpoint and a personal standpoint. Well, my name is Noelle Jones-Ramsey, and I am currently living in the East Bay Area by way of Arizona. I spent 20 years in Arizona. Before that, I spent 20 years in Utah. I like the sunshine. So the relationship brought me to California, which I love very much. And I have been in the corporate leadership space for probably 20 years now. I guess it's 2025, so about 20 years. And I am so excited to be able to just meet people like yourself that have the experience and the connections and the insights of the industry that I'm a part of and actually aspire to have the curiosity and aspire to be a part of. So I'm really looking forward to our discussion today. Yeah. Noelle, what's your job role right now? I currently work for a SaaS company as a director of customer support. Excellent. So you're always talking to customers. And I noticed that you pulled in, you know, in one of the previous interviews, you talked about hiring people and what kind of leader you want to be. And you mentioned, you know, you would like to just take the ball, hire people who like to take the ball and just run with it and come up with an entrepreneurial spirit, right? So what does it mean by entrepreneurial? You know, there are so many different backgrounds and levels of experience that I get to talk to when I'm hiring for a role, specifically leadership roles, just because of the position that I'm in. And I really look for people who talk about, you know, I like to ask things about ambiguity and how, how you, how scrappy are you when you don't have the resources or the support that you need to be successful? And when I hear things like, well, I just go to my leader and I tell them that I need help, which is fine. That's what your leader is for. But also how creative are you in those roles? What, what in your entrepreneurial mind comes up to say, if this was my business, this is how I would solve this problem. And those are really things that I, that I look for. I never stayed in my lane, which is why I think I've gotten to the role that I've gotten. And I love it when people just say, hey, I have an idea and it's, it's, it's somewhere completely different from support. Do you mind if I float this by, by you? And I get so excited about people like that. Yeah. And in a way, support itself is such a very creative role because you are never going to face the same situation because of the human element you are dealing with. So every time you're trying to be creative to address different things for the different personalities, even if it is the same kind of technical issue. So I totally get that. And what you're also talking about is that creative thinking that people have to have and critical thinking, I should say, critical thinking that people have to have. So I love that. And another thing that was very interesting for you to have as a guest here is normally in the Bay Area, especially I see people constantly, the Stanford, Google crowd, et cetera. But here you are, you have actually been a very successful executive after getting a GED. And right now you have enrolled in college and you are making honor rolls while you are preparing to weddings. Yeah. So tell us a little bit more about, you know, how did you get here with the GED and then now your journey? I think it's two different questions, but yeah. Yeah. You know, thank you for the acknowledgement and the reminder that I should be going gray and a little nervous right now. You know, I got my GED in 2016 and I was already in a leadership role. I was a manager, a site manager for a contact center. And I wanted to go to Grainger, which is a Fortune 500 company, been around for almost a hundred years. And they have this great reputation and they headhunted me. But I needed some kind of, you know, at least a high school diploma. So as an adult, I was like, oh man, I haven't been in school for so long. And I did, I just, I went, I studied, I got my GED within 30 days and I got hired on at Grainger. And that's when I really thought, you know, I, if people are seeking me out, I could probably go really, really far in my career if I actually had, you know, classical education. But I kept being promoted and I remained successful. And I thought, I don't even, I don't need a degree. And I had these amazing mentors along the way in different places, the VPs and SVPs and one CEO that has amazing TED talk that I watch probably once a month on YouTube. And I remember them saying, you really don't need that degree, but I'll be 50 this year. And it's something, thank you. I appreciate that. It's something that I said to myself, you know what, this is just something that I want to do for me. I've made it to my direct role. You know, hopefully the next stop is, is senior director and VP, and maybe the sky's the limit. But I would feel more accomplished and I would feel better if I got a degree. And my intention is to go all the way, hey, I would love to have a PhD, even if I'm 60 by the time I get it. But in April of 2026, I will have my bachelor's in industrial organizational psychology. So I'm pretty excited about that. Yay, that's wonderful. So just so you know, I have a very good friend. I hope she's watching this, vice president of a very large tech company. She quit her job and went to do pre-med credentials when she was 56. So and then she wants to do medicine. And she has been like me in the tech career for over 30 years. So, you know, anything is possible. It's just a number. So when you're proving that, so that's really awesome. It's a little tough question, but one of the things that you mentioned in terms of your career and differences across the various industries that you have played in, you mentioned that tech has its nuances, right? And because you're here, you are as a person who says, I don't have to be the best person in the room and always bring in the tech person who can talk about tech while you are taking care of that executive presence. But tech always prefers people showing that expertise, number one. Number two, tech also prefers one being technical. So the question is, what did you mean by nuances? That's one. Second is, are you, you know, is that insecurity? Is that imposter syndrome? What is it that makes you think that you're not technical enough? Reality. So I'll say this. I have had, I have felt as a woman of color specifically, you know, living in primarily very conservative states, Utah and Arizona, navigating the corporate world, which is predominantly run by, you know, white men, right? So I have had to become very savvy and have a quiet confidence about myself. Now, obviously, I'm not your conventional suit. You know, you see that I have tattoos and I'm very, very authentically myself when I show up in these spaces. So I have had to learn whether the industry, you know, I've been in water, supply chain, healthcare, whatever industry it is, I have to build relationships. And that is what, that is my superpower. And that's what makes me a good leader is my ability to build relationships. Therefore, I have mentors. I have people that take an interest in me because they decide what they're, there's, there's something there. And I've been fortunate enough to have those people around me to say, I'm going to give you this opportunity. In tech, they do ask you to have, you know, SQL, Agile, you know, have this certificate, have that certificate. And I just wandered my happy self into tech and just thought, I'm just going to show you guys how to do this stuff. And I have, you know, my technical ability is have you turn it off and turn it back on again, right? And that's why, you know, like you mentioned, I had mentioned before, I hire, I don't want to be the smartest person in the room. I do hire people. So while I have battled with imposter syndrome in the past, the fact is I just do not feel that I am technically enough. I'm just technically enough to be dangerous or to carry on a conversation or to be customer facing and inspire confidence and, you know, create that space for, hey, you tell me what you need, tell me what you want, and I'll be able to make it happen for you. But I'm not going to do it. So the way I've navigated the tech space is just by being a very, very strong leader. And I can lead, you know, I've led individual contributors. I now have been a leader of leaders for the past 10 years. And if I can inspire them to do things that I need them to do and hire appropriately, I remain successful. And that's how I've been able to grow in my career. Yeah, so one of the first advices that I got as a manager, this was in the early 2000s, is find somebody else who can do your job better. So you will always find something better to do. That is the best advice that I've gotten. So what you're saying totally resonates with me. But I'm still wondering, how did you navigate that tech career? And is there any advice that you would give for others? When there's so much expectation on tech? Yeah, so I remain curious, as curious as I possibly can be. I dig into cases. I do a lot of, you know, observations. I look at the workflows. I want to see what my people are doing so that I can better understand. And that's what gains me a little credibility, right? So as a leader, if you're a strong leader, you can go into any space. But you might have that respect. But do you have the credibility of, well, does she really know what she's talking about? So coming in green into a tech space, first, my focus was the people. And then once I jumped in with both feet, it really was, let me just tag along. Let me just shadow. Let me just ask questions. And I went to sales. And I went to engineers. And I went to all of my account managers and my customer success teams. And I just observed. And it's a really kind of crude or rudimentary way to be introduced to the tech space. But that is really what gave me a leg up. And right now, our company is providing certifications. And I'm raising my hand. Put me in coach. Because I think with kind of the hands-on and the observation and now the technical testing and assessment piece, I think I'll be in a much better state. You should actually write a blog. I would love to see you write a blog about, here are the things that I learned when I moved to the tech industry. And the lingos and the acronyms and everything that we try and that no other industry thrives in. So yeah. The last question. There are so many mentors who came for you when you talked about them and how they kind of came in at the right step during your career and enable you to rise up or learn or be your sponsor or whatever, right? So what would you do back for the community to be a mentor? Oh, you know, it's something that I do now. Previous Next

Discover how to transform your customer service into a profit center. Learn about the CX Maturity Model and strategies for making support proactive and revenue-generating. Gain insights on tailoring your pitch to different decision-makers and measuring ROI. Turning Customer Service into Profit Centre | Transcription Kay - Welcome to the experience dialogue. In these interactions. We pick a Hot Topic. That doesn't really have a straightforward answer. We then bring in speakers who have been there and seen this but approached it in very different ways. This is a space for healthy disagreements and discussions but in a respectful way. By the nature, of how we have conceived, this, you will see the passionate voice of opinions. Friends having a dialogue and thereby even interrupting each other or finishing each other's sentences. At the end of each dialogue, we want our audience to leave with valuable insights and approaches that you can try at your workplace and continue the discourse on social media channels. A little bit about Ascendo, it is addressing optimization of support to operations within enterprises so that they can serve their customers better. We enable enterprises to optimize workflow for the agents and provide dashboards for insights on risk, churn analysis, and visibility for senior managers. We are revolutionalizing support ops in the same way DevOps and RevOps have transformed other areas of the business. In the last three years, we have created a G2 category and are ranked #1 in user satisfaction. We are very proud to be loved by our users, and now with the topic Turning Customer Service to a Profit Center. For most companies, customer service is still viewed as a cost center and with the increasing and ever-changing customer demands, this perception is further strengthened. However, when utilized correctly, customer service could be one of the biggest revenue generators in your entire organization. Now it’s a pleasure to introduce the speaker, Jonathan Shroyer, Chief Customer Experience Innovation Officer at Arise Virtual Solutions Inc. Arise .has been following a customer experience maturity model that can help you turn your cost-center service team into a profit center. We will be talking about each phase of the maturity model and how to make a support center proactive and profit-oriented. Jonathan, a pleasure to chat with you. Thanks for the opportunity to come on the show. Jonathan - It's really great to be kind of in the catalog of great shows that you have and has the opportunity to share a little bit more about the views of turning customer service into a profit center, so I appreciate the opportunity, Kay. Kay - And, what is interesting about your background, Jonathan, is. You've done various types of companies, from security to, you know, office applications to Autodesk and Kabam fully in the gaming space and now you are in the gaming space. So, it's fabulous to see the entire customer success and how it has been, it's rare to see people who have had decades of customer success experience.so it's wonderful to have you at the show. Jonathan - Thank you. Thanks for the opportunity. Kay - So let's start right there. Actually, since having decades of experience, Jonathan, tell me a little bit about how you have seen customer service change over time. Jonathan - Well, I think it's interesting. So when we go back to the 1980s, there was this very kind of brick-and-mortar viewpoint to services. It was in the early times when we started to see the kind of technology morph to where contact centers or call centers, started to come about. Then you go into the nineties and that that infrastructure at the the kind of the boon of the internet at the end of the nineties enabled. That type of capability to be serviced outside of the local country, whatever the local country was, right? I was in the United States and they were fast, quickly. The 2010s in it and, and we went from, Hey, we can contact to, all of a sudden there's text, there's email, there's chat, there's Facebook, etc. I mean, there are all these different ways that customers can access brands, and what changed at that moment was this concept where brands now could hear from their customers more often, understand their customers more and the customers had a lot more information and data to make better choices, and they started to make different choices based of how brands started to treat them and then as we fast forward, you know, up through the 2010s into now, into the 2020s, we're noticing that customers are making choices based off of how brands treat them in their customer experience, and they're making their choices with their feet and with their money. So it's been an interesting transition of where, and how consumers and customers make their business and their brand choices based on how they're treated. Kay - it's fascinating that you brought in the data, and evolution along with the customer service evaluation when you answered this, Jonathan. So one of the things we say is metrics are good, but metrics don't say a story, data does. Do you actually see the way brands are relating to customers and the amount of data that they are using more and more and, how do you see the data transition also over the years? Jonathan - Well, I think what's interesting is, data is like super important to companies. I think consumers don't always think about it in as in that valuable sense, right? They think of, oh, I'm not a member. You know, I'm a person, treat me individually. Customize it for me. But the reality is, for companies to be able to deliver that optimum service, that best-in-class service that drives stickiness, loyalty, and so forth, they have to have the data to understand, what the customers are doing with their product inside their product and so forth. And so I think that over the years, one of the things, you see happen in the early 2010s, this idea of a unique identifier that could identify a customer across an entire company or entire product suite, and the reason why a unique identifier was created partially was for security reasons, to protect the customer, protect the client, the business and so forth. But the other component of it, which people didn't realize at the time, was and enable the company to be able to correlate. Data between different parts of the company. So for example, it allows this correlation between customer experience and profitability as an example, right? Or like adoption and usage and different product suites or different product features that come out. A company can now say, feature X, and based on what all these key clients are telling, Based on using it and loving it or not loving I, that tells us whether the product feature was a good product feature or not, in addition to, anecdotal feedback from the market or from the individual customer. So I think, the transition and maturation of going from very lean to data in the eighties to big data in the 2020s have been just as important in this framework of creating profitability through customer experience as it has. As, the actual activities or the processes that a customer experience team does to drive that profit. Kay - Arise has been actually following a CX maturity model and I would love to understand how you see that model will make support, proactive and how do you see that transition from cost center to profit Center. I know that's two different questions, if you wanna split it and answer, that's fine too. Jonathan - So to give a little context, I invented the maturity model called the Service Tech Maturity Model at the time and trademarked it, but the idea was when I started Officium Labs, I had been working in large enterprises in startups and gaming companies for some time and what I realized was that there wasn't like a simple, easy-to-use framework or model that you could go and join a company and all of a sudden look at like, based of this model and this framework, I have all the features necessary to create profit and to communicate profit, to the power cores of the company in a way, they understood it. And so what I thought I would do is, I said, what if we treated customer experiences if it's a product? So if you create a product, what you tend to do is, create a framework of what the product is going to be. And inside that product framework, you have pillars that we'll call major feature sets. For each of them in your feature sets, you have feature components that build that feature area, right? So for example, if you're building a Microsoft Office product? They have seven or eight different types of feature sets, and then they're like, well, we wanna add this, or we wanna add that, and the same thing with the video game, the same thing with any type of product, right? So I said, let's build a customer experience product or let's at least think that way, right? So change the mindset and say, what are the pillars that we need to have inside of this product? And then what are the features inside the pillars? So for example, workforce management, learning, and development, interactions, products, those types of things, right? Quality, operations, those types of kind of pillars. Okay, so those are the pillars. And then what are the features that need to exist? And, so we build them out and we said, okay, these are the basic feature sets if you wanna run the basic operation. So, when we use that word, basic was a, had a different meaning than it does today. Basic has a very different meaning, The urban definition of it and whatnot. But generally speaking, the next one was, what's the standard look like? What's the next feature set? And then what does best in class look like? And then, you know, what is the next generation look like? So we build it that way for the users, but the idea was that if you build a customer experience with the product mindset and you have these features, then you can help companies understand, where are they at in the maturity model or where are they at in the customer experience product to be able to mature to a state where they can deliver a profit? And the outcome is if you're in the best, if you have the best in class features or the next generation of features of the maturity model, we can 100% show you how to correlate the work you're doing in customer experience. Kay - I love it because suddenly when we treat it like a product, what happens is, it enables the entire company to become a customer-oriented, customer-obsessed, customer-focused company, right? So it's easy for all the teams within the company, within the organization to tune in to the customer very easily because then there is a framework that enables a company to tune to that and say, okay, marketing, this is what I need to do. Sales, this is what I need to do, product, this is what I need to do. So I love that. So, can you summarize the eight pillars, and then we can dive into at least a few of them? Jonathan - them? Yeah, for sure. I mean, so when we look at the pillars, You, you've got, uh, the interactions and interactions really include all of your technology, so it's the non-human facing interaction component, like everything that sits behind that makes the interaction possible, whether it's AI, technology, CRM and so forth, then you have kind of the operations piece, and this is really about all the different processes and how to Interact and engage, you know, with that human being on the other side. Do you use vendors? What are your frameworks of success, your methodologies, and those types of pieces? Then you have the quality piece, which really looks at the overall experience and what type of quality experience you're delivering, whether you look at quality from customer effort, customer satisfaction, NPS, whatever it is, you know, it has that piece. Then you're looking into the learning and development piece, which is how are you training, onboarding, and equipping your people to be able to talk to the customers, engage with the customers, and then be proficient. In that piece, the next one you have is, we call it content management, but it's really looking at your knowledge base, the knowledge that's out there, whether it's through self-service or you know, whether it's through using social channels like Discord or other components and pieces. The next one looks at the product itself, which is like we, we firmly believe that when you design an experience for a customer, you design it at the development table. You don’t. The product's launched service go, make the customers happy like that's old school thinking. So inside of it, we have this whole concept of product liaisons that need to sit as in the experience design piece, design it, and develop it and then you go to beta or alpha, then you go to beta, and then you launch it, right? So there are some key components overall. And then, and then we, then we kind of then look at, you know, what's the rest of, you know, the tech stack that looks that supports the organization, that doesn't really have to do with it. The interactions themselves, cuz there are a lot of techs inside of the experience side, right? And then you look at kind of the pillar with looks at the customer journey, looks at the journey mapping and all of those different components and pieces, so really kind of, it sets up these different overall pillars that which is a little bit more complex for someone that's trying to follow along to what I'm saying now. so we can share a visual of it later. But the most important thing is, what are the features that you have today? So how do you assess against that? And then what are the key features that you want to add over the next 12 months? And so every company's gonna be a little bit different in that, in that component. Some companies wanna invest in interactions or AI, some companies don't have a quality program or WFM program, and they're gonna wanna invest in those things first, right? And so it's gonna be different by company, but the goal is how do I create ROI? How do I create profit? And then once you have the unique identifier set up and you have the correlation capabilities. The maturity model is then doing A/B testing and proving out, what correlative values, and a correlative profit look like for your business, because it's gonna be different for gaming, which is the hat that I'm wearing, right? We have gaming clients, healthcare clients, finance clients, and clients all over the world. Tech clients, it's gonna be a little bit different, like how you drive profitability or correlation of profitability, in what tests you need to do and so forth, so that's doing the assessment, understanding the model, but then actually putting the model into the application and driving it as a business strategy. A business transformation is just as powerful and having the mathematical data to verify and validate the work that you're doing has an impact, that's kind of the soup to nuts. Yeah. Kay - So what is interesting for me is when if you look, you know the model that you're talking about, if you take the interactions, right? So the interactions are actual ones that is there in the system. The second one you talked about is the activity that is happening with the customer and if I tie in with the unique identifier, what are the existing regards with this unique identifier? I know. What are the interactions or the activity that are happening in any channel, that's happening with the customer? And then I can start correlating saying, how is this Piece of interaction that's happening right now with the customer, with quality, with learning and development, with the product and all of that and suddenly you have a nice flushed-out full interaction where you have the intelligence built-in and then you take, you know, thousands or 10 thousands of several of those interactions and then look at the patterns and the anomaly of those interactions, it becomes a beautiful, intelligent player that's more customer first. Did I give the same view of what you mentioned, but more from a data perspective, but does it? Jonathan - Yeah, I mean it totally does and I think the reason why I create the maturity model is cuz what I found is that there's a big disconnect between how people at companies that make funding decisions and the services team themselves and so it was a big frustration and a big tension between the services teams and the power cores as it were at a company that is making these investment decisions and so what I thought was important, how can we talk in a language that will help the power cores in the company understand the importance of investing in the customer experience side? Right? And that's where the money comes down to like that was the simple question. I'm a big believer that simple questions lead to the next generation of innovation. And so that was the simple question, asked me and then I was like, okay, well in order to do that, it's this, and it's the maturity model and the application of the transformation but the most important piece is, then how are you communicating back to the power cores of the company. The impact, and that's what, where one of the biggest things that the power cores have to get over is investing in the unique identifier, because most companies, they'd be like, we've been over for 30 years. A unique identifier across 45 databases is gonna be really hard. Yeah, it's gonna be really hard, but it could also drive 10 to 20 to 30 million of future revenue for you as well and so helping them understand and correlate is super powerful and then demonstrates the impact of like, hey, you gave us a million dollars. We protected, or we created 4 million or 5 million of revenue based on that a million funding that you gave us, and that conversation is just as powerful and important to the overall success of the services team, creating a profit center and communicating then the framework itself too and so I think that's important to note. Kay - I love, how you mentioned the power cores, who make the decisions, so let's talk a little bit about the various people who are making the decisions for this funding and the kind of roles and how it differs from your experience. Jonathan - Well, it's super interesting. So I think there's like four or five cores inside of a company and in any company, it's never the same group of people that have the actual power versus the perceived decision-making and authority and so you look at IT, you look at finance, you look at marketing, you look at sales, and then you kind of look at the executive team and product, so those are kind of the six-ish power cores and in some companies, product drives everything. Like product is the power core, right? And you see this a lot in startup companies. You see this in companies, that are more tech, you know, industrial companies, but then you go into other companies like financial or healthcare companies that have been around for 30 years and I find that oftentimes finance and legal are the power cores in the companies, which is super interesting to me cause it's different than, a product power core company or a finance power core company. I think the most important is to understand here, doing an analysis on who actually makes the decisions in your company, and then what's the decision process that they go through to make decisions, and then how can you speak in that language? And I think that's the most important thing to think about. Like, I was with a gaming client two years back and it was clear that finance was like a power core in their company and so the way that I pitched the maturity model and the value that it could provide was a little bit different than a gaming company where tech was the power core. Right. And so it was much easier to get tech to do the single identifier than finance, and so you had to talk in a different language. Like as example, tech was like, yeah, it makes sense, we should do it. Then we were ready to go and they're like, yeah, let's do BDI, Big Data, and let's do this and they were ready to invest much faster, whereas a finance company or finance power core was like, well actually, what's the ROI? Take me through these five presentations to convince me and prove to me, and then I wanna follow up every week afterward so in one way is not bad or, or right or wrong, but they're just different and so it's important that as you start to think about, how do I talk about profitability. In the customer experience area, you just have to know who your audience is and what's important to them, and then how you can frame the language to help them get value out of it. Kay - there are a lot of people watching this, who are from the support background, and one of the biggest things that they are asking is, how do I take this argument to the decision makers? It's changing a little bit with the chief customer offices themselves making those decisions, but they have to work with the rest of the company, so I think what would be wonderful for this audience is let's take it through these examples that you talked about. Let's talk about stories that are wonderful, Jonathan. For example, this finance, and how did you do it for the finance, with the CX maturity model? Jonathan - Well, I find the easiest way to start with any power core is to start with, its magical word, which I love, it's called a pilot and most power cores are willing to take risks on a pilot, but they might not be able to be willing to take risks enterprise-wide, right? or company-wide and so what I end to find is it's really important for the customer service team to identify a group of customers, whether it's a product, whether it's a delineation of customers inside of a product or, whatever it is. Find a group of customers, you believe, as you have a hypothesis like, Hey, this group of customers will definitely be able to demonstrate a profit. We have a hypothesis in video gaming as an example, a mobile game company that we worked with, the top 2% of their customers generated 80% of their revenue, right? And so for us, let’s focus on doing a pilot for the top 2% of these customers, let's identify, what we're going to change for this 2% of the customers versus the rest of the customer base. You can call that an offering, right? What's the pilot offering going to be? What are the pro processes, the methodologies, and the policies that we need to change for this pilot, and then what's the data that needs to be recorded or adjusted in order for us to be able to demonstrate whether the pilot was a success or not and then, what's the key success measures of that? So you build all that and then you take that presentational proposal to the power core. In this case, it was the finance team and then for the finance team, and it was really important for them to understand how this impacts the bottom line. So we built the entire presentation to do all of that, but then also talk about, like this is going to be an investment of X, but it's gonna deliver an ROI of Y and we're gonna be able to get it. Know that the ROI was delivered within eight weeks or 10 weeks, right? You give them a timeframe. And then you set up the meeting to talk about this, the success or the non, the findings of the pilot, and then from there, you know, the findings statistically significant enough to scale it, or do you need to elongate the pilot for another six weeks or eight weeks to get that statistic significant? We presented to them and so forth, and in this case, we were able to present it. For the pilot, we scaled it enterprise-wide based on the fact that they saw the ROI, they saw stickiness and retention numbers. They saw the stickiness and increased revenue attribution and so we were able to do it right, but that's the most important thing is to start with the pilot experiment, have a hypothesis, prove your hypothesis, and make sure you think through all of it, the processes, the policies, the methodologies, the data structures, the technologies, all that build up a bit, right? Put it together and then proposes that and then make sure you have your finance number of what you need for funding versus what the output is going to be and then you can attribute what the success of the pilot was or not. Kay - That's, you know, essentially, a full framework for the proof of concept, right? So that's essentially right from the planning all the way to the measurement, the proof of concept too, so in the end, there are no questions about, was this even successful? It is very clear what that success metric would be, so in the end, the decision-makers can participate in saying, yes, this made sense. Now how do I, we go and deploy and in what stages do we deploy? That's very good. Would you like to add anything else with respect to the framework, if it was a different decision-maker? Jonathan - Well, I mean, I think that like, if you're talking to like a technology or product power core, they're interested in the ROI, but they're also interested in what could we change in the product in addition to what you're doing in an experiment in the service? It's like, maybe like after the beta test, we like that, we'll just build it into the product, right? And maybe we'll just become part of the product and it could drive that attribution, so they just, they just have it. It's more of a like, Hey, how can we use this next? Yeah, give us data online, but then let's build in the product so we can scale it and it's not a manual process and then they tend to think, you're a creator as well. I know that you think this way too awesome, but how do we build the product so we don't need so much manual intervention? We retain the customers earlier in the cycle versus later in the cycle, right? So a product like something like that, somebody that's in, from a legal standpoint, but are we getting away? Is there any risk or, like, are we gonna lose all these other customers because we're doing this thing for this customer? or what about the data? Like, is this data, is it gonna be GDPR? Is it those types of things? Right. You have to go through all that for power core, but those are kind of the questions that they'll want to have the answers to. Right? If you talk to an executive, maybe an executive that's more high level, they'll be like, I don't even know about anything. All I wanna know about is what's the service metric, what's the ROI metric and gimme a weekly update. Right? So it will just depend on who the power core is and how invested or interested they are and how you make the sausage versus the sausage being made for lack of a better metaphor versus the sausage, how well it tastes, and how much customers love the sausage and all that jazz. Kay - Yeah. So, essentially in that group of concept, the ROI is the metric for the finance person, and that metric changes if it is a product person, marketing or sales person, or something else, that's kind of how the metrics tie back to the profitability of it, so at this point, you don't know anything about your customer experience of the product, but here it is, when you tie all of this together, you have real-time feedback on how customers view the product and what changes you need to make to the product and that is the driving force for turning it into a profit center. So, the CCO drives who the decision-maker is, and what is the metric that's going to resonate with that decision-maker? Jonathan - I think that as you do that, create that decision maker and help them become a sponsor and that will be the next powerful thing, you have these conversations cuz if you have a sponsor, you're good, that's outside of CS. If you can get an executive sponsor that has influence in the company, then we're not only gonna be able to try this first pilot, you're gonna be able to do other experiments. There's, a Nelson Mandela quote I love, “I never lose, I only win or learn.” that's a very iterative way of thinking, which is like, either I won or I learned something and now I'm gonna go try something new and I'm gonna win at that which tends to be more stereotypical of a product or, or a tech power core, but in essence, I think any company that wants to be successful, they have to try this. Long ago, the waterfall was the way that we did project management. Hopefully, that's gone because it doesn't allow for iteration. It assumes that you're having, this is the product and we're delivering the product. Let's see what customers think. You know, it's a very risky way to do it in my viewpoint, but it's an iterative way and I think that if you can get a sponsor and they'll be okay, what's the project that we're gonna do? what's the next experiment that we're gonna do to drive stickiness and retention? and then what you'll find is once you prove that pilot out, you'll get another PowerPoint. Be like, wait a second. Like how are you doing that? Oh, wait a minute. I wanna know how can you help me think about that for marketing. Or how can you help me think about that for sales? You know, how do we drive that same mentality? Maybe the framework is different, but in the customer journey, how do we drive a mentality of experience, design, and retention proactively rather than just reactively? so then you look, then you start to give this interest across the entire customer journey. Kay - Yeah. I know we are running out of time. I wanna squeeze just one little thing into the conversation. How do you create urgency? The easiest is to create that urgency to make this happen. Jonathan - You show the amount of money that is being lost because of it, like that's the other important piece is like you when you look at the industry data and you know, I have industry data, I could share that. Maybe each particular customer experience person has their own data, but essentially I always like to do an analysis, a simple analysis to do this is all open source, right? Give it away. So, but a simple analysis, what is the revenue generation of customers that never contact support versus the revenue generation of customers that do contact support, do that analysis and full stop. 99% of the time, what you'll find is that customer service or customers that contact support is stickier. They drive greater revenue, and they have more of an impact on your future and so that revenue difference is saying, hey, all these customers that never contact support, they just left. They don't have a good experience, they don't care about the brand, and they don't care about the product. That's money that's on the table, that's lost. That's how you create a sense of urgency. Usually, in large companies, that's millions of dollars. And that starts to speak. Kay - people. Yeah and then you can start showing urgency on what are the brief thoughts, right? So then you need to get to it now, to make it important and I like that metric. Anything else that you would like to add that add to it? Jonathan - I mean, I think that as you think about it from a high-level standpoint, I always think about two words. How are you protecting your customer, but how are you optimizing the experience? And so that's, that's those two words are kind of what helped us build the maturity model because different pillars in the maturity will protect the experience. Different features and other features will optimize the experience. I think as an example, like, protecting the experiences is really understanding who the customers are, and how they want to be engaged. Meeting them where they are, as an example versus where you want them to be, but optimizing is looking at, how does AI help my customer have a better experience versus human-to-human engagement? Encourage better experience. so those are just very simple examples of how you protect and optimize. But as we built out the framework and we built out the maturity model, that's how we thought. Kay - Excellent! Protecting the customer versus optimizing the customer's understanding, how to create the urgency around, how many you know, revenue generated with customers who reach out to support versus not, and then building out that, a framework for the proof concept to make it a reality and then driving from the proof of concept into the entire deployment to make this a customer-wide phenomenon, so thank you very, very, very much, Jonathan. I think you have given a full framework. It feels like there is a lot we covered in a short time and it feels like there's still more we will certainly bring you in for a further conversation, but I really appreciate the time that you took today. Thank you. Jonathan - No, thanks for the opportunity. If anyone has any questions about it, they can reach out to me on LinkedIn or you can find me. I'm a Chief CX Officer on TikTok on the instant, a variety of other places, and YouTube. So, thank you so much, Kay for the time, to share my thoughts and add to all the wisdom that you know, that your podcast and your life have. Thanks for the opportunity. . Previous Next

Uncover strategies for leveraging AI to drive ROI in customer support—tailored for Heads of Support aiming to optimize performance and efficiency Maximizing ROI with AI in Customer Support Team A Guide for Heads of Support | Transcription Good morning, Anita! It's a pleasure to have you here today. Thank you, Kagan, for the introduction. Anita brings extensive experience as an executive and independent board member in many technology companies, with a particular knack for maximizing return on investment. It's fantastic to have someone with such a background, having been associated with Vio, Flex, Texas Instruments, and currently serving on the boards of Power Integrations, Exro, and Svako. Her expertise, coupled with a finance major from Waton and a computer science background from Virginia Tech, will undoubtedly enrich our discussion. Anita, I was thinking we could approach today's discussion from the perspective of a head of support evaluating AI tools and working with vendors to identify the best fit for our team. Additionally, we'll delve into how to present these findings to our executive team to secure funding and ensure success. Does that sound good to you? Absolutely, sounds excellent. Anything else you'd like to add before we dive into the slides? No, let's get started. This is a critical coaching moment, and it's crucial for people considering adopting new technology to view it holistically and understand the concerns of all stakeholders. Exactly. Our audience primarily consists of heads of support in medium to large enterprises, so our discussion will cater to their needs. Are the slides being shared now? Perfect, let's proceed. Today, we'll discuss why ROI on AI is crucial, navigating internal dynamics, expectations from vendors, and positioning the AI tool to senior-level executives to garner support for the project. We have a lot to cover, so let's see how far we get. Anita, anything to add? No, this agenda looks good. Let's dive into it. It's essential to address the skepticism surrounding ROI on AI, especially considering past projects' unclear results and use case ambiguity. Absolutely. As a support leader, it's vital to address this skepticism first. Do you have any suggestions on how to begin? Each organization has its risk tolerance and regulatory concerns. Understanding these factors and quantifying potential risks and costs are crucial first steps. So, essentially, I should start by identifying and quantifying risks and costs by consulting various stakeholders, including those knowledgeable about regulatory issues, security risks, and industry-specific challenges. Exactly. Once we've addressed these concerns, we can move on to identifying specific use cases and their potential ROI. Right. And then, I can work with vendors to calculate the ROI for each use case and present this data to our CFO. What should I keep in mind when presenting this to the CFO? Be transparent about costs, risks, and realistic timelines for ROI. Be prepared to answer questions and be open to multiple discussions. That makes sense. Once the project is underway, how do I ensure continued buy-in from stakeholders and the CFO? Transparency is key. Provide regular updates, be honest about progress, and be prepared to address any unexpected challenges constructively. So, maintaining a transparent and constructive relationship with the vendor and stakeholders is crucial for success, even when things don't go according to plan. Absolutely. Building strong relationships and handling challenges effectively will ultimately lead to success for everyone involved. Thank you for your insights, Anita. I believe our listeners will find this discussion valuable, and we look forward to exploring their questions in our next session. I'm happy to help. Thanks for having me, and I look forward to our next discussion. Previous Next

Explore how large language models (LLMs) like ChatGPT can be used to improve customer support. Ramki, co-founder and CTO of Ascendo, discusses the limitations of ChatGPT, which include lack of real-time data and factual accuracy. He contrasts this with Ascendo's LLM, which is trained on enterprise-specific data and leverages human feedback for continuous improvement. ChatGPT and the future of Customer Support | Transcription Kay - Welcome to the experience dialogue. In these interactions. We pick a Hot Topic. That doesn't really have a straightforward answer. We then bring in speakers who have been there and seen this but approached it in very different ways. This is a space for healthy disagreements and discussions but in a respectful way. By the nature, of how we have conceived, this, you will see the passionate voice of opinions. Friends having a dialogue and thereby even interrupting each other or finishing each other's sentences. At the end of each dialogue, we want our audience to leave with valuable insights and approaches that you can try at your workplace and continue the discourse on social media channels. A little bit about Ascendo, it is addressing optimization of support to operations within enterprises so that they can serve their customers better. We enable enterprises to optimize workflow for the agents and provide dashboards for insights on risk, churn analysis, and visibility for senior managers. We are revolutionalizing support ops in the same way DevOps and RevOps have transformed other areas of the business. In the last three years, we have created a G2 category and are ranked #1 in user satisfaction. We are very proud to be loved by our users, and now with the topic ChatGPT and the future of customer support. There is excitement on many Tech and business channels on ChatGPT from OpenAI. It had a lot of adoption within the first five days of its getting released. We've been following OpenAI and GPT 3 for some time. We will discuss the technology, explore its impact on customer support experience space, it's possible limitations and opportunities. So join us and bring in your questions to LinkedIn and Slack channels. Now it is a pleasure to introduce the speaker. He is the co-founder and CTO of Ascendo.AI. Ramki comes in with deep data science, and support background.He ran managed services for Oracle Cloud, created a proactive support platform for NetApp's, multimillion-dollar business and is respected for both his mathematical and business thinking and data science. At Ascendo, his mission is to give meaning to each and every customer interaction and elevate the experience of customers and support agents. Welcome, Ramki. Ramki - Thank you Kay, Glad to be here. Kay - So we can start with the basics Ramki. What is actually ChatGPT? Ramki - You know, I create a slide that kind of shows what ChatGPT could be, you know, and I know that it kind of comes from the comic strip but now let's talk about what ChatGPT is. It's essentially a modern variation of a chatbot. We all know and we've been living with chat bots. Typically the chat bots require you to set up the rules, based on a question that the person might ask it basically has rules that match the contents, and the whole thing happens in a coordinated way. The ChatGPT, difference is that instead of only knowing a little bit of whatever it is, for the website that you are on. Essentially, it's kind of a robot ChatGPT knows, just about everything, and it's more articulate than the average human. So it's kind of comes up with- Hey, I've consumed all the internet and I can provide answers in a conversational way. Let's talk about the technicality of it. It's essentially a language model. It's been trained to interact in a conversational way, it's a sibling model to the instructGPT which was trained to follow an instruction on prompt and provide a detailed response. What I mean by that is, essentially it remembers the thread of your dialogue and using the previous questions and answers to inform, what the next responses could be. The answers are essentially derived from the volume of data that got trained on which is what we had on the Internet. So that's kind of the technical answer for it. You can think of it as it understands the conversation, it consumed all of the internet. It knows the history of your dialogue and it can prompt automatically what the next language sentence could be. Kay - So we've been following ChatGPT, GPT3 for quite some time, right? There was GPT 3, and then now ChatGPT, tell me the difference, please. Ramki - Yeah,I said it's a language model, right? So underneath this nothing but it's using the GPT. Its GPT is the Transformer model. What it means is it's predicting what the next words would be based on what it is seeing. The difference is GPT3 uses 175 billion parameters in whereas to instructGPT which is kind of a 1.3 billion parameters. You can look at it as a hundred times fewer and it's still performing quite well because it's the way it got trained but the same time You know, everybody knows that excitement is great but OpenAPI warns, you know, Hey not all the time, the answer may be correct. So you got to be watchful of what you're seeing and you have to inculcate what it is saying and then see in your own form, whether it makes sense or not. Kay - So there are a lot of people who are new to data science, also here Ramki. and when you talk about Transformer model, we are talking about transforming the learning from one to another or transforming the learning. Correct? Would you like to add any other definition for Transformer? Ramki - Transformer was the kind of technical term essentially it was done. You know, you can kind of look at all the words in One Sweep, and the training time is less. So you are essentially looking at the whole sentence or whole information and taking the mass in one set of tokens and understanding the relationship and then you can kind of predict what the next one would be. So it's a combination of doing the training faster and having fewer parameters and doing it with a lot of content and also making some kind of a model that really reinforces the better behavior of which is correct and guarded better. Kay - So a lot of people have interacted with ChatGPT. Right? It's a, you know, they ask a question and they type a piston in and use the content that gives a result and they give feedback and based on how it's trained. So, in a way, it's kind of Google but not Google also. So can you describe a little bit more? Ramki - It's gone, in a way that, we all go to Google and say, hey, I want to know something. Then we go search and we look at the results and we kind of look at them. What makes sense, and put our thoughts into it and make sense out of it, right? The most notable limitation that you're going to find is that this ChatGPT doesn't have access to the Internet. It's basically loaded with the entire content prior to 2021 data-wise. But it can not look at the current image. In fact, OpenAI tells you that. For example, I want to know when my tree train is going to lead, you cannot get it right but you can pretty much ask anything like, Hey, I want to write a poem. I have an issue with this code, does it make sense. Those types of things one can ask and it can be ask it to fix it. In fact, The very first day was out. One of the teammates asked, hey, I want to write a poem on Ascendo and it kind of actually did a pretty decent job. You know. Kay - I would love to see it at the end, so I was playing around with it, and I will share that also in a bit. So, now the adoption of ChatGPT has been pretty exponential, right? So we see millions of people using it. What Are some of the key differences that you would point out in terms of its output? Ramki - Recently, I was listening to several things, one of them being Steven Marsh. He recently, like, even last week, wrote an opinion column in the New York Times in. You also had a podcast before that with the intelligence quiet, a British podcast media. He's been using similar tech for some time. It's not like we just looked at this and said, yeah, you looked at a different variation not opening another company, and then you looked at them as well. He says it in a very succinct way, he says ChatGPT is a great product that he calls, it can provide a filler response by the filler response means it's not junk, it's not a trivia, it leverages on how people are taught to write essays in a structured manner, you know, we have an opening sentence, kind of things like that. The key point that he's bringing up, is the ChatGPT does not have an intention, it's not like an author, you know, I want to, I have a will I want to want to like what when you write an article you're thinking about the point that you want to convey, right? You want to say this, I want to be able to show that to you. I want you to know that, that's not what you're going to be getting. ChatGPT is a kind of a filler, but it gives you a starting point. One can use the starting point and add the rest of the information that are from your Vantage standpoint. We may be entering an era of doing things differently. Like when we started with the internet, right? When the internet came, then Google came, and then, you know, yeah I remember going to some places, where people essentially say, Hey the computer tells me, this is what this must be the truth, kind of thing, so the open source came all of them, right? So that's the same way here. We are going to be entering a different era where you may be asking, and it gives you some responses that use that as a starting point and go from there. Kay- So some could also say that a GPT3 is the base model and ChatGPT is the bot version or the conversational version of using GPT3. That's already indexed and modeled with internet data. As you mentioned, September 2021. Will that be a correct statement? Ramki- It’s kind of you know, Yes and No. I know the GPT is the base, ChatGPT is not using a bot version of GPT3. It's essentially a smaller model, right? It's created by fine-tuning GPT3. In other words leveraging what GPT does it has to offer a mix of its own bot kind to give this whole intelligent conversational experience.Does this make sense. Kay- Yeah, absolutely. So you know, we know RPA came in, right? So that was the first iteration of introducing AI and I Love to equate it to the autonomous driving experience which will also bring up in a sec. So the RPA came in and it became too much rules-based and very cumbersome to maintain, but RPA was very hard and then that got faded away. Then came chatbots, and I remember at one point, we were counting 318 ChatBot companies and they were the chat versions of the RPA, which again was very rule-based and you had to pretty much codify the question and answers and stuff. And they were very well used within the customer service context. So tell me a little bit more about the Bots in the customer service context. Ramki - You know you're right there. A lot of chatbots. In fact people think when we know when you say have a question they always think bot as one of the things but they have a lot of Baggage, you know, companies have tried with limited success to use them, instead, of humans to handle customer service work. There is potential in these bot where you can kind of alleviate the pressure on answering some mundane questions. But the thing is yesterday was like, you know, recently 1700 American sponsored by a company called Ujet whose technology is handling customer contacts. What they saw was very interesting. 72% of the people found Chatbot to be a waste of time that's a very serious thing. You know, the reason is the biggest challenge people want to have is they don't like the feel of having to work with the robot. When I talk to many of our customers you know when they get you to know, yes there is a potential for doing a lot of self-service self answering but the reality is as soon as you give the option to talk to somebody or something, they just click that, you know, that's what people want. The reason is they don't Like to work in a bot-like environment. Kay-They want a answer. Right? Ramki-Exactly. Kay - You know which is like, I'm having an interaction, why can't it be an answer? Why does it have to be a conversation with a machine-like thing which has to be maintained and codified extensively? and on top of it, I don't even want to go in and extend this process ultimately creating a ticket, right? So Yeah, elaborate. Ramki - If you look at the ChatGPT right, on the other hand. It sounds like a human, you know, and it is of one, what you are saying to form the response. It is not pre-coded with a response. It really thinks off what you're saying and that kinda makes the whole discussion and responses more conversational but doesn't make its responses always right. You know, again OpenAI says that. You have to look at the response and make your conclusion. Kay - You also talk about ChatGPT’s initial, audacious claim. So elaborate a little bit more on that. Ramki - I'm going to share one slide on this. You know, it's an interesting lie that you will get a chuck lot of it.In fact, I went and asked this to ChatGPT. Hey, tell me about the customer support kind of thing. So we ask this and first, you can you know, I just put the same response, what I got right on the slide. It first makes a very audacious claim. Hey, it is not capable of making a mistake so that's a big thing but at the same time, it also did admit that it cannot help with real-world tasks. So it's kind of that is essentially what I want the readers to understand. It will appear that it is not making a mistake, it's giving the answers. But at the same time, you have to know that, you know, it may not have the ability at least as of now, to provide customer support or the real-world task, you know, where's my training? What is the issue? Because in a real customer support scenario, things change more normal things that things are going to be relevant now, and it may not have all the answers. So that's where the big difference is, I would see. Kay - There is a question from Shree. He's asking, what is current state of art in ChatGPT integration with KnowledgeGraph enterprise solutions ? continues saying, particularly around Particularly around Explainability for conversational problem solving , in domains that have high compliance bars ( like healthcare or finance )? Ramki - You know you can't just Wing, you know if you look at it right when you and I are having a conversation we're going to use of the knowledge that we have gained and we are going to just tell you and there is no fact-checking. So we got to be conscious of that. So just because you get a response and in fact, the response may look somewhat legit and it doesn't mean that it is right? Especially when there is a complaint kind of a thing as a mod so I would strongly suggest it. In fact, you know about the openAI will in fact concur with this type of thing. You got to, you know, it's giving the answers based on what it had been trained on, but fit is for Real World past and something that you need to do, contact the customers and do contact that particular customer support and get the answers. It's, they're saying, so that's what ChatGPT itself with that, you know it is audacious to say, it will never make a mistake, but it doesn't make a mistake and it also tells you that you have to be at your own. Kay - Yeah, and it's good that the model actually understands its own limitations and claims its limitations, and it's by us too since, you know, I'm just bringing it up because there is the explainability component of it. So absolutely. So, let's now that we talked about Transformer models, we talked about GPT3 and ChatGPT. Tell a little bit about how Ascendo works. Ramki - You know, if you look at Ascendo.AI. At the core it also uses the Transformal model. Well, we essentially developed our model based on the domain expertise that we have, you know, many of our key people come from there, come from customer support or customer service background. So that's a great value because we know how the support model Works. How large companies' technical support organizations should handle even smaller ones as well. And we know the nuances of finding the answer for a customer question and issue. Sometimes, be a simple and elaborately explained to me what it is and what the product test is. Sometimes it is actually an issue. I'm facing, I'm doing this, and I'm facing an issue. What should I do? How can you help me? kind of thing. Our Transformer model essentially looks at the knowledge and the other data point that we have within the company that we are that we have implementing or we are basically providing Ascendo service on top and it's looking at all the content within the company and to evolve, what should be the answer. For example, there may be a new issue blowing, Right? it probably never happened before, but it's coming. There may be new knowledge that got updated. You know, somebody found an answer and the dots or maybe there was a bug that came in, and then somebody answer it, it became a knowledge, all these things are happening as things go by, then maybe these things in some similarity, there are some similarities with ChatGPT because we also use as kind of human feedback to make sure that we can constantly evolve and self-correct self learn, right? That part of it is very similar. We are using actual data and we are also evolving and with actual factual data, not from the entire internet to provide an answer. Kay - Very specific to the Enterprise, very specific to the product, Etc. So the analogy is very similar to the autonomous engine auto driving. Right? So we start with giving the Triggers on predictive actions, escalations impact, risk intended context, and all of that. Then our agents and leaders still make the decision on what they use and when they use it. So in a way, we automate the data aggregation, aspect of humans, maybe I would, I always equate it to what an engineering calculator did for the basic calculations but on an advanced scale so it does help remove the biased. It enhances collaboration, even when people went, whether people are together or remote and it also helps with faster problem-solving. So essentially, we are automating support ops, like, whatever dev-ops and rev-ops are doing. Back to chat GPT.So, the challenges explain a few challenges of ChatGPT, like the media. I kind of alluded to writing. Ramki - One of the biggest issues that we are all going to face. It happened even with the internet, right? When you see something, you may actually believe it. The way we probably get unknowingly got caught by the early days of the Internet, just because something is said multiple times, it appears opinions may drive the truth. The fact-checking asked to be will be on the Forefront. Unknowingly, someone keeps repeating the same thing or, you know, gets Amplified through multiple things. And then information comes on top. People may think that is the truth and it may, you know, actual truth will be hidden, right? That's where we have to be watchful. We have to be careful how these things happen just because it says something so nice and it, you know, feels correct and eloquent. It should not be that, it's always right. And we have to remember but it's a nice way of saying things but it is not the truth to have to do, a fact check. Kay - Yeah, a model is as good as what we feed it in and ChatGPT is fed with internet data and there is a lot of information that needs fact checking whether it is from humans or a machine and it's we at Ascendo we always talk about metrics versus data? Data helps say the story. So from a story standpoint aggregating all of this customer data and bringing out an ability to say, a story is something that models as Ascendo does, But the actual story is told by humans and not by the data itself. So that's where there is this human connection. So Thank you. I think this has been helpful So I was actually asking to ChatGPT to write about holidays in 2022 and it did respond by saying that the day it has stayed only up till September 2021 cannot write about 2022. But you did talk about the poem, it wrote about Ascendo AI, and want to share it, before we end? Ramki - Let me..you know, it's kind of interesting, you know, we basically talked asked it Hey tell me about Ascendo.AI. Like Steven would say it did a pretty decent job, you know, kind of filler information, you can call it. Now you can take it and you can now use this and can just change it the way we want to convey it or whatnot, but here it is, you know, it did a great job. I would say, Kay - I like that so let people read it while we're stopping the Livestream. Thank you very much for tuning in and we want to continue the conversation here on the LinkedIn and slack channel. So, feel free to post your questions and comments. What else can we do to help continue this engagement? Ramki - Thanks. Absolutely. Previous Next

Explore how AI can be used to improve service operations. Anne, a medical device leader, discusses a project where AI was used to analyze service data to optimize maintenance schedules. She highlights the importance of using a data-driven approach and getting stakeholder buy-in for successful AI implementation. Using AI to Drive Service Improvements | Transcription Kay - Good morning, good afternoon. Good evening. Welcome to the experience dialogue. In these interactions. We pick a Hot Topic. That doesn't have a straightforward answer. We then bring in speakers who have been there and seen this but approached it in many different ways. This is a space for healthy. Disagreements and discussions. But in a very respectful way, just by the nature of how we have conceived, this, you will see passionate wiser of opinions friends. Having a dialogue and thereby interrupting each other or finishing each. The sentences. Our mission is at the end of the dialogue. We want our audience to leave with valuable insights and approaches that you can try at your workplace and continue the discourse in our social media channels with that. It's my pleasure to introduce the topic of today. Which is AI and how to use AI to drive service improvements. And for this, we have several Eight. She is aum global medical device leader. And then, what interested me about Anne's background, is she has done everything from, our organizational strategy program management sales marketing field operations and she is ranked huge field service operations within Philips and Baxter in many many, many capacities and has grown. The business has considerably And she has a Ph.D. in biomedical engineering from Washington University with that. She focuses on the patient. Experience is fabulous and it fits in this conversation today. We're and will be taking us through a framework of how to introduce data science to improve service operations. Including how to identify a proof-of-concept project. So how do you start with a Concept and how do you determine AI is the right for your organization for this? She will be sharing a practical example of, how she and her team used AI data-driven insights. To drive improvements in service processes at large medical device companies and teams with that welcome and welcome to the discussion. Anne - Thank you. Okay. I'm excited to be here and speak with you and those who are Watching this experience dialogue today. Very excited to discuss this topic. Kay - Yeah, I know this is the first LinkedIn life for you. So it's a good experience and we had a lot of interest from service leaders who are in medical devices and outside of the medical device, backgrounds, who are in the show watching today, which brings me to the first question, what led you to impart down the AI Pack. Anne - Yeah. So you know, in the years that I've been managing service organizations but the one thing that you have a lot of within Services, data tons, and tons and tons of data so much that, you know, it leads you to wonder how best to mine. It how best to make conclusions from it and how do you, you know, improve, you know, over time and so what intrigued me about AI, you know, we had started. Are working with you through the R&D group, you know, looking at some futuristic kind of applications and reviewing log files and things like that, and as you and I were discussing with the team. Well, what can we do today? You know, so certain improvements have to be made over time. You have access to log files and increase the information that's available in them. But is there anything we can do today? That would practically help solve some of these issues. And that's where we started to discuss. Do you know what kind of information is available and then what can we do with it to help Drive improvements within These large organizations? Kay - Yeah, and usually what we have seen companies, look at implementing IoT on edge devices, digital twins data, lakes, and all of that, all of it was important. But I remember the first conversation we were having and you were like, what can we do with the service circuit data? Because we have very, you know, details of and what more value can be reaped from the existing service record data so we can improve service operations. And that's how I believe we started down the service record data path. Anne - Absolutely. Yeah. And I had in parallel but having a discussion within my organization that the Service Experts and the service leaders across the globe around you know some potential you know needs that they had that they saw. Right and so just to give an example in this, this is related to the project that we ended up working on together. Many medical devices require some sort of annual maintenance schedule, right? Whether it's an annual preventive, maintenance work by annual, you know, And this is a huge driver of overall service cost because it's a predictable service event that has to happen at a certain frequency and requires a field service engineer to go out and look at the equipment. Well, most companies are looking at, you know, what is the optimal p.m. schedule for any given piece of equipment? It's not always assessed when the product is originally designed, it is sort of assumed. Well we need to do something annually, right? And so with this, Particular device that we were working with there was a biannual p.m. that was pretty detailed and involved parts replacement and several things that were kind of required to keep the equipment up and running.However, there was also this annual on-the-off years kind of a p.m. light, you know. So it was an electrical safety check and some you know some basic things that India decided years and years and years ago were required to make sure that the equipment Moment was running and functional and so our regional leaders and our Service Experts were sorts of asking the question, what is this p.m. Am I doing? You know, is it reducing the incidence of corrective maintenance later? Excuse me. Sorry. Yeah. And there wasn't a good way for them to prove that so they had gone to R&d. And said can we look at this and Hardy said, well it's always been there.It's going to take a lot of time and money to assess that we're going to have to run devices and do testing. And, you know, let's just leave it for now. We've got other stuff we're working on and so it kept coming up and that's when we started our conversations around. What is it that we could do with the existing data? And we realized we do have these service records, right? We have a history of what has occurred on these devices now Now, the question was, how do you know if you took away a p.m. light, for instance, versus keeping it in? You know, what would happen to the devices? And that's where we had a bit of a fortuitous occurrence that had occurred as well. This is that just like within all large medical device companies, occasionally there are some misinterpretations or inaccurate interpretations of requirements which had You know, within this device in a certain country for many years and that's a compliance risk. But you know we run into it all the time within service that these things happen and you have to look at it and then make a decision. How do you remedy that? But in this case, we had a country that had not been doing the p.m. light events for some time, and then we had the rest of the world where they had. And so basically we had a test Population. And then, you know, the control that we could look at the service records and compare amongst them and say.So in these groups where that wasn't done what happened were there more Service events that were required. The problem was. And so we knew this but the problem is the amount of data, right? I mean, we're talking thousands, thousands of service records, you had to track it over time from one device to the other. There was also manual information entered, right? So sometimes when you do a PSA, You know, they have to replace something that wasn't part of that p.m. schedule and we needed to document all of that. And so that's exactly the kind of thing that AI is designed to help solve, which would require a massive amount of people to do a project like that.And so that's where, you know, your team and our team partnered up and spent probably good two to three months, right? Figuring out how best we Analyze that information. And what are we looking for? And what do we do with the information that comes out of it? So, that's, that's kind of just in a nutshell, the project that we embarked on and we can talk a bit more about, you know, the results if you'd like. Or if there are more details around that, if you think that the audience might like to know, I'd have to be happy to answer questions, too. Kay - Yeah. You know, what intrigued me is the path on which Your team embarked on it, right? So there was a hypothesis. Hey, we needed to evaluate it. And you question some of the assumptions that have been there for a long period. That's the one-second thing you want to make sure that there is data, so substantiate, whatever hypothesis that we come up with, and that has to be cost-effective from a service angle optimized and quality based does. Snot impact patient experience and has to make sense of the data has to make sense, such that going forward. The teams can operate with this new normal and that's a perfect you know, a data-oriented project that you are describing here, you know to be able to do something like that with existing data and two to three months is awesome. And we also mentioned a little bit about humans.Into data and humans enter data. I should say so which means there will be some level of algorithm inconsistencies and any should factor into that level of data cleansing to see which ones to take and which ones to ignore and where to put the emphasis on and all of that. Anne - Yeah. And it really required close collaboration between your team and then our team, right? Because you have to understand the process and the equipment and what's needed to be able to tell, you know, if we're seeing something a year later was that related to the fact that you know, the p.m. wasn't done or was it completely unrelated, right? And so the data can help tease that out but we wanted to look at that in detail together as a group because, you know, your team is the expert in the data. I didn't how to how to, how to develop those models and interpret the information that comes out of them and then you need the company to be the expert on the equipment right? To help point you in the right direction. Kay - Yeah, absolutely. And that also means doing a lot of change management internally across the geographies with the product teams. Getting a lot of product feedback from service data and then driving product Efficiency through service data is not normal in this industry and you have spearheaded some of it. And we see that with a lot of other Med device companies that we are working with, is this a trend that you see continuing? How did you embark on the change? Anne - Service is a huge cost driver for medical device organizations. Ask the globe, right? So it's a requirement, it's absolutely necessary. It provides a lot of value for customers as well. And so there's your huge attention on how to capitalize on that value while also reducing the cost, right? And that's true for the companies themselves, but also customers, right? So there's a lot of customers that do self-service on these, on these pieces of equipment, and they want to be able to manage how to do that themselves. And so the Question is, can they do it at the level that you know, an organization like Baxter Phillips does? You know, has that kind of knowledge and that kind of expertise within their group? Well you know the only way to do that is to provide them with the right data. The right tools. The right information to be able to service that equipment at the right level.And so I see organizations large medical device organizations are very interested in AI-based solutions for service because it is, it is a way to improve efficiency for them, as well as for customers, and to create more value over time as those insights and that information can be fed back to improve devices. Number one and also to help customers understand how to maintain their equipment better. So yeah. Yeah, please continue. Oh no, I was just going to say I mean, I think you know, AI is being used all over and every industry. But in particular in medical device service, Isis is one of those areas that can benefit the most from AI because of the massive amount of data and information that is running through a large organization every single day. Kay - We are so happy to have been popped off that Journey with you and your team. And what benefits do you see from doing this project? Anne - Yeah so I think, you know what What comes to mind first and foremost is always returning on investment, and what is the fine? What is the potential financial benefit? Right? And so, if we look at the example that we talked about with, you know, potentially removing this annual p.m. light, because the data showed that it didn't really, it didn't reduce the number of Service events that were happening with these devices over time. That alone and that example would have saved the organization a million dollars. Just to and that's every year, right? That's an annuity over time. So that's the kind of you know, what are relatively small projects that you can work on that have humongous Roi overtime right now. It's not just about Finance though. Service is all about customer experience and so you have to be able to prove that whatever change you're making first of all it doesn't degrade the customer experience in any way or another quality and compliance. Those are huge. Those are always number one and then beyond that. Is there an improvement? You know, for instance, in this case, if we tell customers will you don't, you don't have to do these p.m. lights either. If you're going to do self-service, right? That saves them time and money. They don't have to pull that equipment out of service for out of use for a day to do this. So that's a huge benefit to them and you can imagine and that's just in this example, but say that you Had access to service record data beyond this, and could look at Trends and patterns and see that certain devices are failing more frequently than others. For some reason, then that becomes a huge indicator of a customer experience issue or you might, we might need to pull the device out and then replace it or, you know, somehow determine which devices are functioning at the right level and not. And you could also avoid field actions, and narrow the scope of a field action if you could figure it out. For instance, it's only devices that were manufactured in 2015, that all of a sudden have these issues, that's the kind of power that I can give it can give that kind of insight. And ultimately, you know, help all of the metrics that a service organization is tracking, as well as, you know, improving the financial side as well. Kay, - You bring up a very good point. Most of the teams are used to looking. In meantime, between failure, and them tbf failure characteristics alone in service what you are bringing up is that alone is not enough, there is a lot more color to it which is when it was installed. What is geography? Who was the supplier?Do you know which teams worked on it? What are the human element and I can keep going? Can you think of some more failures? Chicks that you can add to what we were just talking about. Site of alone, MTBF Anne - Customer use patterns, it could be a chemical thing as well. That the way that they're using the equipment is somehow different in one region or another.It could be that the service process is not ideal in one country or, you know, with one set of tools. I mean there are so many variables and unfortunately, the tools to be able to assess all of that have been Limited. Did you know, for organizations and so they've had to embark on very large projects, to look at that information to try to narrow down the scope of field action, or to figure out the root cause or put together a Kappa, you know?So, these are ways that, I could speed up all of those various categories, Kay - each one is a problem in itself, right? So neat. On that. You're pointing out can drive service improvements significantly on its own.um, So can you expand a little bit on the other things that you mentioned here? You know you said it fast. You can. Please expand on it, Anne - sure.So, you know, if we're looking at some devices that let's say, you know, a piece of equipment was designed for an average use of Three times per day by a customer. But you're not, you're installing devices in a large high volume. A dialysis clinic is an example and they're doing 15 procedures per day. So most of the time, you know, the requirements that a device was built under our, then how it is tested, right? So for a device that's on average used three times a day, there's a certain service interval and frequency, and there's an Acted failure rate of certain Key Parts which are all within the acceptable range of how the product was originally designed for the customer requirements. But when you install those devices then and start using them just like you would a car, you start driving it. A lot more we have to do to get new tires, more frequently of to get the oil changed more frequently, and up until now, most companies haven't had the tools to be able to assess what is it that we need to do to make sure that that equipment. Payment is still delivering at the level that that customer expects. Right? And, do we need to modify the customer's expectations? Yes, we told you. It's only going to need one p.m. a year, but you're using it five times more than we expected. And so we need to do p.m. at least two or three times a year. Those are the kinds of insights that I think AI could help provide because of its real-time use. So it's unrealistic to design a product to Encompass all of the In areas, in which, it might be utilized in the field, they try, but it's very difficult to do, right? And so, the question is, once it's out there and they have real information about what's happening, how do we utilize that to then feed back into our service processes? Our data, our design requirements for the future to improve and I think that's where you know the types of Tools that Ascendo developing and putting together around a, I could benefit organizations in that effort. Kay - Yes, Anne what you did within the, you know, your team is, you're getting information from the product into service, but you close the loop going in from service, back into the product into the design of the next generation of the product, giving them insights. What you see in the field. So, in a way, you have alleviated, not just the service experience for the patients, and our customers, but you elevated the service experience for the R&D teams to absolutely. Anne - And I think it also has, you know, one of the challenges that a med device companies run into is that that feedback loop isn't always there as you just mentioned but even if it is there and it's getting into the next product, you still have 10 or 15 years of use of the existing product that you have to figure out how to optimize and it's unrealistic to read design the products that are out there, right? If there's a very large installed base there there there, and we need to figure out what to do with the ones that are already out there. And again, I think that's where I can help. So that's, that's sustaining engineering piece is huge for a lot of companies. It's a Strain on R&D, resources, and investment, it's necessary. But if there's a way to sort of provide better data around, what are the top opportunities? Because I think it gets hard, there's a laundry list of items that, you know, as a service organization, you want to see improved in the devices that are out there but R&d and sustaining engineering rightfully ask well, which ones are we going to go after? Because I can't work on 50 things. And so that's where having better data. Helps. You build out a case for which ones can require sustaining engineering resources, which ones are a process issue that could be solved within the service itself, or which ones do we just literally need to replace the devices because they're just not functioning at the level that was intended, you know? Kay - Yeah. And that's, you know, the beauty of using something like a simple AI is As you said, we can get that pretty much real-time information back from service as a voice of the customer into the product. And that gives a lot faster feedback back from the field into the product. And like you said, it's for sustaining, how do you manage and continue? The experience of the existing products is as much as the new products, right? So, getting this real-time, Input is hugely beneficial.um, Do you have anything else on the topic? Otherwise, I was going to switch it. Anne - Be only the only thing I think that you're hitting on probably transitioning into our next topic a little bit but that data piece is how you then convince stakeholders right.That this is an important initiative, that requires investment and that will generate the kind of return on investment that every organization is looking for. And so, that's huge. That's a huge piece of the change management part as well. Kay - Yeah. Can you speak about change management before and after AI because even after AI, it still affects the business processes before doing AI? It's a lot of convincing and looking at the data and being able to substantiate it. Yeah. Can you speak a little bit more about it? Anne - Absolutely. Yeah. So I think you know if we look at the example that I gave earlier you know it's It started with a team, you know, the team of experts, whether it's your Regional experts or your service engineering kind of expertise, experts on the hunches that they have, right? So they have these questions. Is this the p.m. cycle that's been redefined? Is it the one that is optimal for the device? Is it doing what's intended? Right? Is it reducing the number of Service events later and I think just starting with that, question knows, And was that had been in place for a long time? Hadn't gotten the right level of attention or investment. Because again R&d didn't have any data to say, why would we believe this? You know, this is what was predefined? Why change, what's working? You know we might introduce more issues, we don't want want to do that and those are all valid concerns, right? And so what is needed then is to get that data, right? And we ended up in a fortuitous situation where we Had a compare group and a control group that we could look at and basically, and I think any company could do that if they could create their own, their compare group, right? If you could take their requirement away in one area and then wait a year and then see what the data showed. We happen to have historical data, which was helpful. And so we were able to pretty quickly in two to three months. Compared those groups and take a look at what is the data show. And with the Data. Then you have evidence to go back to the right, stakeholder groups within R&D and it's going to be leadership, right? Because that's where the investment is required, both in terms of time from their teams and then also the return on investment. And so you want to show compare those two things and say this is, does this make sense to go after most of the time within service it will because even if you don't generate the returns in one year if you look at the lifetime of that equipment, You're going to generate it into, or multiple five years, right? And it will pay off and so. So that's kind of the direction that we looked and luckily in this case it was very clear well if we remove this p.m. light and here's the implication of that I think with some other types of interventions in might be a little more complex to say, well what do we do about this problem? If it's one particular part that seems to be failing more than another does that require Design, or does that require a replacement strategy? I think that could be a little more complex with some of the other problems. But the returns could be even greater right for something like that. And so, that's kind of how we moved that particular project forward. It had a pretty clear outcome, the data was convincing. And so the question just became like when can we do this, right? Not should we be doing this? Kay - Yeah, that's perfect. So essentially what you have given for any leaders, service leaders is a framework to do our think about AI Projects based on our joint experience together. So if I me summarize the kind of steps that you have been guiding us through, you started with a hunch you looked at what is the data that we have to substantiate that. Conch and where are the most efficiencies that can be improved and what information do I need? So it's also coming up with a clear deliverable at the end that needs to be convinced for the change management portion. There was a so that determined a clear outcome for the project and then ongoing, how do you continue that change management and the steps that need to happen? To create, you know, come up with that review and do this periodically. So did I summarize this correctly? Anne - Yes and I think, you know what you would like to do with the proof of concept. Like, this is convincing the right people within your organization of the power of this kind of approach, right? So that then it becomes something that the next time it's more Blessed. Right? There's less convincing that needs to be done around the model and the data and things like that. If you can get some buying early on and show the proof that that project worked, then that creates kind of a framework and a roadmap to continue this type of improvement down the road. Right? And so that was the vision for us let's pick something very tangible that we can use to develop a model. Internally how to use AI insights to improve service the fish. Kay - That's awesome. That's awesome. Thank you so much for your time. I think this is very very helpful from a service standpoint, for leaders to be able to start implementing AI within their teams and create that feedback loop and that voice of the customer to the R&D teams. Thank you for your time and for continuing. The Discussion and look forward to sleeping more benefits. Anne - Thank you for the opportunity. Previous Next