All (242) Blog Posts (55) Other Pages (187) 242 items found for "" Blog Posts (55) Chatbot vs Conversational AI Choosing the Right Solution for the Support Teams As businesses increasingly rely on automated tools to enhance customer service and operational efficiency, the terms " Chatbot " and " Conversational AI " are often used. However, while they share similarities, they represent different levels of technological sophistication and capabilities. Understanding these differences is crucial for organisations looking to implement the right solution for their needs. What is a Chatbot? A chatbot is a rule-based system designed to interact with agents through pre-defined scripts. It can handle straightforward tasks such as answering FAQs or guiding agents through specific processes. These bots operate within a narrow scope and are limited by the commands they’ve been programmed to recognize. For instance, Amazon Lex uses natural language models but is still typically deployed as a traditional chatbot in many applications, limited to specific, rule-based tasks. What is Conversational AI? Conversational AI , on the other hand, represents the evolution of chatbot technology, incorporating advanced machine learning and natural language processing ( NLP ) to enable more dynamic and contextual interactions. Unlike simple chatbots, the Conversational AI of Ascendo.AI can understand and respond to open-ended questions, learn from previous interactions, and provide more personalised experiences. This allows for more natural and human-like conversations, as seen in platforms like Google's Conversational AI, which powers virtual assistants to engage with users across multiple channels. Key Differences 1. Complexity and Flexibility : While ChatBots follow a strict set of rules, Conversational AI adapts to user inputs, making it more flexible in handling diverse queries. This flexibility is achieved through NLP and machine learning, enabling the AI to refine its responses over time. 2. User Experience : Chatbots are often limited to providing specific information based on user prompts, which can sometimes lead to frustrating experiences if the bot fails to understand the request. On the other hand, Conversational AI can manage more complex interactions, improving customer satisfaction by providing relevant responses and insights even in tough scenarios. 3. Scalability and Efficiency : Both chatbots and conversational AI offer scalability, but conversational AI has the edge in handling high volumes of interactions simultaneously while maintaining a high level of accuracy and relevance. This makes conversational AI ideal for businesses looking to automate complex customer service tasks without compromising on quality. 4. Application and Use Cases : Simple chatbots are best suited for tasks like answering FAQs, booking appointments, or guiding users through basic processes. In contrast, conversational AI can be deployed in a wider range of applications, from personalized customer support to sales and marketing automation, as well as complex data analysis tasks. Choosing the Right Solution When deciding between a chatbot and conversational AI , consider the specific needs of your business: - For Basic Interactions : If your primary goal is to automate routine tasks like answering common questions, a chatbot might suffice. Some other tools can be configured for these purposes with minimal setup. - For Complex and Dynamic Interactions : If your business requires a more nuanced approach to customer interactions, conversational AI is the better choice. It offers the flexibility and learning capabilities necessary to handle a broader range of queries and provide personalised responses. Result for Chatbot vs Conversational AI In the battle of Chatbot vs conversational AI, Chatbot gives a success rate of 60% whereas Conversational AI gives a 90% success rate. As conversational AI continues to evolve, businesses that invest in this technology are likely to gain a competitive edge by offering superior customer experiences and optimising their operations. For more information on conversational AI technology for the support teams, visit Ascendo.AI Learn more: The Future of Customer Service: Generative AI CRM Copilots Tips to transition from Self-Assign to Automatic Assignment Enhancing Support Efficiency with AI-Powered Correlation and Content Optimization In today's fast-paced digital landscape, efficient and effective support services are crucial for any organization. Leveraging the power of artificial intelligence (AI) and data correlation, support teams can transform their knowledge base and streamline content creation processes. Strategy Overview Identifying Common Issues and Solutions: By analyzing case data , we can pinpoint prevalent customer challenges and create targeted content that addresses these issues head-on. This ensures the knowledge base is populated with relevant information for future inquiries. Understanding Customer Needs and Trends: Data analysis allows us to identify emerging trends and customer needs, enabling proactive content development. Keeping the knowledge base up-to-date and aligned with current trends enhances its value significantly. Improving Content Quality and Accuracy: By comparing case data against existing articles, we can identify content gaps or inaccuracies. This continuous improvement process ensures that the knowledge base remains accurate and relevant. Optimizing Searchability: Analyzing frequently used keywords in case data informs content tagging and titling strategies. This makes content more discoverable for both customers and support agents , leading to quicker resolutions. Enhancing Self-Service Options: Insights derived from case data guide the creation or update of FAQs, how-to guides, and troubleshooting articles. Empowering customers with self-service resources reduces reliance on direct support. Customizing Training Materials: Identifying recurring themes in case data allows for the development or customization of training materials for support agents. Focusing on the most pertinent issues ensures effective training outcomes. Facilitating Product Improvements: Correlated case data serves as invaluable feedback for product development. By identifying areas for enhancement, we can mitigate similar issues in the future. Supporting Personalized Support: Creating content tailored to specific customer segments or product lines promotes personalized and relevant support experiences, fostering customer satisfaction. Value Proposition Implementing this comprehensive data correlation and content optimization strategy offers numerous benefits, including: Reduced Time and Resources: Less time spent on repetitive inquiries frees up resources for more complex issues. Improved Customer Satisfaction: Relevant and accessible content empowers customers and leads to higher satisfaction levels. Empowered User Base: A well-maintained knowledge base fosters a knowledgeable and self-reliant user community. Increased Content Publish Rate: By correlating content and identifying patterns , we can ensure that a higher percentage of case data is transformed into valuable knowledge base articles . Conclusion By leveraging AI-Powered Correlation and Content Optimization, support teams can revolutionize their knowledge base and deliver superior customer experiences . This strategic approach ensures that every piece of content is valuable, actionable, and aligned with customer needs, leading to increased efficiency, customer satisfaction, and product improvement. Are you ready to transform your support services? Embrace the power of AI and data correlation to unlock a new era of support excellence. Learn more: The Future of Customer Service: Generative AI CRM Copilots Tips to transition from Self-Assign to Automatic Assignment The Future of Customer Service: Generative AI CRM Copilots Customer support is a battlefield. Between sky-high expectations, complex product ecosystems, and a constant influx of inquiries, support teams are under immense pressure. Without the right tools, these challenges can quickly overwhelm even the most dedicated agents. The Struggles of the Traditional Support Agent Information Overload: Switching between multiple tabs, searching knowledge bases , and manually cross-referencing information is exhausting and time-consuming. Repetitive Tasks: Answering the same questions over and over drains morale and prevents agents from focusing on more complex, high-value interactions. Inconsistent Responses: Different agents may provide varying answers to similar queries, leading to customer frustration and confusion. Limited Personalization: Without a comprehensive understanding of each customer's history and preferences, it's difficult to provide truly tailored support. Slow Resolution Times: Long wait times and drawn-out issue resolution can damage customer satisfaction and loyalty. A Holistic Solution: Empowering Agents for Success A truly effective solution goes beyond simple automation. It's about empowering your agents and giving your team the insights they need to excel. Here's what a holistic support solution should offer: Agent Empowerment: Equipping agents with the tools and knowledge to effectively troubleshoot and resolve issues, rather than relying on scripted responses. Collaboration Tools : Providing seamless integration with platforms like Slack or Teams, allowing agents to collaborate with internal experts when needed. Knowledge Creation: Going beyond simple prompts, the AI tool should help agents understand the underlying issues and contribute to the knowledge base , creating a continuous learning loop. Real-time Insights: Providing leaders with actionable data on issue trends, customer sentiment , and root causes. This enables proactive decision-making, such as developing targeted training, updating knowledge articles , or even making product improvements. Enter the AI Co-Pilot: Your Agent's Superpower Ascendo.AI is that holistic solution. It's a Generative AI CRM Copilot designed to transform your customer support operations from the ground up. With its deep integration into your existing service CRM , Ascendo.AI : Empowers Agents: Gives agents the insights and tools they need to provide exceptional, personalized support. Improves Productivity: Automates routine tasks and surfaces relevant information, allowing agents to focus on complex problem-solving and building customer relationships. Enhances Efficiency: Streamlines workflows and facilitates collaboration, reducing resolution times and improving customer satisfaction . Drives Revenue: By fostering a culture of continuous improvement and delivering a superior customer experience , Ascendo.AI helps increase customer loyalty and drive repeat business. Provides Real-Time Insights: Equips leaders with the data they need to make informed decisions and optimize support operations . Are You Ready to Revolutionize Your Support? Don't settle for band-aid solutions that merely mask the underlying challenges. Embrace the power of a truly holistic AI co-pilot and unlock the full potential of your support team with Ascendo.AI . Let's build the future of customer support together. Learn More: Unleashing the Potential of Generative CRM: Redefining Customer Engagement Uncovering Trends and Redefining Success in Customer Support with AI-Powered Precision View All Other Pages (187) AI-Powered Slack Support Conquers Customer Service Chaos and Boosts Agent Productivity Download AI-Powered Slack Support Conquers Customer Service Chaos and Boosts Agent Productivity Breakthrough case study reveals how a high-growth SaaS company achieved 100%+ faster customer resolutions and a 100%+ productivity boost using Ascendo's AI-powered support platform within existing Slack channels. Please enter your business email ID Please enter a valid name with alphanumeiric value Drowning in Slack support chaos? As a high-growth SaaS company, you know the struggle: hundreds of channels, endless questions, overwhelmed agents. But what if Slack itself became your secret weapon? Ascendo's AI-powered platform unlocks the hidden potential of your existing Slack channels. Imagine this: Agents get instant AI support: relevant solutions, knowledge base suggestions, all within Slack's familiar flow. Lightning-fast responses: customers feel heard with under 1-minute first replies, no more ticket queue frustration. Supercharged agents: resolve issues 100% faster, freeing up time and boosting morale. Team-wide learning: Ascendo captures expert solutions, continuously improving its knowledge base for everyone. The results? The case study reveals: 300+ Slack channels tamed: transformed into efficient support hubs. Customers thrilled: exceeding expectations with lightning-fast service. Agents on fire: 100%+ productivity, empowered to excel. Ready to experience the Ascendo Advantage? Download our case study to see how AI can elevate your customer experience and unleash the full potential of your support team. Escape the chaos, unleash your team's potential, and join the Ascendo revolution today. Download now AI-Powered Slack Support Conquers Customer Service Chaos and Boosts Agent Productivity Ready to learn more? Contact Us Using AI to Drive Service Improvements Transcription Using AI to Drive Service Improvements Transcription Previous Next 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. Deepdive Voice of the Customer Playbook transcription Deepdive Voice of the Customer Playbook transcription Previous Next Kay - Welcome to the experience dialogue. This webinar is a place for healthy discussions and disagreements, but in a very respectful way, just 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 the dialogue. We want to make sure you are the audience to leave with valuable insights and approaches that you can take to your work. continue the discourse on the other social media channels. Today's topic is specific to going into a deep dive in specifically on the voice of the customer Playbook using wise of the customer data to enable us to understand that customer Journeys, which facilitates the ability to keep our customers satisfied and retain our customers to avoid treating our customer's Murmurs, we needed to set up and guarantee the level of support quality, which comes through the voice of the customer Playbook system. There are multiple levels of this Playbook and playbooks generally provide step-by-step guidance that's needed for the standard ways in which an agent, any customer success individual, or anyone who's involved in the customer journey responds in a standardized way. With that I would love to introduce the speaker today, Ashna. Ashna is an emotionally intelligent coach, who came across very well in the first decision that she had. I was also very intrigued with her background coming in from sales and she is a community Builder 4 Cs and as part of Cs Insider and would love to hear from her experience on the voice of the customer Playbook. Welcome, Ashley. Ashna - Thank you so much K. Good to be here. Excited to be here. Thank you. Kay - So we can start T off concerning. How do you see it with a customer Playbook and you know what faces, do you see that the revised estimate program? Ashna - That's a great question. So I think, now, when we talk about what to the customer Playbook, it's really about, I mean, it's kind of, in the name, it's really about that. Capturing those feedback, capturing those moments from the customer set, those expectations with the customer, and then having your processes built around. That so you know as part of your customer Journey which again it was the customer placed throughout your customer journey and as part of your customer Journey, there are many different areas now, we, when, you know, every part of it, there's Playbook But then there's Playbook Within the and these playbooks are processes and the way that voice of the customer plays around is in each of this area of your Customer Journey. How are you capturing information from your customer? And then, you know, making sure that you have Your processor built around it. So to answer your question it means there are different parts but you can start with the onboarding, you know, within the onboarding. There are multiple different playbooks that we can talk about. There's kickoff, there's sales to see as Playbook.And then, you know, as we progress with the onboarding implementation, you know, configuration and pushing the customer to long term success. That's kind of like the ongoing part of the things, then just a high level. There are some other Business Reviews. Play bulk, you know high Outscore lower Health score playbooks.So, really about the health score are asuh of customers journey. And then also, you know, renewal playbook which contains, you know, turn playbooks and even, you know, when there is an expansion and cross those opportunities. So, throughout that Journey, the voice of the customer plays, because you're capturing data from your customers, you have your processes built out so that you can Capture those data, and then you react against it. Kay - When you talk about processes, Schneider you come from a CSS background, you come in from a sales background, are you going to be covering those two specific areas so, when they hear your experience, do we look at it from those two contexts? Ashna - Yes, hundred percent. That's a great question. K. Because I believe that, you know, the success of your customer, really starts at the very beginning and also, you know, We see it starts when your customer, your prospects are knocking the door, but to kind of answer your question is that, you know, the hand of that the structure that you need to have defined a design that goes from cells to see s, it needs to be something that, you know, at that works. And you want to make sure that, you know, everybody, all parties involved, all stakeholders involved are on, you know, they understand that structure. You want to make sure that everybody's kind of on the same page. And so, Likely cells to CS handoff. That's something that even we have, you know, done quite a bit of work to implement and we continuously revise and redefine.You know what, we have just to kind of give you an idea in that specific Arena. What we have done is particularly different for a larger scale of the largest customer so Enterprise Sheedy, customers and then also different for SMB, mid market customers, there's a different type of, you know, the handoff and then the playbooks that we created there are a few changes. A little bit of a difference in those, but the sales to CS is about, you know, who's doing, what during this cycle, you know, Celsius playing some part of it, CSS playing some part of it, and I'll give you my example company, we have everything post cells is the customer. Success is handling so onboarding to up, to Renewal to some part of it. Customer success is handling it.So, the cells to see, we make sure that as soon as a prospect becomes a customer the end, CSM is a sign that to onboard that customer csm and sales members are coming together before connecting with the customer. And having that particular hand-off. Now for the, you know, midmarket and smaller team, we have just that, you know, it could just be book questionnaires that salespeople can answer for the CSM.uh What we recommend with the Strategic Customer because a lot is involved, there's a lot of stakeholders. The customer side but they're also involved. We recommend that you take that 1530 minutes on your calendar and have that proper hand-off between, you know, cells to CS. So it's a knowledge transfer session that happens between the cells and CS. Where cells are saying, you know, here's you know, how the deal went years what they want, this is the expectation, this is what we talked about, and here's the plan, here's the end goal for the customer and then CS is taking that. And then, you know, it's almost kind of like, you know, you're going into a war with all the tools and everything that you can. So see us. So that when they do get on that kickoff call, which is the next Playbook After that, they are prepared, they're confident and they know exactly, you know, how to get to the next stage. Kay - So that's the process that we have so a couple of things that I want to note down, right? So once we look at it as a joint partnership with the customer, it's not going on the water. Every experience we want to make sure our audience hears that the speaker speaks from their background and experience because we had previously, and the handoff, actually, sometimes in some companies, especially to be technically oriented starts much earlier, even at the solutioning, but space. And so the door kind of really, you know, is not a fact. They become a prospect to a customer, but even when they are prospects certain points to that because they are also trying to buy offers and everything. So, when we look at hand-offs, we have to remember the business model. We also have to remember that they support the business model. We also have to remember the product business model. Yeah. Both in mind before the hand of happens, um so in terms of, Setting the right expectations. Is this the fact that you look at it from a value-driven standpoint, are you defining the values here or you're doing it as a next step? Ashna - That's a great question and just to kind of redo what you mentioned. You're right. I think we also have some areas and some customers that we also try to, you know, do that hand off before that? So I think, generally speaking, it depends, you know, business to business, but you're right, you know, handoffs can be happening beforehand, too. And it's all about. Just make sure that you present that plan that you have in place in front of your customer and you're coming together as a team coming together, you know, from the customers and from the from your business, Community standpoint coming together as a team to Andrea, got another question, I mean, great question. I think the value proposition is throughout the journey. I think you're real, you're defining and redefining the value, you're making sure that you know, the end goal of the customers is continuously discussed and, and some help. Protective Services exactly kept kind of in the center of it. But I would say that you know, part of the onboarding Playbook as I mentioned, one of them, the biggest part is the Celsius handle, but also the kick off that you have with your customer. Now business, this could be different from the way that we have it. You know, after our hand we have ourselves reach out to our customer schedule that time. And what we're doing is we're officially handing off the customer on that kickoff call. CSM and in that kickoff, the call is all about. Here's what we know, here's the expectation, where are you in this process? Where do we need to go? Let's Build That Joint plan, as you mentioned together, and let's build out those timelines around it. And you know, you kind of go from you go further. So the main portion of the beginning, you know, at the beginning of the cycle, or when you are setting and realizing and understanding those values of your customers that ends at the kickoff level. But even before that, you know, cells, and see, we have already been talking about because cells know a lot about, you know what the customer's expectations are, what do they need, what the success looks like for them. So that's, that's been part of the conversation. It's coming together. Now, with the customer at this stage at the kickoff and you know making sure that we're all on the same page and we understand what is success for you and what's going to be the plan going forward? Kay - Yeah, I love them and always love the findings. W framework, right? So who what? Why how? And then there is a hitch there, but the interesting part in the entire hand-off is defining who is going to play? What roles are we in this together? What is it that we want to achieve but I do want to know how usually the definition of the customer does this? We do this. All of that gets defined, that's perfect. And when is very important because when are we going to achieve it? That's when the right expectations that you talked about earlier come. Yeah, play, right. So yes. When are we going to achieve something like this? And I know you had aum sample here that you would like to share with the audience, which will be there when we have the fine. um, Things. And you can also reshare it, so we will go from there. Soin then you talked a lot about health scores. So tell me from your experience. What defines the health score here? Ashna - Yeah, that's a great question. And, you know, help scores could be different from company to company, business, or business. I think, there are some generic major parts of Health scores or criteria for say, you know what makes a health score, but how you wait for it, you know, how you wait for your, your score per how you know what criteria are included in the health score. It could be different based on, you know, business requirements and based on the type of products you are based on the type of customers. The way that we have it is we have a higher Health score defined and then lower Health score defined and it's just you know, the criteria that we have included in the health score includes all sorts of it, not just about CS it is as We were discussing earlier. It's also about you knowing those support areas to you because the customer is getting, you know, customers getting support from all parts of the business, you know, from the CS a little bit from the sales as well and also from the support. So we want to make sure that we're capturing all of those into the health score as well. So he'll score for us is, you know, usage adoption their main ones and a 1 is also product and marketing, right? Kay - So there is yes, well, yes, so there are experts, for example, security companies. There's usually a security person from, you know, compliance companies and stuff like that. But please continue. Ashna - No, no, that's great Great. great. Correct. Correct. Just wanted to run that. Yes, Lily. uh, That's great. But no, I think it's a lot around usage adoption for us, you know, but we're also kind of doing, you know when was the last time you had an activity with this customer? That's something that we also Target the amount of Engagement that you had with them. You know, Finance is also somewhat involved. Have they paid their views already, you know, then you have support? As I mentioned, what we include from support, is what we call bugs, and other people like we call warranties and warranty. So, you know, how do they have active warranties and effective bugs with us? That's also included and waited somehow if they have cases that many cases open with us or tickets per se and for more than you know, 30 days or whatever, it's for that. That's also defined for us and so that's and also you know main parts surveys. You know we also support doing surveys. We want to make sure that those surveys are also as part of you know what's there whatever. You know The NPS RC sad or satisfaction score sentiments. Do you know what they look like? Then we also include gum. Check, you know sometimes your health score could be high but you know your CSM or your ccsm has a feeling kind of like well I'm not too sure about this customer for this many reasons. There is that then you have you know, if the champion has left that's also involved in the health score, then you have Acquisitions and mergers. If they've gone through that, we want to also, make sure that that's involved. Another major thing that we have is competitors, you know, have they, you know, if we have some insight into competitors that they've been looking at or been working with whatever? That's also something part of them, the health score that we have. So we have waited for this differently based on, you know what we find important. And like I said, it's business. A business could be different, but most of the time, some of these are, you know, really important ones that I have a feeling that you know, a lot of the companies include these types of criteria in their health score two. And then it's just about creating processes and these scenarios and which will be called playbooks. So, what we have is, if the health Or is about the certain line of a certain number. We consider that higher if it's below that then that's lower. And we're continuously tracking those and our CSM's have, you know, Cadence's and playbooks created around those so they can continuously, you know, make that as part of their schedule and going behind them, and then we have built out resources and libraries, you know, accordingly. So that we can pull those resources also and then send it to the customer based on where they are, you know, if they have a for Sample, they have a higher Health score. I mean, that that's an indication that you could be talking about, something more that you can do with the customer. There is some more value that you can provide me. There's an expansion opportunity, maybe there is a cross selling opportunity, you know, in this area or even to help marketing with some, you know, getting some reviews for them or whatnot. And so these are the areas that we have defined, okay? If this is a scenario, this is what you do. This is a scenario, that's what you do. But then again it could change over time. So we're continuously revising and redefining You know, I'd create different processes areas where our csm'scan work. Kay - That's right. That's the client creating that knowledge, not just creating the knowledge, but also making sure understanding, of which knowledge needs to be improved. Then examined, updating that piece of knowledge as required and that goes, comes back into the feedback from them, saying, hey, this helps us didn't help this needs to. Yeah, so yeah, is it in itself a journey. Ashna - Yeah, yeah, exactly. I hundred percent and I think and it's a queen back to the topic of the hour west of the customer. It's particularly in this area. Like just imagine how much feedback you're getting, you know, from the customer with all of these. There are different criteria that define a health score but that's also a way for you to get that feedback from your customer, you know. And then you kind of have your processes built out around it. Kay - So it's I think healthcare is one of the most of the voice of the customer playbooks uhscoring should definitely. It, you know, provides a standardization that nonscoring is not right. So there is a standard way to measure everything. Yes, right? So how is the support experience? How is the customer experience? How is the onboarding experience? was so each one of those, I think scoring definitely helps concerning Sizing and looking at data from multiple angles and also doing analysis on top of it. I loved how, you know, you did touch upon the feedback. So he's right. So there's a lot that can be done by surveys, but now we are also moving towards understanding sentiment in every tourist, and then bubbling up the actual sentiment of that interaction. So, we don't have to necessarily just rely on surveys alone. So 100 on, it's amazing how where the industry is going and there is a lot that we see that's happening on the right path, right? So yeah, that's present, yeah.ThemeForest, just brought out a couple of research and we shared them with our social media, and to State how much customers support customer support experience and customer experience. And Intertwine is at the lowest level in the industry right now. So and how that presents an opportunity to make Leaps and Bounds in that area. So glad we touched upon the Health's scoring part of it in detail. So yeah. So when we talked about onboarding sales to see someone off, we talked about the kickoff process. The five wiseW'sthing.Then we talked about the health scores. Anything else that you would like to see? Have you seen the covered invoice for customer Playbook? playbooks Ashna - Yeah, I mean, I know, I think it's fun because playbooks have playbooks within them. So it's kind of like, there's just so many that could be involved in it, but I think one of the other ones that I would say is the renewal which is also kind of like, coming back to the circle of the customer's life cycle. Renewals are also really important because and you're going to capture a lot of, you know, a lot of information from your customer whether they're renewing, whether they're churning, whether they're reducing, whether they're, you know, Expanding whatever that may be at the time of renewal, there's going to be a lot of data that is going to be captured from your customer. That's going to Define how your relationship with your customer is going to progress going forward, you know. So I think renewal is also a thought important part of it to kind of give you some more into it. I mean the way that we handle Virgo is all two different parts of it. We have Auto Renewals and then we have manual renewals either way. I think it's important that you are putting enough I guess, you know, you have a process defined so that you are capturing, you know, forecasting. First of all four rules are for scat forecasting. For your customers ahead of time, but then you also kind of like, you know, reaching out ahead of time to make sure that you have enough time and customers also have enough time to work with you. But you also have time to understand where they are and where they need to go. And then you have those options to present them. So I feel like when it comes to renewables, That's why I always recommend that those parts are implemented. Well within your, you know, playbook renewals. Kay - Let's go a little deeper into this, right? So at the end of the day, support and success professionals are the ones talking to customers. They are the ones who know the gut of the customer. They are the ones who know we did. We provide them the value that we signed up for and are we continuing to provide the same level of value to the customer? Right. Soso during renewal times that value assessment comes back into play, right? So if you can drill a little bit more into tying the value back into the renewal cycle, it'll be great. Ashna - Yeah. Yeah, 100%. So like I, you know, we talked about throughout the cycle, you know, there's even at the onboarding part of the journey, there's going to be that value assessment. You can be doing that value assessment. Whether it's a, you know, one of them, one of them, one of the practices again. Is also studying whether it's a survey or just like, you know, doing a little bit of a gut check at, you know, how successful this customer is? At the end of that onboarding cycle, when you're moving them into long-term success. So, that's kind of like the value that you've taken from them. If you're going through a business review if you look at Health scores, that the midjourneymade part of the Journey of customers' Journey. When you're looking at a health score, that's also a part of the value, you know, if they're held score is high, but if they have Champion, that's Left. I mean, you know, then that's kind of an area where, you know, I would like to consider that as like, you know, I'm going to put this customer or renewal at risk. Or I'm going to put a little bit more pressure on this, or more attention to this because there might be an area where I might have to resell. We sell it to them, if I don't have the champion that I used to have, things like that, it's important. I think. So all of these areas where you capture this information come back to that rule. And so that what we do is well, we're Advanced and I think a lot of companies are Advanced. You can have triggers and things created. What we have done is based on the health score and based on other criteria that had been talked about. You know, you could have triggers created that indicate that fork at that it's forecasting. Your renewal already. So that, you know, you already have a different set of customers that you can work with. And the way that I it's, you know if the health score is low, I want to Target them at risk, you know, already. And then we're tracking those separately and we're targeting those separately already. So there is a whole different Playbook that we played with that customer ahead of time. One of the things that we also like to do is, you know, sending them out sometimes, you know, for a certain segment of our customers, not all of them, but send six months in advance, whether it's a survey or just a question, like, hey, if you had to renew tomorrow, would you renew with us? And if the answer is, yes, well, then that's your opportunity to do. Castelo Expansions, and other areas that you can work with them. If the answer is no, well, then that's your opportunity and you have six months now to work with them rather than, you know, knocking on their door about renewal 30 days or 60days in advance. So it's kind of like, you know, creating those processes in place, which can kind of help you based on this information that you're capturing based on the value discussions that you've been having, maybe you've had a business review, where you talked about some of the values and you kind of understood that maybe something's happening on the customers business side of the things that might have. Effect in the future, that's your indicator. You know, that's what you want to kind of, you know, take it separately and work towards you knowing that that's almost kind of like marking at risk or maybe, you know, that could be a best case, we're not sure yet. So I think that's a whole another playbook right there for you that you want to, you want to kind of work with them. So that's valuable. The proposition is really important before renewal and also at the time of renewal because even at the time of renewal, you are going to be learning so much from your customer, maybe they will reach out to you six. It's or 90 days in advance that they're like, hey, we're ready to renew but we want to reduce our whatever the contract that we have now, that's a type of whole other discussion that you need to kind of go back and maybe you might have to resell to them on a certain level. So it's continuously looking at the data that you're capturing. And about, you know, what type of process is what are you going to do about them? And that's part of the plate. But that's all about those processes that need to be in place. Guidelines. You know, when I say processes their guidelines because you don't want to be stuck in that kind of like this is it, but their guidelines. So at least you know what the next steps are, and then you can kind of go from there. Kay - Yeah. Hardware upselling Hardware was always with a lot of benchmarks and with a little more value-driven even decades ago, I'm so happy to see that software is also getting More value driven because at the end of the day you know gone are the days where you're just buying it for a workflow like buying a database or something like that. So It's wonderful to see that value-driven aspect in every step of the way in a customer's journey and the customer and the transparency right to be able to do that. So thank you so much for your time Ashna. Really appreciate it. Appreciate the time you took to share. ThePlaybook and specifically drink-driving around the voice of the customer. Thank you for your time. Ashna - Thank you so much for your pleasure. View All

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The CCO Playbook: Unlocking Customer Experience Leadership Previous Next Good morning, Jeb, how are you doing? Good morning, great. Kay, how are you? Very good. I'm super excited for this conversation here because this looks like a consolidation of talking to multiple CC OS all at once. Talking to you hopefully. Yeah, I think yes, that's fair. I'll try. So Jeb, you were a chief customer officer at Oracle and one of the even even before chief customer officers was a cool thing to be in and, but you chose a path to actually guide and coach other CC OS. Why did you pick this path? Well, first and foremost, it's, it's easier to coach than it is to actually do the work, which I so there's, there's one small point, but setting that aside, you know, there's a, there's a few reasons. I, I mean, I think fundamentally when I, I've talked to a lot of CC OS over the last several years, as you might imagine. And, and what I, what I found, and I'm sure this is no surprise to you, is that every single CCO goes about their job differently. I mean, literally everyone goes about their job in a different way. They have different focus, different span of responsibility, different way they measure their performance. It's all different. And if I can do a just a little bit to help solve that problem and to make it easier and more effective for people coming into this role to be able to execute quickly, I feel like I'll, I'll have done some good. I also kind of look at it that the situation from the standpoint of, I mean, I would like to help at this point in my, in my work life in a way that I could have used help, you know, so if I, I just kind of look at I, I was ACCCO for 12 years, I guess about that. And if I had somebody that could help me in the way that hopefully I'm helping chief customer officers, that would have been amazing. Also there, you know, there's, there's just this kind of goes to the first point about every CCO having a different approach to their job and different span of responsibilities and so forth. There's no playbook at all. Like if you're, if you're a chief financial officer or CMO or even a chief operating officer, there's at least a playbook that's in your head, you know, that you can use to go about your job and, and really kick, kick into gear with some, some setting of priorities and, and working with people to kind of figure out what needs to be done and what can really make a big difference for the organization. Nothing like that really exists for the chief customer officer. And the last thing is just selfishly, I mean, I, every conversation I have with the CCO, I, I learn a lot. So it's, it's fun to me for, for that reason that I'm just, I'm just constantly learning and if I can share what I learn and, and share, you know, what I've learned in different ways, either doing the job or talking to people who are actively doing the job today, you know, I, I feel good about that. Hopefully, hopefully I've made a contribution. Thank you for that. Actually, you know, I have noticed that some non traditional industries CCO is even called chief experience officers. And I, if I, if I look at the so I run this group called the experience alliance. And if I look at the members of the group today, there's a wide range of people. There are chief customer officers, chief client officers, there's a chief, there's a chief experience officer, there's a, there's a patient experience officer for a healthcare provider. I mean, it runs a broad range of, of titles. So, so even when I look to bringing new, say new members into my group, I, I, I kind of generally sort of focus on those titles, but you have to kind of look beyond that for people that just fundamentally are thinking about CX broadly and thinking about how they can really have a positive effect on their business through CX programs. So that's, that's kind of more, more of the way I think about it. Yeah. So, so it's a good point that the titles are different, the job descriptions are different, the way in which each of the Ccos are approaching their own role is different and how they measure, how they implement what they do, it's all very different. So are you, you know, in terms of what you're achieving from the coaching program, are you trying to bring normality in the sense that are you trying to define this is a standard in which somebody needs to operate? Or are you saying, you know, Jeb as a CCO is doing it this way, how can I make them better and K as a chief experience officer or a patient experience officer is doing it this way and how can I make it better? What is the approach? I, I, I think it's a combination of those things. If, if I had to come down on one side of that or the other, I would say it's more about the latter in the sense that every, every chief customer officer, every chief experience officer, they're all, they're all doing their jobs differently. And so if I can, if I can learn about how they're doing it and try to help them do it better, that's, that's great. You know, I, I do, I do try to create some, some, some commonalities and, and kind of, kind of some best practices or at least some, some frameworks for thinking about how to approach the job. I, I'm, I'm not a big believer in one size fits all for anything, certainly not for CX and CCO work. So, so I try to avoid that. But, but I do think there's some, there are some things that people can learn from others and there are some commonalities, you know, that, that, that I try to, to bring out and, and really try to, to get people to, to think about in their own context. I mean, it's, it's a, it's a little bit like, I mean, I think about the, the commonalities of, of, of the customer experience in the sense of, you know, there's, there's some basic human psychology that underlies the way that customers make decisions and, and, and the way that they perceive their interactions with the business, you know, and there's some, some basic sort of economic principles that underlie the business decisions. And, and so if you can understand those and apply those in, in your own unique business context, I think that's, that's, that's kind of a good way to go about it. So I do, I do try to, to bring some, some commonality to, to what people are doing, but, but I also try to be very respectful of the fact that it's, it's, it's very much a, a, a unique role for each person. And, and if, if I, if I can help them get better, if I can help them sort of really drive some results in the business, however they go about their job, then, then, then I've done something useful.

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ChatGPT and the future of Customer Support Previous Next 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.

Using AI to Drive Service Improvements Previous Next 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.

Using proactive metrics for support operations Previous Next 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.