In the realm of AI-powered conversations , a persistent challenge has been the limitations posed by rule-based AI chatbots. However, we are excited to announce a significant breakthrough with our ResolveGPT—our AI Chat has undergone a remarkable upgrade with customer needs and preferences, now boasting Gen AI capabilities. This upgrade addresses the challenges of rule-based systems and elevates user interactions to unparalleled levels of customization and precision. Let us delve into these cutting-edge updates that are set to revolutionize the Artificial Intelligence experience. Overcoming the Challenge of Rule-Based Chatbots: Unveiling Gen AI Capabilities Context Option: Tailoring Conversations to Your Needs Our latest innovation, the "Context Option," serves as a powerful solution to the limitations of rule-based chatbots. This groundbreaking feature allows users to finely tune their interactions with the AI BOT by selecting specific data sources and grouping them into contexts. Imagine having a tailored Sales context or a Technical Support context - the choice is now yours. Based on your selection, you receive prediction results that precisely align with your chosen context. Customization is at your fingertips. Users can effortlessly switch between contexts with a simple click on the Context icon. This seamless context switching ensures that the AI Bot's focus matches your evolving questions or objectives, overcoming the rigid nature of rule-based chatbots. By incorporating context, you can anticipate highly accurate, tailored responses that align precisely with your intent, enhancing efficiency and satisfaction. Dynamic Context Switching: Precision on the Fly The challenge of rigidity is further conquered with dynamic context switching, allowing users to change the context of their query in the middle of an interaction with the AI Bot. Imagine troubleshooting an issue and receiving detailed steps along with a part that needs replacement. Need assistance ordering the part? Simply switch the context and get guidance on ordering, configuring, and integrating the part, all within the same conversation. No more sifting through multiple manuals—our AI Bot is your adaptable and comprehensive solution. Simple Start Option: Effortless Interaction Management To address the challenge of inflexibility in managing conversations , we've introduced the 'Simple Start' option. Users can now initiate a new conversation within an ongoing interaction seamlessly. Whether the conversation veers off-topic, or you have multiple questions, this option ensures a fluid and organized dialogue, conquering the rigidity often seen in rule-based chatbots. Tailored Results with Solution Display Preference Your preferences matter to us. In our intelligent AI Bot, we have introduced the 'Solution Display Preference' feature. Choose whether you want to view the top results from each data source or see all data sources for a comprehensive result. This level of control ensures that you receive information tailored to your needs, delivering a personalized and efficient interaction with the AI Bot, overcoming the limitations imposed by rule-based systems. Try It Now and Embrace the Future! Excited to experience the future of AI conversations? Try out these new enhancements and witness firsthand how they overcome the challenges of rule-based chatbots, providing a dynamic, user-centric experience with our ResolveGPT. The context switch significantly enhances the built-in Context engine, making conversations intuitive and user-friendly. These expanded capabilities ensure that every conversation with Ascendo AI Bot is a tailored and efficient experience, empowering you to optimize your workflow and enhance overall satisfaction. Ready to redefine the way you interact with AI? Go ahead and try these new features. If you need assistance setting them up, reach out to us—our team is here to help. The future of AI-driven interactions is now, and Ascendo AI Bot is at the forefront, leading the way into a new era of seamless and user-centric conversations. Learn more What sets Ascendo Apart? Revolutionizing Customer Support with Ascendo AI and SAP Service Cloud

In today's fast-paced business landscape, efficient customer support can make or break a company's reputation. Complex support cases, outdated information sources, and the challenges of remote work can often result in frustrated customers and overwhelmed support teams . However, a game-changing solution is appearing in the form of Ascendo AI, a powerful self-learning engine, seamlessly integrated with SAP Service Cloud . The Challenge: Complex Customer Support Complex customer support cases have long been a thorn in the side of businesses. The need for quick and exact resolutions often leads to a cycle of escalating calls, waiting times, and incomplete answers. An aging workforce and the rise of hybrid work environments only worsen the problem. Knowledgeable experts leave the company, and the information nexus shifts further from effective solutions. The Solution: Ascendo AI Ascendo AI , a co-pilot for technical support teams that transforms the support landscape. Ascendo leverages AI to extract meaning from every interaction, guiding users through step-by-step problem-solving processes and delivering effective generative AI solutions. It offers real-time, predictive guidance that eliminates the need for consulting multiple sources or transferring tickets. Engineers receive highly accurate game plans with over 90% accuracy. The Power of Ascendo AI Knowledge Intelligence: Ascendo AI harnesses the power of knowledge intelligence to provide precise predictive guidance in real-time. This minimizes the need for juggling multiple systems or external assistance. The AI distills information from diverse sources, transforming it into specific actionable steps tailored to each customer's unique needs. Debug Engine Ascendo AI acts as a robust debug engine, specializing in predicting root causes for even the most unique issues. It goes beyond surface-level identification, delving down to the level of individual parts. Additionally, the AI suggests the optimal technician for a given problem, streamlining the entire support process for efficiency and effectiveness. Generative AI Ascendo AI employs generative AI to offer comprehensive insights that extend beyond mere symptoms and resolutions. This advanced system predicts a comprehensive range of pertinent details crucial for precise issue handling. From forecasting Customer Satisfaction (CSAT) to evaluating customer effort and performing sentiment analysis , Ascendo ensures accurate and holistic issue resolution. Predictive Actions: Ascendo AI continually learns from interactions, giving it the capacity to recognize emerging trends and provide actionable insights for service enhancement. As time progresses, the AI-driven recommendations evolve, becoming increasingly refined and effective in providing guidance for optimal support strategies. Seamless Integration with SAP Service Cloud Ascendo seamlessly integrates with SAP Service Cloud , leveraging information from various platforms, including SAP, Confluence, Slack, JIRA, PDFs, and more. Through a single sign-on capability, issues are effortlessly transferred to Ascendo for precise solutions. The Journey Unfolded · Problem Identification: Ascendo analyzes past solutions and sources to identify the most suitable resolutions. Top solutions, complete with views and ratings, rise to the surface. · Guided Solutions: Based on the likelihood of specific categories and subcategories, Ascendo generates guidance for the issue at hand. · Generative AI Solutions: Ascendo creates generative AI solutions tailored to the problem. Agents can preview and send these solutions to customers via email. The Promise of Ascendo AI Ascendo transforms customer support by offering accurate, efficient, and innovative solutions that empower support teams and enhance customer experiences. With the ability to integrate seamlessly into SAP Service Cloud and access data from various sources, Ascendo becomes a reliable tool in the hands of support professionals. In conclusion, Ascendo AI represents a significant advancement in the field of customer support, addressing the challenges posed by complex cases, remote work, and outdated information. Its ability to extract insights from interactions and provide tailored, AI-driven solutions revolutionizes the way support teams engage with customers, leading to improved customer satisfaction and business success. Learn more, Future of Work in Customer Support With AI Ways To Improve Customer Services With AI

Customer Support Software Trends and Ascendo's Innovation In the dynamic customer support landscape, businesses continuously seek ways to stay ahead of the curve and deliver exceptional experiences. As we approach 2023, customer support software trends are shaping the industry's future, revolutionizing how businesses interact with customers. One company leading this charge is Ascendo, a pioneer in the field of AI-powered customer support software. Ascendo's cutting-edge innovation unlocks new possibilities, empowering businesses to elevate their customer support operations and achieve unprecedented growth. Top Features to Look for in Customer Support Software Trends: Ascendo's Cutting-Edge Capabilities Omnichannel Support: In an interconnected world, customers expect a seamless and consistent support experience across various communication channels. Ascendo excels in providing omnichannel support, seamlessly integrating across channels such as email, chat, social media, and more. This cohesive approach ensures that customers receive support on their preferred platform, enhancing customer satisfaction and loyalty. Sentiment Analysis: Understanding customer sentiments is crucial to delivering exceptional support experiences. Ascendo's advanced sentiment analysis capabilities enable the software to gauge the emotional tone of customer interactions. Armed with this insight, support agents can respond with empathy and address issues proactively, enhancing overall customer experience and fostering positive brand perception. The Growing Importance of AI in Customer Support: How Ascendo Stays Ahead Enhanced Efficiency: As businesses handle an ever-increasing volume of customer inquiries, efficiency becomes paramount. Ascendo's AI-powered automation capabilities streamline routine tasks, reducing response times and leading to quicker issue resolution. Automating repetitive processes allows support agents to focus on more complex and high-value interactions, resulting in increased productivity and improved customer support outcomes. Personalization: Customers appreciate personalized interactions that cater to their unique needs and preferences. Ascendo.ai leverages AI-driven insights to empower support agents with relevant customer information, enabling them to provide tailored solutions and recommendations. The personalized touch fosters stronger customer relationships and elevates the overall support experience, driving customer loyalty and advocacy. Continuous Learning: In an ever-evolving world, staying up-to-date is crucial for success. Ascendo's models are continuously learning and improving from customer interactions. This adaptability ensures the software remains relevant and effective in addressing new and emerging customer needs. By harnessing the power of continuous learning, Ascendo.ai delivers adaptive and informed support, setting it apart as a forward-thinking solution in the market. Ascendo in Action Improved Customer Satisfaction The true measure of customer support success lies in customer satisfaction. Ascendo's ability to deliver personalized interactions and prompt resolutions has elevated business satisfaction rates. Satisfied customers are more likely to become loyal advocates, driving positive word-of-mouth and attracting new clientele. Seamless Agent Onboarding Transitioning support agents to a new software solution can be a daunting task. However, Ascendo's user-friendly interface and intuitive design make the onboarding process seamless. With Ascendo, support agents can quickly adapt to the platform, ensuring they can deliver exceptional support from day one. Rapid Ticket Resolution Time is of the essence in customer support, and Ascendo's ticket analysis and categorization capabilities enable support agents to address issues swiftly. By efficiently routing tickets and providing relevant information, Ascendo.ai reduces backlog and improves overall support efficiency, leading to faster ticket resolution times. In summary, customer support software trends for 2023 are driving businesses to embrace innovative solutions that optimize support operations and elevate customer experiences . Ascendo stands at the forefront of this transformative wave, offering cutting-edge capabilities that empower businesses to unlock growth and success. From omnichannel support and sentiment analysis to enhanced efficiency and personalization, Ascendo.ai delivers a comprehensive suite of features that meet the evolving needs of modern customer support. As AI continues to shape the future of customer support, Ascendo remains a trailblazer in the industry, leveraging the power of continuous learning to stay ahead of the competition. Ascendo.ai offers a persuasive and forward-looking solution that entices readers to work with us, revolutionizing customer support in 2023 and beyond. FAQs - Customer Support Software Trends for 2023 What Industries Can Benefit From Ascendo’s Customer Support Software? Ascendo's customer support software is versatile and can benefit businesses across various industries, including e-commerce, technology, finance, healthcare, and more. Whether you are a small startup or a large enterprise, Ascendo.ai's cutting-edge capabilities can help optimize your customer support operations and elevate your customer experience. Does Ascendo Offer Customization Options to Align With Our Specific Business Needs? Yes, Ascendo understands that each business is unique and may have specific requirements. The software offers customization options to align with your workflows and business processes, ensuring seamless integration and maximizing efficiency in your support operations. Learn more, Future of Work in Customer Support With AI Ways To Improve Customer Services With AI

Revolutionizing Customer Support with AI In today's fast-paced and technologically advanced world, customer support is pivotal in ensuring business success and customer satisfaction. As businesses strive to deliver exceptional support experiences, the need for innovative solutions has become paramount. This is where Ascendo steps in, revolutionizing customer support with its cutting-edge AI-powered software. Ascendo, a leading tech industry player, combines artificial intelligence and advanced natural language processing (NLP) techniques to transform how organizations handle customer support. By harnessing the capabilities of machine learning and automation, Ascendo empowers businesses to enhance efficiency, productivity, and customer satisfaction. The Future of Work With Customer Support: Enhances Efficiency and Productivity The future of work with customer support is all about efficiency, productivity, and streamlined processes. Ascendo embodies this vision by providing intelligent automation that handles routine tasks, allowing customer support agents to focus on more complex and high-value interactions. With Ascendo, organizations can experience a significant reduction in response times, as the AI-powered software accurately understands customer queries, identifies the appropriate solutions, and generates personalized responses in real-time. This results in quicker issue resolution, enhancing customer satisfaction and loyalty. Moreover, Ascendo's innovative approach goes beyond traditional keyword-based search and response mechanisms. By employing advanced NLP techniques, such as sentiment analysis , intent recognition, and entity extraction, Ascendo gains a deep understanding of customer interactions. This allows for more personalized and contextually relevant support, fostering more robust customer relationships. Choosing the Right Customer Support Software: Ascendo's Key Features and Benefits Regarding choosing the right customer support software, Ascendo stands out with its key features and benefits. Let us explore some of the standout capabilities that make Ascendo the ideal choice for businesses looking to elevate their customer support: AI-powered Automation: Ascendo's automation models are pre-trained on a vast range of customer interactions , ensuring a comprehensive understanding of text representations. The AI-powered automation streamlines routine tasks, enabling agents to focus on complex issues and deliver personalized support. Intelligent Knowledge Base: Ascendo leverages a robust knowledge base that continuously learns from experts' interactions. This knowledge is shared across the organization, empowering the entire team with real-time access to the most up-to-date information. Seamless Multi-Channel Support: Ascendo seamlessly integrates with various communication channels, including email, chat, and social media platforms. This ensures a consistent and unified support experience across multiple touchpoints, enhancing customer satisfaction. Data Analytics and Insights: Ascendo provides in-depth analytics and actionable insights that help organizations identify trends, measure agent performance, and make data-driven decisions to improve overall support efficiency. Enhanced Self-Service Capabilities: With Ascendo, businesses can offer self-service options to customers, empowering them to find solutions to common queries and issues independently. This reduces the workload on support agents while providing customers with instant assistance. Ascendo vs Other Software: What Sets It Apart and Why It is the Best Choice? In a crowded customer support software market, Ascendo distinguishes itself by offering a unique and comprehensive solution that truly empowers organizations. Here are some aspects that set Ascendo apart from the competition: Advanced AI Capabilities: Ascendo's utilization of Advanced Transformers and Attention networks allows a deep understanding of the text in vector space. This enables accurate context-based responses and a more nuanced understanding of customer interactions. Privacy and Security: Ascendo prioritizes the privacy and security of customer data. With robust encryption protocols and strict adherence to data protection regulations, businesses can trust Ascendo to handle sensitive information with the utmost care and confidentiality. Continuous Learning and Improvement: Ascendo's models are pre-trained on vast datasets and fine-tuned using customer-specific datasets. This ensures that the software continuously learns from each organization's unique interactions, adapting and improving over time. Customization and Scalability: Ascendo offers flexibility and scalability to meet the specific needs of businesses. The software can be customized to align with organizational workflows, ensuring smooth integration and maximum efficiency. Ascendo is revolutionizing the future of work with customer support software by empowering businesses with AI-powered. Ascendo enhances efficiency and productivity through its advanced AI capabilities while providing personalized and contextually relevant support. With a comprehensive set of key features and benefits, Ascendo stands out in the market, offering privacy, continuous learning, and customization options. By choosing Ascendo, organizations can elevate their customer support operations, deliver exceptional experiences, and drive long-term success. FAQs - Future of Work With Customer Support What Are the Advantages of Ai-Powered Customer Support Software? AI-powered customer support software offers numerous advantages, including: ● Enhanced efficiency and productivity through automation of routine tasks. ● Improved response times and quicker issue resolution. ● Personalized and contextually relevant support. ● Access to actionable insights and analytics for data-driven decision-making. ● Consistent and unified support across multiple communication channels. ● Self-service capabilities for customers, reducing the workload on support agents. How Does Ascendo Ensure Data Privacy and Security? Ascendo prioritizes data privacy and security by implementing robust encryption protocols and adhering to stringent data protection regulations. The company employs best practices to safeguard customer data and ensures that sensitive information is handled with the utmost care and confidentiality. The future of work lies in embracing innovative solutions that enhance efficiency, productivity, and customer satisfaction. Ascendo's AI-powered customer support software empowers organizations to stay ahead of the curve and deliver exceptional support experiences. Learn more, Role of Customer Support Team in Engagement and Churn Strategies To Manage Key Customers Through Difficult Situations

Automated self-service approaches are a set of techniques that allow people to solve problems on their own without having to ask for help. They're often used in situations where asking for help would be too difficult or embarrassing. It's increasingly hard to find retail shops, restaurants, and public spaces that don't utilize self-service technologies in some form. Everybody wants things done quickly and without concern for the process. Likewise, to enhance customer service and cut expenses, organizations are increasingly introducing automated self-service technologies (SST). Besides, if you are still not convinced that self-service is the best way to be served, many companies are adopting the combination of human-machine. That way, they satisfy both needs: speed and personalized service. Let's take a look at how self-service technologies are being employed in different sectors. 1. Travel SST in the tourism and hospitality industry allows for a better customer experience. Airports The airport can be a hectic and crowded environment, with long queues and waiting periods. SST enables airports to replace flight check-in, baggage check-in, and airport parking with automated machines, thereby improving the overall experience of air travel. From the customer's perspective, SST shortens the time it takes to complete formalities and provides flexibility. Hotels At hotels, self-service kiosks give customers a convenient check-in option, especially at late hours. Guests can use contactless technology for various purposes such as checking in and out, updating preferences, room service, and payment. Tourist Places In the age of COVID, it is more important than ever to employ self-service stations in tourist spots as they keep the public safe by limiting the spread of viruses and other germs. SST also assists tourists in ticketing, navigation, and providing information on exhibits. Parking Kiosks They are compact devices that help motorists obtain parking tickets for parking their vehicles in a parking lot for a certain period. Sometimes, people don’t park their cars in an organized manner and end up occupying so much space. By enforcing smart parking systems such as CCTVs and parking kiosks, the parking lot can be used to its optimum level. Motorists also have a variety of payment options. With SST, the old parking systems' difficulties are gone for customers. 2. Banking Traditional banking structure is rigid and not customer-centric. Previously, opening an account took many days and numerous paperwork. People who travel abroad often could not control their finances easily. Even today, banks are accessible only during a certain time. Customers demanded flexibility in banking. The advent of self-service technology thus drastically improved customer experience. Now, customers can manage their finances at all times without having to visit a branch by using self-service channels like ATMs, mobile banking, banking apps, and internet banking. Automated self-service options have made processes- monitoring account balances and statements, transferring funds, paying bills, and making purchases- quicker and hassle-free. People don't have to worry about long waiting periods, thanks to multiple payment methods and 24/7 access to services. 3. Healthcare Be it at a hospital or a doctor’s office, SST can improve in-person visits. Patients expect their healthcare experience to be fast and flexible, as other industries are already offering this type of service to customers. Digital technology has given birth to all-in-one platforms that automate the complete medical check-in procedure. Collecting and integrating patients' data is a huge benefit. This means that patients' data will be available to doctors, hospitals, insurance companies, and other medical institutions. In addition to providing a seamless experience for the customer, this process also greatly enhances patient and preventative care. 4. Retail SST in retail spaces has become a part of our daily lives rather than a new feature. Supermarkets Shoppers can now scan and check out their purchases without assistance, thanks to self-checkouts. The number one customer pain point, as per Capgemini's Smart Stores report, is having to wait in lengthy lines when it comes to billing. However, through SST, customers enjoy a finer in-store experience and a swift checkout process. Fast Food Chains Customers don't like to wait around when they visit fast food restaurants in particular. With the help of SST, customers can place orders and pay their bills in a single transaction. Getting the order correct is a metric for customer satisfaction. Self-ordering increases order accuracy. It directly sends the order to the kitchen, expediting the overall process. Vending Machines Automated devices known as vending machines distribute goods like refreshments, snacks, lottery tickets, and other items. By doing away with the requirement for a physical checkout, these stores also have the advantage of being open around the clock and giving customers the quick, easy shopping experience they've grown accustomed to. Self-Service Gas Stations Where customers serve themselves, paying before they pump gas into their vehicles. Self-service gas stations are usually cheaper than full-service ones, and customers can save time by not having to wait for an attendant. This is however a contentious technology as fear of fires and explosions has led some places to ban self-service gas stations. 5. Government Government-run institutions are becoming more customer-friendly by employing self-service technologies. DMV DMVs are changing the perception of who they are by providing a good customer experience with the use of SST. To reduce the queues and therefore wait times and also to save money, kiosks are set up in DMV offices and off-site locations. These kiosks provide various services including license renewal, duplicate printing, as well as registration renewal and printing for cars, trailers, and boats. Some DMVs are collaborating with the judicial system so that court-ordered fines can be paid via self-service kiosks too. They not only give customers a better experience by removing the dreaded wait, but they are also providing them with the ability to do things other than those unique to the DMV. Post Office Customers want same-day delivery, real-time tracking of their packages, and the ability to redirect deliveries that are already in flight. To keep up with these demands, post offices must adopt digital technology. Self-service enables post offices to be more agile and responsive, while also allowing for the establishment of new services at a cheaper cost. Post offices with greater accessibility have greater revenue potential and can satisfy their customers while focusing their staff on the most valuable or complicated customer transactions. Customer Automated Self-Service: What Is It & How To Do It Right? Customer automated self-service is the process in which a business gives its customers the ability to find solutions on their own without having to wait for a support representative or another team member. Resolving common customer issues should be automated. This way, your support agents will have time to tackle complex problems. Build your self-service tools based on your customers' issues to gain maximum benefits. In the rare scenarios, automated self-service goes wrong, do let customers know that you've made a mistake and apologize for it. This type of communication works because it shows that you care about making things right with the customer. Track the performance of your SST through various metrics such as Customer Satisfaction Score (CSAT), Customer Effort Score (CES), ticket resolution rate, and the number of unresolved tickets. The data will provide insights for further optimization. Build A Best-In-Class Customer Self-Service Experience Ascendo helps companies offer proactive support with the power of artificial intelligence. Based on customers' queries, Ascendo's AI bot provides intelligent predictions and guides toward resolution easily. Ascendo is available directly on channels like Slack and WhatsApp to assist support teams in handling customer issues, making the process more interactive, more accessible, and faster. All interactions with customers are recorded and used as a knowledge base to understand the trends of customer concerns over time. When an agent handoff occurs, the agent is not only aware of the previous solutions the customer has tried, but also has access to more predictions due to extensive data access. Ascendo empowers support agents with AI Search, a feature that consolidates knowledge from multiple data sources to provide solutions in one platform. Customer support teams can easily add this smart search to their work environment and use it to solve issues efficiently. If you are interested in improving your support operations, contact us.

The fear of not having enough data can stall an enterprise's digital strategy. When you think you do not have much data, you stop to look at potential possibilities with existing data. However, it becomes a singular focus to collect additional data. You invest in making changes to your product to bring in sensors or vendors who coach you on how to collect additional data. Doing this without exploring what value you can bring in with existing data is equal to diversifying your portfolio without knowing your current asset allocation. So how much data is needed? Professor Yaser Abu-Mostafa from Caltech answered this question in his online course. The answer is, as a rule of thumb, you need roughly 10 times as many examples as there are degrees of freedom in your model. The more complex the model, the more you are prone to overfitting, but that can be avoided by validation. Much fewer data can be used based on the use case. At Ascendo AI, we provide a field service SaaS application for manufacturers to do service planning. In one of our use cases, a manufacturer had thousands of devices but only a few field service technicians. We provided AI-based automation to help the manufacturer reduce dispatch of field service technicians. Essentially, this allowed the company to use existing data to optimize for the given number of technicians. The automation steps to reduce dispatch include: Automatically assigning a service rep. Providing the service rep with a potential solution for the problem. Predicting issues before they happen to remotely fix them. But when it does require a field service technician to go in and fix the problem, they can only handle a few incidents every day. In this case, it is critical to predict high-priority incidents that need the most attention versus being 100% accurate in your predictions across all incidents. This way of thinking reduces the need to have every piece of data possible to even start the digitization journey. Data based on internal surveys that we conducted has shown that enterprises only use 1% of data collected, while 33% of the data is actually usable. And according to Forbes contributor Bernard Marr, "On average, companies use only a fraction of the data they collect and store." It is critical to work with software that can extract value from data you already have. Whether you build (in-house) or buy (vendor), your software should specialize in identifying gaps in your data. To do so, your software would specialize in: How to prepare data and partition it for training and testing. What algorithms and heuristics to use with it. How to call out relevant patterns. Actions that can be performed from a reliable system in production. Identifying inefficiencies in the data so you can add processes to clean them. Showcasing the most critical area for new data to be collected. The need for data depends on the variety of data we need. For example, to predict a device failure, you would need data based on the normal status of the device as well as data when it generates anomalies. The higher the variety, the fewer data (in terms of time and volume) is required. Beginning a journey of whether you have enough data will show inconsistencies that you likely never realized, show holes in your business processes that you thought were perfect, deliver cost savings on what you thought was already optimized and, hopefully, generate additional revenue from where you thought the pie could not possibly be any bigger. Learn more, Evolution of Data Before Implementing AI Escalation Management Guide For Proactive Support Teams

The field service management (FSM) industry revolves around incidents that are handled by end employees. The incident life cycle begins with the initial submission and ends with a resolution to the problem. Incident tracking or ticketing systems have been around for years. Moving these systems to the cloud has been the biggest tech-based evolution in the industry so far. The Current State Of Service And Why It Needs To Change Today, the customer service industry is complex. Support technicians need lots of time to resolve issues, use more parts than they need, and are bombarded with having to support multiple products and versions. At the same time, hardware and software product companies are shifting toward service-driven revenue growth. Unfortunately, all aspects of service and FSM / ITSM systems are workflow-driven, time-based or monitoring-based and reactive. Automation of these systems alone will not fulfill the needs of the enterprise. It requires a new class of solutions that taps into the systems of record to create a system of intelligence through which decisions can be made. There are four primary reasons why faster resolution of service incidents should be made more effective: 1. The scaling issue: Incident tickets do not come from humans alone; there are also product-generated alerts. The assignment of a remote service agent can take a while. Agents need to be quick in diagnosing the problem as well as the resolution to the problem. Even with human-generated tickets, products getting so complex, with shorter product life cycles, that remote staff members need help in diagnosing problems and providing quick resolutions. It costs an average of $250-$1,000 per ticket. For hardware companies, costs go further up when having to send a technician for repeat fixes. SaaS companies resolving 86% of customers say they will pay more for better service. By 2020, customer service will drive sales more than product or price. 2. Complexity: According to Field Service’s Future Trends report, 81% of companies believe smart connected products will be implemented in the next five to 10 years. With the advent of robotics and innovations in manufacturing and materials, digital equipment out in the field is complex. Problems are diagnosed remotely. When an issue can’t be diagnosed, a dispatched technician goes in to diagnose the problem and then makes multiple visits to install the part in place. Instead of worrying if a technician has expert knowledge in regard to a particular problem, automation can help identify a problem, make sure the parts are in place and provide detailed instructions for fixing the issue so a field technician can focus on the customer relationship and procuring new opportunities instead of the diagnosis. 63% of agents are solving difficult problems in high-performing organizations versus 43% at under-performing companies. 3. Staff skills are changing: Service staff that supports legacy systems also need to be trained to support new systems. Baby boomers are retiring at a rapid rate, and as they ride off into retirement, companies are losing tribal knowledge. Sixty-five percent of service training is outdated. I’ve seen firsthand how millennial recruits no longer have the touch-and-feel experience or understanding of the equipment, and they don’t consider field service as a long-term career. So, new recruits need to be trained constantly. These new recruits come in with college degrees that companies want to utilize. Businesses also want to put the soft skills of new recruits to good use to increase revenue in customer-centric environments. Before a dispatch happens, a problem is identified, the part that’s required is procured, and cheat sheets on how to resolve the issue are readily sent to the field technician’s device. Having an automatic problem and solution resolution with cheat sheets on how to replace a part allows staff to focus on the customer rather than figuring out how to solve the problem. 4. The business model is evolving to a “pay as you go” model: As companies explore this model, the only front end to the customer is field service. With the new model, companies are forced to have an efficient way for incidents to be automated. As the business model evolves, there is also a servicing model evolution with democratized field service. As of this article, there are around 9,000 data science engineer jobs versus 34,000 field service engineer jobs posted on LinkedIn worldwide. If your company is considering automating aspects of its field service operations, here are a few tips to consider to ensure it’s a successful endeavor: Understand the evolution of data within your organization. Make sure you have the relevant data for the business problem you are trying to solve. Get agreement on the usage of automation from all stakeholders. The last thing you want is to implement something that has organizational pushback in regard to adoption. One of the best ways to increase adoption is via a value calculator. Changes in the field service business environment warrant new technology to help take it to the next level. In the next article, we will discuss the long-term benefits and value calculation/return on investment (ROI) of solutions that address the automated service world. Learn more, 10 Ways To Improve Time To Resolution Innovative B2B Automated Self-Service Technologies

This is a year of articles and conferences about artificial intelligence and machine learning (ML). Even though ML has made strides in the consumer world, it has only started to show value in enterprises. Many companies are still trying to figure out what value they can harness, how much data they have, how much data is enough, how to start or pick a project, how to measure return on investment (ROI) and how to use ML as a tool in their digital transformations. I plan to write articles that address each of these areas. It is important to understand the evolution of data within the enterprise in order to understand the true value of machine learning. When the internet started, the focus was on transaction response time within online transaction processing (OLTP) systems. These systems powered websites, and the focus was on improving reliability, availability, and scalability (RAS). As transactions grew on e-commerce sites, we wanted to know who was using the system, from where, what they were doing, for how long, and most importantly how we could offer more value to existing customers and bring in new customers. This brought a whole slew of applications around business intelligence and reporting or analytics engines. We went into a world of user-generated content -- both structured and unstructured through social media and smart devices including mobile, voice, and video content. To garner value from this content, new types of analytics engines were needed. The growth in computing power along with a lot of research to create sophisticated algorithmic tools has allowed for the ability to fundamentally change what a platform is all about. Traditionally, a platform was used to address an enterprise process workflow -- human resources (HR), finance, manufacturing, etc. They are what we categorize as enterprise resource planning (ERP), customer relationship management (CRM), human capital management (HCM), functional setup manager (FSM), information technology operations (ITOps), etc. The data generated by these workflows were then analyzed using analytics or business intelligence applications to make further modifications to workflow. These workflow applications were customized as the data warranted any changes in the workflow. When intelligence becomes the new platform, data from these traditional applications will be used to determine the workflow and actions of organizations. The workflow actions will be passed on to the traditional applications or directly to the people or system that will perform the actions. These new systems of intelligence will emerge and will force existing workflow applications to change to be end-user targeted. We are already seeing a trend where AI platforms are slowly becoming a playground for new intelligent applications. More importantly, because open-source intelligent platforms in this area are as rich as enterprise platforms, we are also noticing new generations of applications. These applications prescribe specific actions that can be taken in their field of expertise -- HR recruitment, personality analysis, service optimization, sales upsell, etc. Take commercial travel as an example. Early websites pointed out the lowest flight price available from point A to point B. Then came websites that compared other websites and aggregated results. Now, we have the ability to choose a budget and have a website or app suggest destinations that fit that budget or better days for us to travel. Our decision has transformed from just getting data and then taking action to an action being recommended to us that we can then decide whether or not we want to pursue. This works best when we have clearly established our goals (in this example it is the budget). This same level of intelligence has not come into enterprise applications. In a way, they are lagging behind. From what we have seen, it is not a lack of data causing this lag, but issues with culture and defining our goals. I will discuss more how enterprises can choose what use cases are best for implementing AI, how much data is good enough when to build instead of buy, and how to measure return on investment in subsequent articles. Learn more, 10 Ways To Improve Customer Services With AI Do You Have Enough Data For Machine Learning?

For large Enterprises, Data Variety trumps volume while looking for insights. More enterprises are inclined to make Big decisions with data. To do this efficiently, applications need to be intelligent. They should have the ability to: Accumulate data Analyze data Act based on data The business processes in Enterprise applications so far have been workflow-based. The data is gathered from the workflow to then be utilized to do reporting. The future of Enterprise applications will be based on Intelligence rather than workflow and Prescriptive rather than reporting Essentially, Intelligence will drive the workflow and Prescriptive actions will be based on predictions. This is where the top-tier AI systems stop. There are many tools coming up in the market branding themselves as AI or expert tools. The Customer support market is especially proliferated with Robotic Process Automation (RPA), Chatbots and automation tools. Companies are at a loss on how to evaluate them and know when to use what. Their focus is on Return On Investment (ROI) and the tools don’t specifically cater to the customer needs. This article highlights how to think beyond buzz words and articulates the differences in technology to enable picking the right solution for your support/service teams. Why Complex Systems Need New Thinking In Customer Support When products are new, come out as the most simple, why do we need to support innovation? Complexity is increasing exponentially, complex systems fail in complex ways, and complex failures need dynamic and adaptive responses. Complex systems contain mixtures of latent issues: A system’s complexity means that it is impossible for it not to contain multiple flaws. Most flaws are insufficient to cause significant issues. They are regarded as minor factors during operations. The flaws change constantly, due to evolving technology and organizational factors, and even as a result of efforts to resolve existing flaws. Complex systems run as broken: Redundancies in the system, and the ongoing expertise and effort of humans, ensure that the system continues to function, sometimes in degraded mode. Issues have multiple causes, not a single root cause. Every single flaw is insufficient to cause a major issue. It is the linking of multiple faults that creates the circumstances required for a significant failure. Innovate Customer Support From Ground Up! The level of complexity has increased not just because products are complex but also the environment and usage are complex. In this ever-increasing complexity, support teams are focused on three main goals: Solve today’s problem Make sure it doesn’t happen again — Reduce customer escalations article talks about aligning customer expectation, empowering front line, prioritize which customers to focus on, communicate Predict problems before they happen Whether it is self-service, community, agent assist, or auto-support, problem prevention and problem elimination are the core. As companies look at ways to do the above, most of them believe incorporating AI will help them. They believe AI to be: Automating repeated tasks. This is called Automation or Robotic Process Automation (RPA) for short. In this method, you program the repeated tasks and let the computer execute them. Rules-based and statistics-based tools are an expansion to traditional Business Intelligence (BI) engines. Machine Learning algorithms that identify anomalies and patterns within data in the enterprise Identify relevance using Natural Language Processing (NLP) Ideally, a tool can be called a true AI tool when it utilizes all of the above along with: Extending NLP to include Content and Intent along with relevance. Extending Machine Learning to provide prescriptive actions. Utilize Tribal knowledge from the users. Tribal Knowledge: Beyond AI in support Intuition helps only when it is in the area of expertise — Originals by Adam Grant Value of data as a function of a number of observations in an ML domain, here machine vision. Each vertical line represents the same complexity of a particular problem. Credit: Nicole Immorlica, Microsoft Research. The key to a successful AI journey incorporates utilizing knowledge intelligence to improve support experience from humans, getting the feedback loop, and incorporating it into learning. AI is good for complex systems and dimensions of data. Feedback procured from humans on prescriptive actions provided by the tool adds to another dimension. At Ascendo, we believe one of the most important dimensions is every interaction the users are having with the tool. These interactions become critical learning opportunities. A learning engine knows what an agent is looking for, what the data suggests as actions, what the agent decides and how the agent interacts with the tool. It has the ability to not just learn from data, not just learn from feedback from its users but also from user interactions. What we have seen is when we include tribal knowledge from interactions, the prescriptive actions are more optimal than just utilizing data. Essentially, we have expanded the dimensions of decision-making. Top 10% of AI tool dimensions: Where AI tools need to be? When someone says AI tool, do ask what do they mean by AI. Following are some leading questions: 1. What role does artificial intelligence play in this tool to help customer support teams? 2. How are relevance and feedback enhancing the Machine Learning dimensions of the tool? 3. Can the tool go beyond these dimensions to identify intent and learn from interactions? Learn more, Knowledge Intelligence in Customer Service Use AI to Drive Service Improvements

How does your business treat its customers? As a business leader, are you thinking about customer experience every step of the way and investing in improving your touchpoints with the customer, or do you look at customer experience as an operations cost that must be ruthlessly shrunk? Is there a happy medium somewhere, and what would that look like? I was once a consultant for a corporation where the leaders behaved as though entertaining any requests from their customers would only serve to reduce their company's profits. Needless to say, that behaviour did not work out very well for them in the long run. What is Customer Experience? There is a difference between customer service, customer support and customer experience. As Blake Morgan highlighted in a Forbes article, customer experience is usually defined as the overall end-to-end experience that your customer has with your brand, starting from how do you approach the customer to their experience using your services or offers. Customer service is usually your customer's experience after they have purchased your products, services or offers — particularly if they should have questions or any difficulties. Customer support or customer care, on the other hand, is sometimes used to focus on how your customer interacts with you while they are choosing the product or service. Customer experience directly impacts your brand value and is often measured by your net promoter score. Customer experience is not just about customer satisfaction but how your brand itself is perceived. Gartner, Inc. noted that former Mercedes-Benz USA President and CEO Steve Cannon once referred to customer experience as "the new marketing" in terms of communicating your brand. As competition increases and buyers have more choice and power, customer experience becomes a critical competitive advantage. According to Gartner, more and more companies will compete mostly based on customer experience. Are you willing to pay for better service? Does it depend on what you are buying? A PwC study found that 42% of customers would pay for a better customer experience and that 73% believe a good customer experience is important when it comes to their decision on making a purchase. On the other hand, there are some leaders who find from their own practical experience that their customers only care about getting the best price for the product — even though research says otherwise. This may well correlate to the nature of the product you are selling or even the amount of the premium that you are expecting your customer to pay. However, there is variation here. The customer experience premium for expensive communications infrastructure, diagnostic tools or luxury products may matter more than the customer experience for bulk commodities. How does your company account for your customer experience spending? Do you treat customer experience as part of every product's cost so that you amortize the cost of customer experience, including customer service, over the cost of every product and include it as part of your cost of goods sold (COGS)? Is it part of your customer acquisition and maintenance costs, thus your sales and marketing (S&M) costs? Or do you view it as an investment — an expense in improving the overall product experience that yields returns by virtue of directly increasing a customer's willingness to pay or choose your product? There is continuing debate on this topic, and of course, there is no one-size-fits-all guidance. As Power Integrations board member Anita Ganti wrote, the answer depends on what customer experience does for the business and at what point in your product development or customer engagement process you begin your spending and generate value. A poll conducted by Gainsight offered similar findings. If we create a culture to treat customer experience as an investment, the next step is to ask the following questions: • How do I improve my customer experience? • Can technology help me deliver improved customer outcomes while reducing costs? • How can I leverage the insights from my customer-facing team? In part two of this series, we will further explore how we can improve customer experience by using technology and processes that will enable taking the leap. Learn more, Role of Customer Support Team in Engagement and Churn Knowledge Intelligence in Customer Service