Businesses these days receive hundreds and even thousands of customer queries daily. For any customer service representative, it becomes tremendously difficult to keep track of these issues, specifically because of the three Vs Volume Velocity and Variety With inconsistent similarities between large amounts of incoming data along with the frequency of product updates, it adds exponential complexity to the process of unearthing trends in the data. This data can be created from various data sources including customer-created tickets, service requests, bots, customer reviews, case objects from different CRMs, help articles, or even FAQs. While these datasets share the same ground of belonging to customer interactions , they all can have extreme differences in terms of unearthing actual actions to be derived from them. At Ascendo, we call these “Interactions”. What is common across these interactions is that they deal with symptoms/problems/ that a customer needs. Each of them needs an understanding of what the “ root cause ” of the request is. Then the root cause should be mapped to the relevant solution/knowledge to surface it back to the customer. At Ascendo we believe in Resolution instead of Deflection Resolution instead of just opening a ticket Resolution before Escalation Ascendo takes you on a journey to find out what you have been missing! For very raw data, Ascendo generates information, which in turn is converted to knowledge. This knowledge comes in the form of problems, symptoms, root causes, and solutions. And this knowledge comes to you in the simplest of forms. All you have to do is swing the magic wand and get all the insights that can make your experience with your end customers fantastic. Download the full whitepaper to read more on this...

“ It takes a village to raise a child ” is an African proverb that means an entire community of people must interact with children for those children to grow in a safe and healthy environment. (Source: Wikipedia). This proverb is not just true for raising a kid but also applies in the business context to grow any organization in a meaningful way. Customer Service is no different. It takes various stakeholders to work together to make their products and offerings service-ready and service capable. Ascendo has been razor sharp focused on the customer support area and is continuously getting enhanced to understand the unique needs of the various stakeholders and address their challenges. Let us take a look at the various stakeholders and how they think about their functions and how they are making their customer journeys into success stories. Support Delivery (Agents): Support teams want quick mechanisms to have their customers get self-help first. With improved technology bandwidth, a good number of customers first perform either a search or ask a chatbot to get the answers. Ascendo through its contextualization and correlation engines combined with its self-learning capabilities immediately identifies the context and the intent behind the search queries and chatbot questions and finds solutions from the in-depth knowledge it has garnered from within the support organization. Because of this, the “self-help” customers inherently feel better about appropriate or relevant solutions being suggested. The solutions become more meaningful. Support teams want to seamlessly get agents to help their customers when self-help may not be preferred or sufficient. Ascendo has the knack to identify if end customers require actual live help with the agents. Ascendo can automatically offer and transition the customer to get live help from one of the agents. Support teams want to make sure agents are getting the needed help to solve the issues. Ascendo helps the agents so they exactly know if the previously tried solutions did (or did not) work. Ascendo combines the “context” behind the customer questions and the tried solutions to further recommend better solutions to the agent. In Agent Assist mode, Ascendo looks into prior cases of similar nature and additional knowledge sources that are more reserved for internal audiences within the company and provides recommendations to the agents. This enables providing help and suggestions even for one-off complex issues as the engine mimics similar behavior. Support teams want to make sure agents are onboarded fast and know the expert to connect with. Before the “remote” workforce model, many agents were collocated and had the luxury of “swinging” their chairs and talking to their colleagues to get help. There is an inherent challenge in that agent is “supposed to know who to go to for help”. With an increased remote workforce that has become a bit harder. Also, knowing the expert has become a unique trait of agents rather than corporate knowledge. Even in a colocation scenario, agents tend to ask their neighbor “hey, do you know the answer for this?” and not necessarily the true expert that might know the answer in the back of their hand. This is where Ascendo comes into help. Ascendo has a way to automatically identify the “experts” based on how prior similar issues are solved, how many, and how effective they were. Ascendo uses its own unique modeling technique to derive the expert by first understanding the context in which the question is being asked, knowing the intent, and quickly mapping the issues that were similar to being asked, and detecting the experts based on how well they solved the issues. Ascendo knows the difference between quantity and quality. The modeling technique uses customer satisfaction on the prior solutions to improve the ability to determine the expert. Support Leaders If you ask Support Leaders what keeps them up at night, it would be delivering a fantastic support experience at an optimized P&L. Depending on the type of company, onboarding a support engineer can take anywhere from 6 weeks to as much as 12 months. This does not even include initial shadow/review time spent on the new support engineers. Support Leaders not only about the cost of onboarding but also the opportunity cost of the time. Ascendo is proven to help in reducing the onboarding time due to its inherent nature of being an “experts in back pocket” model. Engineers can easily learn the solutions by querying within Ascendo and that reduces the time and the areas they need to look to provide solutions to customers. Another issue that Support Leaders grapple with is customer escalations. Once customer escalates , they do all they can including involving the best resources, being available for the customers, potentially offering new concessions, and other benefits to keep the customers happy. They are being forced to become reactive and counterintuitive to them wanting to be in charge and lead. They can rely on Ascendo on two fronts: Ascendo predicts potential escalations before they occur and Ascendo predicts the sentiment of the customer using hidden signals and communications from the customer users. Ascendo does this in real-time and alerts the support agents so they can pay even more close attention to the customer's problems. This type of model allows the customer leaders to switch their engagement with their customers from being reactive to proactive and they can regain their advantageous position of being in drive to realize the outcome – delightful customers. Support leaders are seeking to understand hidden trending issues before they become serious. Given the nature of the Ascendo modeling techniques, it can group or cluster incoming issues based on context, intent, and symptoms. No manual effort is needed from the support team to involve multiple team members to comb through the tickets to identify the top issues. Ascendo knows the top issues could change over time and constantly looks at them and provide the support leaders early guidance on the top issues. Knowing top issues also allows the Support Leaders to develop appropriate solutions: Do they need to make fundamental changes in the product? Do they need to train their engineers more? Do they need enrich their knowledge? The early alerts on top issues also allow the Support Leaders to focus their resources on the important things. They can assign fewer resources to focus on issues that matter most to their customers. Support Leaders can also empower their support delivery teams to increase self-service and auto resolution of many routines and/or high-volume customer questions. Instead of support agents needing to look at every issue, they can review Ascendo recommendations and use that to address their backlogs more effectively including auto-answering with possible solutions. The advantage is two-fold: Their customers get solutions quickly and Their agents' productivity increases. Field Engineers Field engineers/Field Techs come into play when the company makes hardware / electro-mechanical type of products or make complex products that require a field person or a professional engineer to work with them. Companies are always looking to reduce both the time and the number of trips the engineers spend to solve issues in the field. Customers also want a quick turnaround – they want the tech to come and complete the work quickly instead of needing to come multiple times. Ascendo can provide a game plan using its arsenal of solutions: It provides the optimum predictions on the root cause The area of fault and Recommendation on parts, if such is needed. Field engineers can use this game plan and are much better prepared before they even visit the customer. They have a fair knowledge of what the problem might be and also can carry the parts. The diagnostics become simpler and no more need to come back again for a part. The entire resolution could be done in one visit and in less time. Such savings in labor and travel is significant. Their customers are also much happier as they get their systems back up and running optimally in less time. This win-win is possible because of Ascendo’s exhaustive self-learning capabilities. Logistics Support teams that need to have spare parts face the unique challenge of needing to stock spare parts in one or more locations (depots) to satisfy support contract commitments they have with their customers. Depending on the mission-critical nature of their products, they may be offering various service levels such as 4-hour replacement, 8-hour replacement, or Next Business Day (NBD). Given their customers are geographically disbursed, support teams may have to stock spare parts in more than one distribution center and depots. Global companies, may also take into consideration specific country custom rules when deciding the location and quantity of their spare parts. Ascendo can use various information such as the field failure characteristics, customer install base growth trend, specific consumption pattern at depot-part level, and type of support contracts, and recommend the spare parts stocking requirements. Using Ascendo, the logistics team can optimize their spare plan in a much more effective way. Ascendo works in conjunction with the support teams' ERP system and the planners can use Ascendo recommendations to determine their reorder point (ROP) at the spare part-location level. Ascendo uses the prediction logic and provides customer SLA risk and the logistics team leaders can use that to take proactive actions. Furthermore, Ascendo can detect the change in install base movement and the change in the companies third part logistics team locations and provide proactive alerts for the logistics team to better plan their spare parts move as well as procurement. Support Operations Support Operation teams are a glue to connect people, processes, and technology to the needs of the various support delivery teams. They are incentivized to think about the future and create practical paths to connect the dots. Given the complex nature of the global service teams, Support Operations is looking to use AI/ML-based solutions that work with their existing deployed systems and artifacts and elevate them to the next level. Support Operations are leveraging solutions like Ascendo because Ascendo automatically learns them from their existing data. Ascendo has the needed connectors to work with their CRM systems and other knowledge/help articles and further enriches them in a meaningful way. The Setup process within Ascendo being straightforward helps with the quick implementation. The Support Operations team is also looking to deploy solutions that are value-based, and they are wanting to move away from user-based models to value-driven models. Given the way Ascendo can be easily consumed and deployed and the pricing model based on value realization, it makes it easier for the Support Operations team to try out and deploy Ascendo solutions. Summary Running a support organization is hard . The products are changing, customer demands are increasing, processes need to get adjusted, people are changing, and the need to constantly improve customer experience and support efficiency only keeps getting bigger and more important. It takes more than a village and Ascendo is right there to help with its fundamental self-learning capabilities, fine-tuned for customer support. Learn more, Tips To Stay In Continuous Touch With Customers Ways To Improve Customer Services With AI

What Is Employee Design Debt in Customer Support? Employee design debt in customer support refers to the accumulation of problems or inefficiencies in the way that customer support is structured or carried out that can lead to increased difficulty or burden for employees. This can occur when processes, systems, or tools are not well-designed or are not kept up-to-date, leading to frustration and decreased productivity for employees. Common Causes of Employee Design Debt Some common causes of employee design debt in customer support include: Lack of clear processes or guidelines for handling customer inquiries or complaints Outdated or cumbersome tools or systems for managing customer interactions Insufficient training or support for employees Poor communication or coordination among team members Lack of resources or support to handle customer needs effectively These issues will lead to a ticket backlog . To address employee design debt in customer support, it may be necessary to review and update processes, invest in new or improved tools and systems, provide additional training and support for employees, and ensure that there is clear communication and coordination within the team. It can also be helpful to seek feedback from employees to understand their experiences and identify areas where improvements can be made. What Can Modern Support Service Enterprises Do About Employee Design Debt? There are several steps that enterprises can take to address employee design debt in customer support: Identify the sources of employee design debt: It is important to understand the root causes of the problems that are leading to employee design debt. This may involve conducting surveys or interviews with employees to gather feedback and identify specific issues that need to be addressed. Review and update processes and systems: Outdated or inefficient processes and systems can contribute to employee design debt. Enterprises should review their current processes and systems to identify areas that can be improved or streamlined. Provide training and support for employees: Ensuring that employees have the necessary knowledge, skills, and resources to do their job effectively can help to reduce employee design debt. This may involve providing training or support for new tools and systems or offering ongoing support and guidance to help employees stay up-to-date with changing customer needs and expectations. Invest in new or improved tools and systems: If the current tools and systems being used by customer support teams are not effective or efficient, it may be necessary to invest in new or improved technology like self-service tools . This could include investing in new customer relationship management (CRM) systems or implementing automation tools to help streamline processes. Foster a culture of continuous improvement: Encouraging a culture of continuous improvement can help to prevent employee design debt from accumulating. This may involve encouraging employees to share ideas and suggestions for improving processes and systems and actively seeking feedback from employees to identify areas for improvement. Tools That Will Help With Employee Design Debt There are several items in any tool that can help enterprises address employee design debt in customer support: The Central Repository of Customer Interactions: Digital systems can help to centralize customer data and provide a single, comprehensive view of customer interactions. This can make it easier for customer support teams to access the information they need and respond to customer inquiries more efficiently. AI systems like Ascendo take it further to standardize all forms of customer interactions into time series data to analyze and prioritize based on escalations, sentiment, urgency, and many other parameters. It enables support teams to not just become proactive but to help the entire Enterprise become customer-centric. Collaboration and communication: Tools like chat and messaging platforms, video conferencing software, and project management software can help customer support teams stay connected and coordinated, reducing the risk of miscommunications and improving overall efficiency. Ascendo AI goes further to share who is the best person to contact for any particular type of issue. Knowledge Intelligence: Knowledge management systems can help to provide customer support teams with quick access to the information and resources they need to resolve customer issues. This can include everything from product documentation and troubleshooting guides to customer service policies and procedures. Knowledge intelligence goes a step further to assess customer interactions using artificial intelligence and share where gaps in knowledge are for employee productivity, quality, or efficiency. We at Ascendo, can show you how we do all this and more! Automation: Automation can help to streamline processes and reduce the burden on customer support teams by automating routine tasks or handling low-level inquiries. This can include bots or other automated response systems that can handle basic customer inquiries without the need for human intervention. Let intelligence from Ascendo spell out possible actions based on real-time data so your team can spend their time making and following through on decisions. Training and support resources: Providing access to training and support resources can help customer support teams stay up-to-date with new products, processes, and tools, and be better equipped to handle customer inquiries. This could include online training courses, in-person workshops, or ongoing support and guidance from team leaders or subject matter experts. Ascendo can help bring support intelligence, knowledge intelligence, and automation. Contact us to see a demo.

What is Customer sentiment analysis? Customer sentiment analysis is the process of using natural language processing (NLP) algorithms to analyze customer feedback and comments, and measure the overall sentiment toward a company or its products and services. This can be done through surveys, social media posts, and other channels where customers provide feedback. The goal of customer sentiment analysis is to identify patterns and trends in customer feedback and use this information to improve the customer experience. This can involve identifying areas where customers are dissatisfied, and taking action to address their concerns, as well as recognizing areas where customers are particularly satisfied and looking for ways to replicate that success. Customer sentiment refers to the overall attitude or emotion that customers have towards a company or its products and services. This can be positive, negative, or neutral, and can be influenced by a variety of factors, such as the quality of the product or service, the level of customer support provided, and the overall brand experience. Customer sentiment is important because it can have a significant impact on a company's success. Positive sentiment can lead to increased customer loyalty and positive word-of-mouth recommendations, while negative sentiment can lead to customer churn and negative publicity. Companies can use tools like customer sentiment analysis to measure and monitor customer sentiment and take action to improve it. Value of Customer sentiment analysis Customer sentiment analysis can provide a number of valuable benefits to a company. Some of the main advantages of this approach include: Identifying customer needs and preferences: By analyzing customer feedback, companies can gain a better understanding of what customers want and need from their products and services. This information can be used to improve existing products and services and develop new ones that are better tailored to customer needs. Improving customer satisfaction: By identifying areas where customers are dissatisfied and taking action to address their concerns, companies can improve customer satisfaction. This can lead to higher customer retention and loyalty, as well as positive word-of-mouth recommendations. Identifying potential issues: By monitoring customer feedback on an ongoing basis, companies can identify potential issues before they become major problems. For example, if a large number of customers are reporting a problem with a product, this can be a sign that the product needs to be recalled or redesigned. Improving the customer experience: By using customer sentiment analysis to identify areas for improvement, companies can take steps to enhance the customer experience. This can involve making changes to products and services, as well as improving the customer support process. Overall, this can help companies build long-term relationships with their customers and drive business success. Tools that Derive Customer Sentiment Ascendo's most popular options for conducting customer sentiment analysis include: Text analysis software: This type of software uses Natural Language Processing (NLP) algorithms to automatically analyze customer feedback and comments, and identify patterns and trends in the data. This can be used to measure overall sentiment towards a company or its products and services, as well as identify specific issues and concerns that customers are raising. Customer feedback surveys: Surveys are a common method of collecting customer feedback and can be used to gather information on customer sentiment. Surveys can be conducted online or through other channels, such as email or over the phone, and can include questions that specifically ask about customer sentiment towards the company or its products and services. Interaction monitoring: B2B companies use many platforms for interacting with customers and gathering feedback. They can be searched through websites, and interactions via community, forums, Slack, teams, AI bot, email, phone, or CRM. These tools can be used to track mentions of the company and its products and services and analyze the sentiment of these mentions to identify trends and patterns. Driving value through customer sentiment Companies can drive value through customer sentiment by using tools like customer sentiment analysis to measure and monitor the overall attitude or emotion that customers have towards the company or its products and services. This can provide valuable insights into customer needs and preferences, and help companies improve their products and services to better meet those needs. For example, if customer sentiment analysis reveals that a large number of customers are dissatisfied with a particular product, the company can take steps to improve the product, such as redesigning it or adding new features. This can lead to increased customer satisfaction, which can drive business success through higher customer retention and loyalty, as well as positive word-of-mouth recommendations. Additionally, with the tools like Ascendo, by proactively addressing customer concerns and improving the customer experience, companies can improve their reputation and build trust with their customers. Learn more, Calculate Customer Service Index Boost Customer Service With Knowledge Intelligence

Every customer support team member is on a constant quest to satisfy every interaction with the customer to the fullest extent. They are on a constant lookout to try and minimize conflicts so it doesn't escalate. They keep an eye on something/anything that leaves customers agitated or frustrated. But escalations do happen. Some interactions do leave customers agitated or frustrated. It is a necessary byproduct to find unique feedback and perspectives and use it to improve the Support Experience. Here are the most important things to know about customer escalation management in day-to-day customer support. This whitepaper will go through the deeper aspects of Escalation management: What is Customer Escalation? Common Reasons For Escalations Types of Escalation Escalation Management Playbook Proactive Escalation Management A Brief Overview of Ascendo AI Think different in order to change the rules. By definition, if you don’t change the rules you aren’t a revolutionary, and if you don’t think different, you won’t change the rules. Download the full whitepaper to read more on this...

In today's fast-paced, data-driven world, it's crucial to have up-to-date information and take into account the human factor when it comes to improving services. However, human input can sometimes introduce inconsistencies in algorithms. That's why data cleaning is essential, enabling one to determine what to include, what to exclude, and where to focus efforts. When it comes to optimizing modern support services, various elements come into consideration, such as Customer service, Artificial intelligence (AI), Customer relationship management (CRM), Proactive support, and Experienced customer service . In this blog post, we'll explore how to utilize AI to drive service improvements, making support processes more efficient and enhancing the overall customer experience. Let's dive in! The data can help ease that out but we wanted to look at that in detail together as a group because AI is the expert in the data. How to develop those models and interpret the information that comes out of them? Then we need an AI company like Ascendo to be the expert on that to help point in the right direction. Effective internal change management and product feedback from service data are crucial in driving product efficiency and reducing costs within the medical device industry. AI systems can improve customer service, as well as provide valuable insights for device improvement and customer understanding of equipment maintenance. Why Do We Need an AI System? Service is a huge cost driver for medical device organizations across the globe. So it's a requirement, it's necessary. It provides a lot of value for customers as well. There's huge attention on how to capitalize on that value while also reducing the cost. And that's true for the companies themselves, but also customers and there's a lot of customers that do automated self-service on these pieces of equipment, and they want to be able to manage how to do that themselves. The question is, can they do it at the level that an organization does? Do they have that kind of knowledge and expertise within their group? The only way to do that is to provide them with the right data, right tools, and the right information to be able to service that equipment at the right level. And here, large organizations are very interested in Artificial Intelligence-based solutions for service because 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 feedback to improve devices. And also to help customers understand how to maintain their equipment better. Which also enhances customer experience. So Artificial intelligence is being used all over every industry. Elevate your support with Ascendo AI In organizations, many areas 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. For any organization, what comes to mind first and foremost is always returning on investment, and what is the potential financial benefit? If we look at some areas that can be potentially removed because the data shows that it is not important or valued, over time, it would save the organization a million dollars and that's every year, that's an annuity over time. It's not just about finance though. Service is all about customer experience , so organizations have to be able to prove that whatever change they make, first of all, it doesn't degrade the customer experience in any way or another quality and compliance. When you have access to service record data and can 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 we might need to pull the device out and then replace it, or somehow determine which devices are functioning at the right level and not. We can also avoid field actions, and narrow the scope of a field action if we could figure it out. We need to understand the human element also. Customer use patterns could be anything, 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 with one set of tools. There are so many variables and unfortunately, the tools to be able to assess all of them have been limited for organizations. So they have 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, these are ways that we could speed up all of those various categories. In this way, we can drive service improvements significantly on our own. Data-oriented ventures need to be in a position with present information and additionally cited a little bit about humans. Into data, human beings enter data, there will be some stage of algorithm inconsistencies and any must component into that stage of records cleaning to see which ones to take and which ones to omit and the place to emphasize, and all of that. It required shut collaboration between the AI group and then the scientific team. To apprehend the procedure and the tools and what's wished to be capable of telling, if we're seeing something a later was once associated with the reality that the PM wasn't done, or used to be unrelated? Now, 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? Data and design requirements for the future to improve, and here is the tool that Ascendo developing and putting together that could benefit organizations in that effort. We can say that a sustaining engineering piece is huge for a lot of companies. It's a Strain on R&D, resources, and investment, and it's necessary. But if there's a way to sort of provide better data around, the top opportunities, we should choose it. Because sometimes it gets hard, there are many items, and as a service organization, if you want to see improvements in the devices that are out there but R&D and sustaining engineering rightfully ask well, which ones are we going to go after? Better data help determine which issues require sustaining engineering resources, process improvements within the service, or device replacements. Using an AI tool like Ascendo provides real-time customer feedback, accelerating product improvements. Customer service, artificial intelligence, CRM, proactive support, and experienced customer service enhance both existing and new products. They also address firefighting scenarios. Support outsourcing can be utilized strategically.

In Modern Support Experience, one of the greatest challenges that industries face is to identify the Root Cause and Symptoms of the high volume of issues coming in at an incredible velocity. With the gathered data from multiple data sources and immensely varied information, it gets harder to identify and establish the latent causes of problems. Ascendo brings to you one of its most utilized and successful components, Auto Categorization, which is built on state-of-the-art Natural Language Processing techniques and Deep Learning algorithms. Understanding the Problem Data in the real world is very convoluted and keeps evolving with time. As products and tools continue to improve, the number of issues and types of issues also increase. These issues keep changing with product changes and can evolve into something similar to what was faced before or extremely unique to how the product has evolved. Almost every industry today suffers from exponential growth in issues. The complexity of issues keeps raising the graph of data points. This pattern hardly stabilizes with growth in product stack and integrations . Currently, support agents tend to spend more than 50% of their time evaluating the problems, let alone finding the right solutions. Historical information needs to be crystal clear to understand the data based on past information. Making past data expertly classified and immaculately accurate is a task that is extremely time-consuming and strenuous. It is not difficult just because of the enormity of the data, but also due to inconsistencies in human thinking. When issues are being described by the end customers, people describe problems in their own ways without adding any context to the components of those problems. Moreover, agents and other experts could think of multiple solutions, causes, and symptoms for the same problems which increases the complexity of normalizing your data, therefore introducing multiple ways to understand the same type of data points. The standard way of solving this problem is to make use of expert knowledge . This method introduces multiple points of consideration, making the available solution hard to be implemented: Thousands of historical data points are needed. Each data point needs to be filled in with expert knowledge and accurately defined. Even for a small support team, about 17.5 hours per week is spent manually classifying problems. Multiple people talk about the same issue differently! The list of problems keeps expanding as the product expands Possibility of multiple unknown issues Mapping new problems with the existing list of problems causes inaccurate classes of category assignment Inaccurate learning Manual work hurts getting the Voice of the customer feedback into the product in a timely manner Not understanding issues in real-time is an impediment to providing proactive support. Ascendo Recommendation Engine Ascendo aims to make the support experience as easy as possible for its customers. The solution has to be simple, smart, efficient, fully integrated with data sources, and most importantly requires little effort and time for users. The solution also has to bring expert knowledge into learning and has to be a continuous learning engine. The feedback loop could simply confirm and enhance automatically created root causes that groups multiple problems into their own bucket. Download the full whitepaper to read more on this.

Businesses these days receive hundreds and even thousands of customer queries daily. For any customer service representative, it becomes tremendously difficult to keep track of these issues, specifically because of the three Vs Volume Velocity and Variety With inconsistent similarities between large amounts of incoming data along with the frequency of product updates, it adds exponential complexity to the process of unearthing trends in the data. This data can be created from various data sources including customer-created tickets, service requests, bots, customer reviews, case objects from different CRMs, help articles, or even FAQs. While these datasets share the same ground of belonging to customer interactions, they all can have extreme differences in terms of unearthing actual actions to be derived from them. At Ascendo, we call these as “Interactions”. What is common across these interactions is that they deal with symptoms/problems/questions/advice that a customer needs. Each of them needs an understanding of what the “root-cause” of the request is. Then the root cause should be mapped to the relevant solution/knowledge to surface it back to the customer. We will be going into topics like: Semantic Inference - what is it? Real world Examples of Semantic Inference How does this help with Support time and effort, Product teams and Go-to-market teams How does an AI engine use the above? What help does this provide to an agent? How do AI systems comprehend data? What help does this do to CX leaders? What if you are starting out with no systems in place? To read more of this, please read the full whitepaper.

In a full support operations platform, the journey starts from the casual information exchange to self-service. In a way, this is the first step in the customer support journey and drives further levels of deeper engagement as the journey continues. Often, this is an afterthought and the focus is only on chatbots . To avoid early escalations and ensure a smooth support experience, it is vital to get familiar with the initial stage early on in the journey. At Ascendo , we want to learn from every customer interaction throughout this journey. We bring to you one of our most useful and advanced components, AI Search. For Ascendo, AI Search is a cognitive way to interact as compared to the conversational way of interactions using chat bots, which is equally important to the latter. We offer this choice to our customers and do believe that support experience encompasses providing customers choices so they can interact in any way they choose. Understanding the Problem Data in the real world is very convoluted and keeps evolving with time. As products and tools continue to improve, the number of issues and types of issues also increase. These issues keep changing with product changes and can evolve into something similar to what was faced before or extremely unique to how the product has evolved. Almost every industry today suffers from exponential growth in issues. The complexity of issues keeps raising the graph of data points. This pattern hardly stabilizes with growth in product stack and integrations. Currently, support agents tend to spend more than 50% of their time evaluating the problems, let alone finding the right solutions. Solutions can be generated from various Data Sources that spread around the business. The origin of these solutions can also be very different. This leads to the first major challenge in the support industry, that is, gathering knowledge from widespread information sources. The second challenge is to find the needle in the haystack. The time and efforts required to look for the solution among a million solutions continue to rise with each new solution added to the stack. Current solutions offer little to the customers as they have many obstacles that come their way. Some of them include: Too many Data Marts and Warehouses to be managed Solutions keep evolving and often become outdated Current Search Solutions are key-words focused and miss the context and intent behind the problem Different Solutions can point to the same problem, but are often considered to be different - hence the knowledge stacks keep increasing unnecessarily It is a difficult task to make the knowledge available to the end customers There is no guidance for the agents to dive deeper into the solutions Ascendo Search Engine Ascendo aims to make the support experience as easy as possible for its customers. The solution has to be simple, smart, efficient, fully integrated with data sources, and most importantly requires little effort and time for users. The solution also has to bring expert knowledge into learning and has to be a continuous learning engine. The feedback loop could simply confirm and enhance automatically created root causes that groups multiple problems into its own bucket. Ascendo provides a No-code and expert-enhanced solution that can be easily plugged into your website and is prediction ready! Download the full whitepaper to read more on this.

The Customer Effort Score (CES) is a metric used to measure the ease of the experience with customer service. It is based on the premise that customers are more likely to be satisfied with a company and have a positive view of it if they have a low-effort experience. What is Customer Effort Score? CES is typically calculated based on customer feedback, which can be collected through surveys or other methods. Companies can use CES to assess the effectiveness of their customer support processes and identify areas for improvement. How is the Customer Effort Score Calculated? The Customer Effort Score (CES) is typically calculated based on customer feedback, which can be collected through surveys or other methods. The CES is typically measured on a scale from 1 to 7, where a score of 1 indicates a very low-effort experience and a score of 7 indicates a very high-effort experience. To calculate CES, the scores from all of the customer responses are added together and divided by the total number of responses. This provides an average CES for the company or a specific product or service. It is important to note that the CES can be calculated for different time periods (e.g. monthly or quarterly), and can be compared to industry benchmarks or previous periods to assess performance. Why Should You Care About Customer Effort Score? You should care about the customer effort score (CES) because it is a valuable metric for measuring the ease of the experience with customer service. A low CES indicates that customers are having a positive, low-effort experience with your company, which can lead to increased customer satisfaction and loyalty. On the other hand, a high CES can indicate that customers are having a frustrating, high-effort experience, which can lead to dissatisfaction and potentially even customer churn. By tracking and improving your CES, you can ensure that your customers are having a positive experience and are more likely to remain loyal to your company. This can ultimately drive business success and improve your bottom line. What can you do with Customer Effort Scores? Once you have collected customer effort scores (CES), there are several things you can do with this data to improve the customer experience and drive business success. Some examples include: Identify areas for improvement By analyzing your CES data, you can identify areas where customers are having a high-effort experience, and take action to address these issues. This could involve making changes to your products and services, improving your customer support processes, or providing more information and resources to customers to help them have a low-effort experience. Prioritize resources By understanding which areas of the customer experience are having the biggest impact on your CES, you can prioritize your resources and efforts to address the most important issues. This can help you make the most of your resources and ensure that you are focusing on the areas that will have the biggest impact on customer satisfaction and loyalty. Benchmark against competitors By comparing your CES to that of your competitors, you can see how your company stacks up in terms of the ease of the customer experience. This can help you identify areas where you are doing well, as well as areas where you may need to improve in order to stay competitive. Monitor trends over time: By tracking your CES over time, you can monitor trends and identify changes in the experience with customer service. This can help you understand how your customer's needs and preferences are evolving, and adjust your strategy accordingly. Does the customer Effort Score show the complete value of Customer Service? Customer Effort Score (CES) is not a measure of the value that a company provides to its customers. CES is a measure of how much effort a customer has to put into interacting with a company to get their issue resolved. It is used to gauge the effectiveness of a company's customer service and to identify areas where they can improve. There are several metrics that are used in conjunction with the Customer Effort Score (CES) to measure customer satisfaction and the effectiveness of customer service. Some examples include: Net Promoter Score (NPS) This metric measures how likely a customer is to recommend a company's products or services to others. Customer Satisfaction Score (CSAT) This metric measures the overall satisfaction of a customer with a company's products or services. Customer Service Index (CSI) This metric measures the overall effectiveness of a company's customer service. Customer Retention Rate This metric measures the percentage of customers who continue to do business with a company over a given period. These are just a few examples of the many different metrics that can be used to evaluate customer satisfaction and the effectiveness of customer service. The specific metrics that a company chooses to use will depend on its unique needs and goals. Ascendo helps companies provide proactive customer support and automated self-services that can elevate your support. To know more, contact us. Learn more, Customer Service Index How to Use AI to Drive Service Improvements?