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One of the most common issues that customer service teams have is maintaining communication with customers. Working with customer service teams we’ve found that mid-touch and high-touch customers tend to stop reaching out to customer service teams after onboarding. So what tips can we use to re-establish communication and avoid customer churn? In general, SaaS companies have two types of pricing models, freemium and tiered pricing. Regardless of the pricing model customers sign up for, SaaS companies aim to provide the best support experience for all their customers. However, ensuring that all customers receive high-quality service can be difficult, especially when there are differences in size and the level of service they pay for. To make sure that all customers receive top-notch service, customer service teams can take advantage of technology and processes that can be used for all types of customers. For instance, the number of unresponsive customers at the freemium level signifies that the customer has not retained interest or value from the product. Further, non-touch engagement levels with paid customers may signal issues with customer churn. Fortunately, we have created playbooks for customer service teams to use in these situations: Building Multiple Levels of Relationships One of the most effective ways to improve customer communication and the feedback loop is by fostering multiple relationships between the customer service team and the customer. However, some relationships are more important than others, and finding the right people is crucial to mitigating churn and improving customer satisfaction. These are the 3 relationships customer service teams should focus on fostering. 1. Customer Service Manager to Customer Service Manager Customer Service Managers are the people in charge of understanding and perfecting the user experience. As a Customer Service Manager, you should be reaching out to your counterpart on the client’s side. Opening this channel of communication will ensure that your client can feel comfortable reporting any issues or requests that may prevent the client from leaving in the future. You always want to build trust and transparent communication channels with the client. 2. Support Admins and Heavy Users Arguably the most important people to build relationships with are the Admins and Heavy Users of your software. These people will be your champions in client meetings and can ultimately decide whether your client stays or goes. If the people in charge of the budget or the people that use your software the most aren’t satisfied, there is little chance they will stay on. Not only does good communication with these stakeholders mitigate churn, but they also are the best people to ask for product improvements and feedback. As the main stakeholders, these clients generally have an in-depth understanding of your product and which areas need the most improvement. Remember, when you have good transparent communication with your clients, that can become an incredible resource. 3. Marketing to Marketing Another area that customer service teams should focus on creating relationships is with client marketing departments. Because marketing departments are in charge of outward communications, this is the best opportunity for customer service teams to collaborate on events, reviews, videos, and training. Marketing teams are also a great target for clients who are likely to become heavy users when managed correctly by the customer service team. Tips To Stay Continuous Touch With Customers Another great strategy to ensure consistent, transparent customer communication is to set up regular touchpoints with the customer. Regular touchpoints such as Quarterly Business Reviews (QBRs) are a value-add that most customers will usually agree to. Hosting QBRs and other recurring touchpoints with main stakeholders will provide customer service teams with plenty of opportunities to gain feedback, understand client usage, and review customer usage as a whole. When conducting QBRs or continuous touch with customers, here are a few items customer service teams can focus on. 1. Joint Goals Establishing joint goals is a great way to assess progress over time. While joint goals can be added or changed over time, they provide a foundation for what are the customer’s expectations. 2. Identify Open Items that Require Higher Visibility Every touchpoint should include a time when clients can identify and discuss items that have recently become important. This can include SLAs, new features, and other items that need to be addressed. Make sure you are transparent with items that did not go well or are not expected to go well. Transparency in features that cannot be fixed, reasons behind escalations, and missed SLAs do need to be summarized. Make sure to share mistakes, plans to fix or avoid in the future is shared with the team so the customer feels that you have their success behind their back. Be proactive whenever possible. 3. Open the Feedback Loop Your customers are your best R&D asset because they use your product in the real world compared to in-house developers. By asking for feedback, customer service teams can build trust with their clients while gaining valuable information that can improve the product overall. We look at the sentiment of each interaction and bubble up with excitement! Always remember to encourage your clients to share any feedback and thank them when they do! 4. Summary of Product Updates It’s always good practice to share with your customers any new features that may be coming down the pipeline. This reminds customers that there is more value to be added to your product and gives customer service teams a chance to gain initial feedback on the upcoming changes. This is also a great opportunity to test out any new marketing for the new product or features by sharing videos, photos, or even demos with the client. 5. Outline New Collaborative Opportunities Discussing collaboration opportunities with clients should be done at least on an annual basis. Joining forces in activities like marketing, sales, and more can be a great way to build relationships and create client dependency on your product. 6. Drive Actions for Next Touchpoint The last item on every touchpoint should be setting up the next touchpoint. Customer service teams should summarize action items and the agenda for the next touchpoint. Customer service teams should get clients to agree to the action items, so everyone is on the same page. Sending out a page memo that summarizes the highlights of the touchpoint and the agenda for the next touchpoint will ensure expectations are understood by all parties. Complete the Circle Bring in other representatives from the Product, Marketing, and Infrastructure teams. Be ready to show that customer that you are pulling in resources across your company to make them successful. At Ascendo, we aim to improve customer relationships and make constant, transparent communication easy for all parties. An AI-driven tool brings out all of the above collaborative touchpoints so customer teams can focus on what they do best – building relationships! Learn more, 10 Ways To Improve Customer Services With AI Use AI to Drive Service Improvements

Post sales service delivered 45% of companies’ gross profits on only 24% of their total revenues (Source: HBR) by the end of the first decade of this century, and since then subscription revenues have consistently grown nearly five times faster than S&P 500 Industry benchmarks over the last decade. The distinction between product and post-sales service sales is slowly blurring. To maximize revenue and profit, companies must focus on the customer experience, on how they can use and get value, and the experience they get from the customer support and success teams. Reacting to service requests is a huge expense for organizations. Service centers spend significant time just categorizing the reported symptom into a problem, and schedule more than 70% of the support engineers reactively because of a reported problem. Because of the stringent SLA, organizations are forced to find sub-optimal solutions for immediate recovery and are forced to come back again at least 30% of the time to fix the same problem. If the company has hard assets, the service organization stocks aftermarket parts inventory at multiple echelons just to support the response commitments to their end customers, but still, ends up facing stockouts and costly, last-minute shipments more than 10% of the time. These challenges have gone up even more with the recent supply chain constraints. Furthermore, if field service is involved, technician dispatch is based on a hierarchical model and unfortunately results in either deploying expensive resources for simpler problems or implementing sub-optimal fixes to the equipment. How Ascendo Helps? Ascendo provides a predictive Artificial Intelligence-based customer support solution to elevate the experience of customers and agents in every interaction. Ascendo applies advanced modeling to incoming customer questions and detects the context and intent behind the questions. Through that, it uses semantic inference to find the symptom, root cause, problem, and solution from across multiple solution articles. Ascendo also predicts customer sentiment (with sentimental analysis) and predicts customer-specific and broader problem trends to avoid future problems. Ascendo connects with the company’s service CRM, installed base, and internal knowledge base to provide prescriptive recommendations within the existing service workflows. For companies that make hard assets, Ascendo has additional models to help with the logistics and field service teams. By looking at the problem and failure trend, Ascendo also helps provide predictive part demand recommendations for the logistics team. For companies that include hard assets in the field, Ascendo provides the game plan in terms of fixed procedures and part recommendations before a technician visits the customer site. Support Agents/Engineers 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-service 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. 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 unique modeling technique to derive the expert by first understanding the context in which the question is being asked, knowing the intent, 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 with the best customer services at an optimized cost structure. 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 a 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. First: Ascendo predicts potential escalations before they occur and second: Ascendo provides proactive support by predicting 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 the 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 the incoming issues based on the 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 provides 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? or do they need to enrich their knowledge? The early alerts on top issues also allow the Support Leaders to focus their resources on 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 twofold: their customers get solutions quickly and their agents' productivity increases. Field Engineers Field engineers/Field Techs come into play when the company makes hardware/electromechanical type of products or makes 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 the fault, and recommendations on parts if such is needed. Field engineers can use this information 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 a saving 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. 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 value-based solutions, 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. 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 dispersed, 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 the 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's recommendation 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 company's third-party logistics team locations and provide proactive alerts for the logistics team to better plan their spare parts move as well as procurement. Summary Ascendo is razor-sharp and focused on the customer support experience. Using the fundamental semantic inference models combined with self-learning models, Ascendo addresses every step in the customer support lifecycle journey. Ascendo is providing multiple returns on investment for the customers and a few customers are now beginning to embed Ascendo in their new revenue models. The pact areas can be summarized into the following: Improve customer experience and Churn (Faster fixes, Less downtime, Higher Satisfaction) Service efficiency (Increase workforce productivity, Increase in incidents handled by a support member) Cost reduction (Fewer resources/Reduce multiple visits/Fewer dispatches/Less inventory) Improve Product Feedback Ascendo fundamentally turns companies to become proactive and predictive in the way they offer support services to their customers. Learn more, Escalation Management Guide For Proactive Support Ways To Improve Customer Services With AI

Customer Service and Support Experience is not a buzzword - it is the DNA of a company's success. With the trend in “outcome based pricing” and “Subscription based Selling”, service experience is right in the center of keeping and growing customers. Customers walking into a copy shop and paying for just the copies getting printed was just a start instead of buying a copier. Today, medical device companies are looking to charge hospitals based on a number of surgeries being performed using their machine. A new electric car company just announced in August that they are thinking of selling their cars for a lower price and charging the buyers a per month battery fee. Customer Service is New Product Post sales service segment delivered 45% of companies’ gross profits on only 24% of their total revenues (Source: HBR) by the end of the first decade in this century, and since then subscription revenues have consistently grown nearly five times faster than S&P 500 Industry benchmarks over the last decade. The distinction between product and post-sales service sale is slowly blurring. To maximize revenue and profit, companies must focus on the customer experience and the experience they get from the customer support and success teams. Customer Service Challenges Reacting to customer service requests is a huge expense for organizations. Service centers spend significant time just categorizing the reported symptom into a problem, and schedule more than 70% of the customer support engineers reactively because of a reported problem. Because of the stringent SLA, organizations are forced to find sub-optimal solutions for immediate recovery and are forced to come back again at least 30% of the time to fix the same problem. If the company has hard assets, the customer service organization stocks aftermarket parts inventory at multiple echelons just to support the response commitments to their end customers, but still ends up facing stock outs and costly, last-minute shipments more than 10% of the time. These challenges have gone up even more with the recent supply chain constraints. Furthermore, if field service is involved, technician dispatch is based on a hierarchical model and unfortunately results in either deploying expensive resources for simpler problems or implementing sub-optimal fixes to the equipment. Ascendo Game Plan for Service Teams Ascendo provides a predictive Artificial Intelligence based customer service solution to elevate the experience of customers and agents in every interaction. Ascendo applies advanced modeling in incoming customer questions and detects the context and intent behind the questions. Through that it uses semantic inference to find the symptom, root cause, problem and solution from across multiple solution articles. Ascendo also predicts customer sentiment and predicts customer-specific and broader problem trends to avoid future problems. Ascendo connects with the company’s service CRM, installed base, and internal knowledge base to provide prescriptive recommendations within the existing service workflows. For companies that make hard assets, Ascendo has additional models to help with the logistics and field service teams. By looking at the problem and failure trend, Ascendo also helps provide predictive part demand recommendations for the logistics team. For companies that include hard assets in the field, Ascendo provides the game plan in terms of fix procedure and part recommendations before a technician visits the customer site. Learn more, Knowledge Intelligence in Customer Service Innovative B2B Automated Self-Service Technologies

Knowledge Management (KM) is the methodical management of information, data, and knowledge assets held by an organization to add value and achieve strategic goals and objectives. In simple words, it is the process of capturing, sharing, and successfully utilizing knowledge. Knowledge management has a variety of use cases, including employee onboarding, customer onboarding, customer service and self-service. These processes can be expedited and run efficiently with the help of knowledge sources. Also, there are knowledge management applications that enable field technicians to leverage knowledge and insights for better productivity and cost savings. Today, artificial intelligence (AI) has transformed the way organizations handle their knowledge management processes. Knowledge intelligence, i.e. AI-powered knowledge management, is a popular trend that will only become more pertinent over time as businesses come to understand the advantages of integrating it into their operations. Why Knowledge Intelligence in Customer Service? Agents are swamped Teams representing a major corporation with millions of customers will manage a greater volume of interactions. Customer service representatives are flooded with help requests, follow-up calls, emails to respond to, reports to process, and other tasks. Apart from this, rigid operational methods, old KM methods and broken internal processes make it harder for agents to satisfy customers. Hence it is key to invest in a modern, adaptive software tool. Issues are getting complex Customer support personnel deal with a variety of problems every day. While some are simple to solve, others are more challenging. Support agents are the face of the company and the impression they leave on customers as they work through complex problems has a significant bearing on how that company is seen and whether or not those customers will return. Therefore, it's essential that agents have the right tools when tackling customer issues. Customers want resolution fast One of the most illuminating results from the survey conducted by Dimensional Research is that “customer service experiences are judged more on the timeliness of the interaction than on its final outcome.” Sadly, after providing all the details to the agent, customers are forced to repeat the information when talking to the next person in the support team. Need to improve customer experience Making customers work too hard to find answers to their queries leads to a bad customer experience. Telling agents to go above and beyond for customers is likely to result in confusion, lost time and effort, and expensive giveaways. On the contrary, companies can improve customer experience and thereby create loyal customers by helping them find solutions quickly and easily. Advantages of Knowledge Intelligence in Customer Service All Data At One Place The core of knowledge intelligence is an information repository that houses data on customers, products, services, and frequent problems or complaints. With its help, decisions can be made more quickly and problems can be solved effectively because all the information is in one location. The knowledge source is a cloud-based solution that makes it easier than ever before to manage customer service data. Analyze & Act On Data Faster Organizations can quickly identify trends and patterns using knowledge intelligence. Support leaders no longer have to manually find out problem areas, internal issues and so on. AI-powered knowledge management systems provide real-time insights into how customers interact with the company. Automate Workflows Automation will simplify the process of carrying out repetitive tasks. Each time, it takes a lot of time to perform it manually. Although it may not appear to be much now, it will build up over time. AI allows the automation of more processes than ordinary automation. It enables support leaders to build workflows based on their own rules. This will cut costs, save time and improve efficiency. Strengthen Self-Service Tools AI-powered knowledge base systems allow businesses to automate responses to customer inquiries. A proactive and personalized AI bot enhances customer experience and reduces agent workload. Static knowledge is never enough in the world of customer service. By adding knowledge to the system time to time, it stays updated and serves useful for agents and customers alike. Deliver Better Customer Experiences Knowledge intelligence is a highly integrated and innovative solution that opens the door for improved customer experience. It provides a comprehensive set of features designed to make sense of the data and take action based on insights gained from analyzing it. It will thereby accelerate issue resolution and reduce customer effort. Improve Productivity & Reduce Costs According to a study of 1000 companies: The average response time by the customer support team is 7 hours and 4 minutes, but the top 5% of organizations respond in just 16 minutes. While the issue resolution time is 3 days and 10 hours on average, the top 5% of companies can solve problems in just 17 hours. On average, each support agent can handle 21 tickets per day. With the use of artificial intelligence, organizations can boost output while keeping costs down. Knowledge intelligence helps support organizations to become the cream of the crop as it optimizes their operations and internal data-sharing procedures. Ascendo Offers Knowledge Intelligence Ascendo helps companies provide proactive customer support with the power of artificial intelligence. With Ascendo's no-code integrations, support teams can link to many CRMs and other data sources and see all of the solutions listed together in one location. Consider the unique tips and techniques established by an employee to complete a task faster or more efficiently. This is important information that is lost when an employee leaves. Organizations that overlook such information jeopardize their overall performance by risking the loss of knowledge that only a few people possess. With the Ascendo AI engine, it's easier to structure tribal data and convert it into valuable knowledge. In a world of limitless possibilities, knowledge bases should incorporate the latest developments as well. Using Ascendo, support teams can update and improve knowledge articles as needed. Ascendo also helps identify how issues evolve over time, which ones are prevalent, and which ones require solutions. Learn more, Innovative B2B Self-Service Technologies Ways To Improve Time To Resolution

What is Customer Automated Self-Service? Customers can address their own issues without a support agent's assistance through a procedure known as customer self-service. Customers use self-service options to research and troubleshoot issues rather than working with a company's customer service representatives. This is an important feature to include in your company's customer service offering as it can provide your customers with quick and easy solutions. IVR (Interactive Voice Response), online forums, in-product self-help, AI bots, and knowledge base are some of the powerful self-service channels available to businesses. Why Provide Customer Self-Service? Customer self-service is quickly replacing traditional customer service methods. This is because self-service options provide customers with quicker solutions that they can find on their own time. Customers can use your company's resources to look up answers to simple support questions rather than picking up the phone and waiting on hold. Your customer service team, on the other hand, benefits from not having to handle repetitive or similar cases. This relieves stress on your incoming request queues and frees up more time for agents to solve complex or one-of-a-kind customer problems. Customers with more serious issues and immediate needs can now receive the attention they require because your agents are no longer required to spend time answering simple questions. Innovative B2B Automated Self-Service Technologies 1. A/B Testing One of the simplest ways to use an automated self-service approach is to run an A/B test. This means running two versions of a website or product with different features and seeing which one performs better. It's easy to do because you just need to make sure that each version has a unique URL. There are many tools available online to conduct A/B tests. The test results can then be used to perform data analysis and gather information about user activity on your website. Insights from A/B tests will help you make smart improvements to your business that are proven to increase customer experience. 2. Lead Generation Another simple example of a self-service approach is lead generation. If you're looking to generate leads, then you should consider using an automated self-service approach. Many online tools allow you to collect contact information and other details of visitors to your site. Based on that, leads are scored and the next action is determined. Knowing when and how your customers interact with your brand online allows you to create a 1:1 customer journey. 3. Customer Retention The third type of self-service approach is customer retention. Identify who and what with sentiment analysis. A straightforward query such as, "Am I answering your question?" or "Do you feel that I'm giving you the response you're seeking?" can start a conversation about what's not working properly. Questions like these prevent customers from simply disengaging before their issue is solved. By asking questions that dig just a bit deeper in real-time, customers are more likely to leave the interaction more satisfied. Artificial Intelligence looks at all interactions and calls out churn before something happens. 4. Customer Feedback Are you able to harness feedback and sentiment from every customer interaction? In general, companies focus on feedback from: Disgruntled customers Customers who are in touch Customers who fill in the survey With AI sentiment analysis, you can now focus not only on a section of customers but all customers and all interactions. AI-powered methods focus on pattern detection and Machine Learning algorithms to automatically categorize unstructured feedback. If done manually, this task can be tedious and time-consuming. You can easily find ways to work and perk up those weak areas of your business based on AI's efficient categorization of feedback. 5. Omnichannel Experience If you're looking for ways to improve your business, then you need to start thinking about how you can make yourself more accessible to your customers. One of the easiest ways to do this is by offering self-service options. These options allow customers to contact you directly without having to go through a middleman. The options can be provided to partners and distributors. Even though only phone and email was used before, now we see that B2B companies are using AI bot on the website, within the application, Slack, Teams, and WhatsApp to provide proactive support. 6. Knowledge Base On the Website Another self-service approach is Knowledge Base. It is a section of your website that assists customers in resolving common product issues and answering basic service inquiries. It includes organized documentation of your products and services, as well as articles that can assist customers in troubleshooting their issues. This way, your team can document and share detailed troubleshooting steps for common customer issues. FAQ section is a common example of a knowledge base in a website. 7. In-Product Help A chatbot can enable your customers to self-serve more efficiently by offering solutions to their issues and questions from your knowledge base. A community forum is a place where users can ask questions, get answers from other users, and search for previously asked questions. Companies can run a community forum as part of their knowledge base or as a separate section of their website. By hosting a conversation with your customers, you can connect with both satisfied and dissatisfied customers and get candid feedback about your product. B2B Customer Self-Service: How To Do It Right? It is complicated and challenging for B2B companies to be successful with customer self-service options, a feature that has been very productive for B2C companies. The key is to test your systems and processes across channels to make sure that whatever your customers must do in order to utilize your automated self-service tools is easy, straightforward, and quick. Your customers' problems are already complicated; the solutions shouldn't be either. No matter how much or what you automate, nothing can replace human interaction when your customer wants one. Hence, be accessible. You can plan for successful outcomes and prepare responses in advance by anticipating problems your customers may encounter. You can make everything available for customers to utilize as and when they need it, including your online FAQs, help videos, tutorials, manuals, and more. For more insights into customer support, check this out. Learn more, Knowledge Intelligence in Customer Service Anatomy of Customer Sentiment Analysis

A Chief Customer Officer (CCO) is a senior executive who is responsible for overseeing the customer experience and customer relationship management strategies within a company. The CCO's main goal is to ensure that the company is meeting the needs of its customers and that it is providing the best possible customer experience. This often involves working closely with other departments, such as marketing and sales, to develop and implement strategies for improving customer satisfaction and loyalty. One of the key responsibilities of a CCO is to manage customer data and use it to inform business decisions. This includes collecting, analyzing, and interpreting data on customer behavior and preferences, and using this information to develop targeted marketing and sales strategies. For example, a CCO might use customer data to identify trends and patterns in customer behavior, and then use this information to tailor the company's product offerings or communication with customers. Overall, the role of a Chief Customer Officer is to ensure that the company is focused on meeting the needs of its customers and providing the best possible customer experience. This often involves working closely with other departments to develop and implement strategies for improving customer satisfaction and loyalty and using customer data to inform business decisions. Why Is AI Needed in Customer Experience? AI can be useful for improving the customer Support experience in several ways. First, AI can be used to automate and streamline customer interactions, such as by providing personalized and instant responses to customer inquiries through chatbots. This can help reduce response times and improve the efficiency of customer service. Second, AI can be used to analyze customer data and provide insights that can help companies better understand their customers and their needs. For example, AI algorithms can be trained to identify patterns and trends in customer behavior, which can then be used to tailor the company's product offerings or communication with customers. This can help companies provide more personalized and relevant experiences for their customers. Third, AI can be used to automate and optimize key business processes, such as by analyzing customer feedback and identifying areas for improvement. This can help companies make faster and more informed decisions, and ultimately improve the overall customer experience. Overall, AI can be a valuable tool for improving the customer experience by providing personalized and efficient interactions, providing insights into customer behavior, and automating and optimizing key business processes. AI in Customer support Artificial intelligence (AI) is increasingly being used in customer support to improve the efficiency and effectiveness of service. AI-powered chatbots, for example, can handle a high volume of customer inquiries simultaneously, providing quick and accurate responses to common questions. This allows human customer support agents to focus on more complex issues that require a higher level of expertise and personalized attention. Additionally, AI can be used to analyze customer feedback and identify trends or common issues, which can help companies improve their products and services. Traditional Metrics of Chief Customer Officer There are several ways to measure the success of a Chief Customer Officer (CCO) in a company. Some common metrics include: Customer satisfaction: One of the most important indicators of a CCO's success is the level of customer satisfaction. This can be measured through customer surveys, where customers are asked to rate their experience with the company on a scale from 1 to 10. A high score on this metric indicates that the CCO is effectively managing the customer experience and addressing any issues or concerns. Retention rate: Another important metric to consider is the retention rate or the percentage of customers who continue to do business with the company over time. A high retention rate indicates that the CCO is successful in retaining customers and building long-term relationships with them. Net promoter score (NPS): The NPS is a measure of customer loyalty, where customers are asked how likely they are to recommend the company to others on a scale from 1 to 10. A high NPS score indicates that the CCO is effectively creating positive experiences for customers and building brand loyalty. Revenue and profitability: Ultimately, a CCO's success should be measured by the impact they have on the company's bottom line. This can be measured by looking at revenue and profitability over time and comparing them to industry benchmarks and previous periods. A CCO who is successful in driving revenue and improving profitability is likely doing a good job of managing the customer experience. New metrics of chief customer officer As data becomes more readily available and sophisticated tools are developed to analyze it, new metrics are emerging to measure customer experience. Some examples of data-driven metrics that are gaining popularity include: Customer journey mapping: This involves analyzing customer interactions and behaviors across all touch points with a company, from initial awareness to post-purchase support. This allows companies to identify areas where the customer experience can be improved and optimize the journey for maximum satisfaction. Sentiment analysis: This involves using natural language processing (NLP) algorithms to analyze customer feedback and comments and measure the overall sentiment towards a company or its products and services. This can help companies identify areas where customers are dissatisfied and take action to improve their experience. Customer lifetime value (CLV): CLV is a measure of the total value that a customer will generate for a company over the course of their relationship. This metric allows companies to prioritize their efforts and resources toward retaining and maximizing the value of their most valuable customers. Voice of the customer (VOC): VOC is a term used to describe the collective feedback and opinions of customers, which can be collected through surveys, focus groups, and other methods. Analyzing this feedback can provide valuable insights into customer needs and preferences, and help companies improve their products and services. Future of customer support The future of customer support is likely to be shaped by the continued advancement of technology and changing customer expectations. Some trends that are likely to impact the field of customer support include: Increased use of artificial intelligence (AI): AI is already being used in customer support to improve efficiency and accuracy, and this trend is likely to continue. AI-powered chatbots, for example, can handle a high volume of customer inquiries simultaneously, providing quick and accurate responses to common questions. Greater emphasis on personalized experiences: As customers become more empowered and have more choices, companies will need to focus on providing personalized experiences in order to stand out. This could involve using data and AI to tailor support experiences to individual customers and their specific needs. The rising importance of customer feedback: Customer feedback will continue to play a crucial role in shaping the customer support experience. Companies will need to be proactive in collecting and analyzing customer feedback and using it to continuously improve their products and services. Increased use of self-service: Customers are increasingly turning to self-service options, such as online FAQs and knowledge bases, to find answers to their questions. Companies will need to invest in these channels and make sure they are easy to use and provide accurate information in order to meet customer expectations. New Age Chief Customer Officer A "new age" Chief Customer Officer (CCO) is likely to be someone who is well-versed in the latest technologies and trends impacting the customer experience. They will have a deep understanding of data and analytics and be able to use these tools to drive business decisions and improve the customer experience. They will also be able to work closely with other departments, such as sales and marketing, to ensure that the needs of customers are being met across the entire organization. In addition, a new-age CCO will be proactive in seeking out customer feedback and using it to continuously improve products and services. The Future Role of the Chief Customer Officer It is difficult to predict the exact future role of the Chief Customer Officer (CCO), as it may evolve and change over time in response to developments in technology and the business environment. However, some potential trends and developments that could affect the role of the CCO include the following: The increasing importance of customer experience: As competition continues to increase and customers have more choices, the customer experience is likely to become even more important. Companies will need to focus on providing exceptional experiences for their customers in order to retain their loyalty and differentiate themselves from competitors. The CCO will play a key role in ensuring that the company is delivering high-quality customer experiences. The continued growth of AI and automation: The use of AI and automation is likely to continue to grow and become more widespread in the coming years. This could lead to the automation of many tasks and processes that are currently performed by humans, including some tasks that are currently the responsibility of the CCO. As a result, the CCO may need to adapt and evolve its role to focus on more strategic and high-level activities, such as developing and implementing customer experience strategies. The increased use of customer data: The amount of data that is collected about customers is likely to continue to grow, as companies increasingly use digital technologies to collect and analyze data on customer behavior. The CCO will need to be familiar with these technologies and have the skills to analyze and interpret this data in order to inform business decisions and improve the customer experience. Overall, the future role of the CCO is likely to continue to evolve and change, but they will remain a key player in ensuring that the company is providing exceptional customer experiences and using customer data to inform business decisions. Learn more, Customer Effort Score in Customer Service How to Calculate Customer Service Index?

What Is the Customer Service Index? The Customer Service Index (CSI) is a metric that measures the overall effectiveness of a company's customer service. It is often used as a way to gauge customer satisfaction with the support they receive from a company. The CSI can be calculated by surveying customers and asking them to rate their experience with the company's customer service on a scale, such as from 1 to 10. The results of the survey are then used to calculate an overall score for the company's customer service. This score can be used to identify areas where the company can improve and track changes in customer satisfaction over time. Data in Customer support to gather CSI There are several ways that customer support teams can gather data to calculate their Customer Service Index (CSI). Some common methods include: Surveying customers: Customer support teams can use surveys to gather feedback from customers on their experiences with the company's customer service. This can be done through online surveys, email surveys, or by asking customers to complete a survey after they have interacted with the customer support team. Monitoring customer interactions: Customer support teams can use tools such as call recording or chat logs to monitor customer interactions and gather data on the effectiveness of their customer service. Analyzing customer feedback: Customer support teams can use AI (Artificial Intelligence) or other analytical tools to analyze customer feedback from sources such as online reviews or social media posts. This can help them identify common themes or areas where customers are most likely to be satisfied or dissatisfied with the customer service they receive. Once the data has been gathered, customer support teams can use it to calculate their CSI and identify areas where they can improve their customer service. This can help to increase customer satisfaction and improve the overall effectiveness of the customer support team. Using AI to Calculate the Customer Service Index AI, or artificial intelligence, can be used to calculate a company's Customer Service Index (CSI) by analyzing customer feedback and identifying patterns and trends that can help companies improve their customer service. For example, AI can be used to process large volumes of customer feedback, such as survey responses or online reviews, and identify common themes or areas where customers are most likely to be satisfied or dissatisfied with a company's customer service. This information can then be used to calculate the company's CSI and identify areas where improvements can be made. Additionally, AI tools like Ascendo can be used to monitor customer interactions with a company's customer service team and provide real-time feedback and suggestions for improvement. This can help to ensure that customer service representatives are providing the best possible service and support to customers. Metrics confusion with Customer Health Score Customer Service Index (CSI) and Customer Health Score (CHS) are two different metrics that are used to measure different aspects of a company's customer relationships. CSI is a metric that measures the overall effectiveness of a company's customer service, while CHS is a metric that measures the overall health of a customer relationship. Responsibility of Customer Service Index The responsibility for calculating and managing a company's Customer Service Index (CSI) typically falls to the customer support team or the customer service department. This team is responsible for gathering data on customer satisfaction and interactions with the customer support team and using this data to calculate the company's CSI. They may also be responsible for implementing strategies to improve the company's customer service and for tracking changes in the CSI over time. In some cases, the responsibility for managing the CSI may also be shared with other departments within the company, such as the marketing or sales team, depending on the specific needs and goals of the organization.

Customer Support Experience today: Katie Deighton reports Forrester research summarizing what we have experienced: The average customer experience score, or CX score, in the 2022 survey was 71.3, down from 72.0 in 2021. With a “very poor” CX score of 48.6, a decline from 2021’s 54.1, the Internal Revenue Service was found to have the worst customer experience of all brands and agencies surveyed. No companies were rated excellent, which requires a score of 85 or higher, for the sixth year in a row. Pet-care company Chewy Inc. claimed the top customer experience rating for the second consecutive year with a score of 83.0. The worst part about the customer service job is - Too many windows and apps to learn and navigate "It can take opening, referencing, copying, pasting from 9 different apps to just reply to one email or respond to one phone inquiry" Employee Experience today: Douglas Kim wrote in his research article "Why nobody wants to work in customer service". Today forrester has released a study that summarized the pandemic experience that still continues: 👉 68% of respondents said the phone was a new “empathy channel for customers”. 👉 70% said their agents were dealing with more emotionally charged consumers. 👉 Agent turnover swelled to at least 80% -- and in extreme cases to as much as 300% While both Customer and Employee Experience are ranked the lowest, it also presents us with an opportunity to improve from here. What can a CEO do? Create a culture that puts customers in the center Listen to every interaction your customer has with your company. If your Support Experience is like this, then you are missing out on several Digital Transformation opportunities: This is what can be done today! Have a senior leader that owns Experience Learn more, Chatbot vs AI Bot Which is Better for Business? How to Become a Customer-Centric Organization?

Anyone who has worked in Customer Service knows how difficult scaling customer management can be. When providing customer support for businesses, it can be normal for CS personnel to manage anywhere from 40 to 100 customers. Managing a large portfolio of clients can feel like a daunting task. Every customer believes their issues take precedence so it can be difficult to determine how to prioritize customers and manage time effectively. Understanding this is crucial for CS teams to run smoothly and for customers to get the most value out of the product or service being sold. Fortunately, there are some best practices that CS teams can deploy to better handle their customer management. When it comes to managing a large portfolio of clients, here are a few tips every CS team should consider. Create Client Cohorts The best thing Customer Support teams can do when handling a large volume of clients is to split your customer list into different cohorts. These cohorts can be split into different categories depending on what makes sense for your company or industry. For instance, the segmentation of the cohorts can be based on: Product or Service client subscribes to Annual Recurring Revenue (ARR) Growth Potential Region of Country/World Client Size Client Engagement (High-touch vs. Low-touch clients) By splitting up clients into different cohorts, CS teams will be able to manage their time more effectively. In this system, CS teams can have dedicated staff or strategies that vary for each cohort. For instance, Customer Support teams can create delegate tasks by region to pair up workers with clients that are in their time zones. Or CS teams can assign their more experienced workers with high-growth clients and newer or less-experienced staff with low-growth clients. Through the cohort system, CS teams can use their people, marketing materials, and strategies more effectively. Rather than dealing with clients in a first come first serve style, the cohort system provides a structure that connects the right resources with the right clients. While CS teams can get incredibly creative with the cohorts they create, the best place to start is by creating two cohorts, a lower and higher cohort. Through this simple segmentation, Customer Support teams will have a significantly easier time managing their CS resources. Low-touch Cohort Clients When splitting clients into different tiers of cohorts, the biggest advantage CS teams have is that they can effectively mitigate time-consuming activities spent on lower-tier clients. Lower Cohort clients are not necessarily less important clients, but they do take less priority. This could be because they’re old, low-touch clients, their budget is limited, or there is little growth potential. Whatever the reason is, it’s likely that the lower cohort has more clients, but each client requires less time on average compared to high cohort clients. Because lower cohort teams have more clients and require less attention, the strategies used for these clients will differ. For instance, if a CS person has over 40 clients, it’s unlikely they will have regular meetings with each of them. Instead, CS people can de-prioritize their low-growth clients and send customer marketing material or pre-recorded webinars rather than in-person meetings. Without this strategy, it can be easy for CS workers to spend too much time with needy clients that have low-growth potential when they should be spending more time courting the business of high-growth clients. By using this cohort method, CS teams will not only use their time more effectively but there is a better chance of growing revenues and customer satisfaction as well. High-touch Cohort Clients High touch cohort clients are going to be the most important clients. These are likely the biggest clients, most time-consuming, most profitable, or even the newest clients CS teams deal with. These clients are where CS teams will want to have regular touchpoints and use their best workers who have the experience and professionalism to handle sensitive matters. With high-touch cohort clients, CS teams can create a more repeatable strategy of how and when to engage the clients. With a higher cohort of clients, the mission is always to increase engagement, as this will increase the number of selling opportunities and improve customer engagement. The best way to execute this strategy is to schedule out all important client interactions to not become overwhelmed and maintain every client gets the CS support they need. Depending on the product and customer base, this may have CS teams engaging in regular touchpoints like monthly or quarterly reviews. CSMs can also create touchpoints around corporate events such as new product releases that provide value to the clients. With high cohort clients, CS teams should always be looking for new opportunities and strategies to engage these clients. Because these clients are the most important and profitable, protecting this revenue is of the utmost importance. Invest in a Customer Data System that helps to Scale! Splitting clients into different cohorts is only half the battle. The hard part is servicing all your clients and keeping them happy. While you might split your clients into lower and higher cohorts, customers will always think their issues take priority. While all customers might be needy, that doesn’t mean it has to be difficult to deal with them. Fortunately, with tools like Ascendo, managing customer interactions can become a no-brainer. Automated Knowledge Creation: Create Knowledge from everyday interactions. Know the expert - Know who the expert is to provide further guidance on the particular topic of discussion. Enhance Self serve: Provide your expert at all times and all channels with a Virtual agent. Resolve issues, learn from issues, and build the ability to augment human agents. Scale your workforce. Reduce Escalations. Auto-categorization: Why solve one issue at a time when you have smart backlog management? Find what type of issues are in your backlog and how to resolve them. Escalation Prediction: Detect interaction intent and sentiment to predict customer escalations. Knowledge Intelligence: The self learns from every response to automatically create and improve knowledge. Bring out relevant and latest knowledge as customers call for it. Provide additional weightage to knowledge created by the expert. Identify gaps, duplicates, and auto-retire what becomes irrelevant. Agent Assist: Assist agents where they need and when they need. Automatically learn from the expert to make every agent an expert. Voice of the Customer: Detect new issue categories as soon as they surface so you’re always on the pulse of customers' concerns and sentiments. Then use trend data to make more informed staffing, knowledge, and scale decisions for your team. Auto Root Cause: Why solve one issue at a time when you have smart backlog management? Find what type of issues are in your backlog and how to resolve them. Action Intelligence: Reveal patterns and insights across all customer interactions that you can use to drive Product to where it matters most, plan to staff, and proactive customer services. With Ascendo, AI does most of the work for CS teams by providing product feedback so teams can accelerate product-led / customer-led growth.

One of the most common issues that customer service teams have is maintaining communication with customers. Working with customer service teams, we’ve found that mid-touch and high-touch customers tend to stop reaching out to customer service teams after onboarding. So what tips can we use to re-establish communication and avoid customer churn? In general, SaaS companies have two types of pricing models, freemium and tiered pricing. Regardless of the pricing model customers sign up for, SaaS companies aim to provide the best support experience for all their customers. However, ensuring that all customers receive high-quality service can be difficult, especially when there are differences in size and the level of service they pay for. To make sure that all customers receive top-notch service, customer service teams can take advantage of technology and processes that can be used for all types of customers. For instance, the number of unresponsive customers at the freemium level signifies that the customer has not retained interest or value from the product. Further, non-touch engagement levels with paid customers may signal issues with customer churn. Fortunately, we have created playbooks for customer service teams to use in these situations: Build Multiple Levels of Relationships Across the Two Companies One of the most effective ways to improve customer communication and the feedback loop is by fostering multiple relationships between the customer service team and the customer. However, some relationships are more important than others, and finding the right people is crucial to mitigating churn and improving customer satisfaction. These are the 3 relationships customer service teams should focus on fostering. 1. Customer Service Manager to Customer Service Manager Customer Service Managers are the people in charge of understanding and perfecting the user experience. As a Customer Service Manager, you should be reaching out to your counterpart on the client’s side. Opening this channel of communication will ensure that your client can feel comfortable reporting any issues or requests that may prevent the client from leaving in the future. You always want to build trust and transparent communication channels with the client. 2. Support Admins and Heavy Users Arguably the most important people to build relationships with are the admins and heavy users of your software. These people will be your champions in client meetings and can ultimately decide whether your client stays or goes. If the people in charge of the budget or the people that use your software the most aren’t satisfied, there is little chance they will stay on. Not only does good communication with these stakeholders mitigate churn, but they also are the best people to ask for product improvements and feedback. As the main stakeholders, these clients generally have an in-depth understanding of your product and which areas need the most improvement. Remember, when you have good transparent communication with your clients, that can become an incredible resource. 3. Marketing to Marketing Another area that customer service teams should focus on creating relationships is with client marketing departments. Because marketing departments are in charge of outward communications, this is the best opportunity for customer service teams to collaborate on events, reviews, videos, and training. Marketing teams are also a great target for clients who are likely to become heavy users when managed correctly by the customer service team. Set Up Regular Touchpoints with Customer Leadership A great strategy to ensure consistent, transparent customer communication is to set up regular touchpoints with the customer. Regular touchpoints such as Quarterly Business Reviews (QBRs) are a value-add that most customers will usually agree to. Hosting QBRs and other recurring touch points with main stakeholders will provide customer service teams with plenty of opportunities to gain feedback, understand client usage, and review customer usage as a whole. When conducting QBRs or other recurring touch points, here are a few items customer service teams can focus on. 1. Joint Goals Establishing joint goals is a great way to assess progress over time. While joint goals can be added or changed over time, they provide a foundation for what are the customer’s expectations. 2. Identify Open Items that Require Higher Visibility Every touch point should include a time when clients can identify and discuss items that have recently become important. This can include SLAs, new features, and other items that need to be addressed. Make sure you are transparent with items that did not go well or are not expected to go well. Transparency in features that cannot be fixed, reasons behind escalations, and missed SLAs do need to be summarized. Make sure to share mistakes, and plans to fix or avoid in the future is shared with the team so the customer feels that you have their success behind their back. Be proactive whenever possible. 3. Open the Feedback Loop Your customers are your best R&D asset because they use your product in the real world compared to in-house developers. By asking for feedback, customer service teams can build trust with their clients while gaining valuable information that can improve the product overall. We look at the sentiment of each interaction and bubble up with excitement! Always remember to encourage your clients to share any feedback and thank them when they do! 4. Summary of Product Updates It’s always good practice to share with your customers any new features that may be coming down the pipeline. This reminds customers that there is more value to be added to your product and gives customer service teams a chance to gain initial feedback on the upcoming changes. This is also a great opportunity to test out any new marketing for the product or features by sharing videos, photos, or even demos with the client. 5. Outline New Collaborative Opportunities Discussing collaboration opportunities with clients should be done at least on an annual basis. Joining forces in activities like marketing, sales, and more can be a great way to build relationships and create client dependency on your product. 6. Drive Actions for Next Touchpoint The last item on every touch point should be setting up the next touch point. Customer service teams should summarize action items and the agenda for the next touch point. Customer service teams should get clients to agree to the action items, so everyone is on the same page. Sending out a page memo that summarizes the highlights of the touch point and the agenda for the next touch point will ensure expectations are understood by all parties. 7. Reaching out to the flywheel Bring in other representatives from the Product, Marketing, and Infrastructure teams. Be ready to show customers that you are pulling in resources across your company to make them successful. At Ascendo, we aim to use data to improve customer relationships by providing and making constant, transparent communication easy for all parties. An AI-driven tool brings out all of the above collaborative touch points so customer teams can focus on what they do best – building relationships!