Time is valuable and this applies to the world of customer service as well. When a customer contacts customer support, they expect a solution to their problem right away. If customers receive a reply days after their query, they will be dissatisfied with the company. The lesser the customer has to wait, the better it is for the company's reputation. So, it is important for customer support teams to be responsive and effective while facing customers. What is Time To Resolution? Time To Resolution (TTR) is an important metric for measuring how quickly a company responds to its customers' needs. It helps companies understand when customers will stop waiting for service and start looking elsewhere. Time To Resolution (TTR) measures the average time it takes for the customer service team to resolve an open ticket or any other customer issue. It is calculated by dividing the sum of all times spent (days or hours) by the number of cases resolved. Mean Time To Resolution formula (is also referred to as Call to Resolution): Average Time To Resolution = sum of all times to resolution/ total # of cases resolved Why is Time To Resolution important? There's a direct correlation between time to resolution and customer satisfaction, or CSAT. Customers don't take the time to reach out to customer service organizations because they're bored or lonely. They only take the initiative when they have a problem that needs solving. Making them wait to resolve this problem creates friction, and sometimes ill will or even the loss of a customer. Time To Resolution metrics also impact team efficiency and effectiveness. For this reason, companies measure Time To Resolution both as an aggregate and on a per-employee basis to single out underperforming employees. OKRs help teams set objectives that can help teams determine what is expected of them and their performance. Everyone on the customer support team– whether they answer phone calls, respond to tickets, or answer queries– can help to reach service goals when their department utilizes OKRs. Customer satisfaction can be improved when Time To Resolution is optimized. Here are 10 ways to improve Time To Resolution: 1. Find & Analyze Average Time To Resolution In most cases, customer service managers will want to analyze the overall Time To Resolution in their team. This formula is generally applied to batches of cases. For example, you can find out the average Time To Resolution for a particular month by compiling all the data. Problems range from simple to complex. There are also outliers. AI tools automatically categorize the type of problem and provide Time To Resolution for a particular category of the problem which is way more accurate and real-time than a human downloading some of the tickets from CRM and mapping them in spreadsheets. 2. Know Your Customer Customer-centricity is a key business strategy that puts customers at the core of one's business in order to provide a positive experience and create long-term relationships. It’s important to understand who your customers are and how they use your product or service. This will allow you to provide better customer service and make sure that you meet their needs. Focusing on customers and their needs will be profitable for the company in the long run. 3. Different Issues, Different TTR You should also know how much time it takes to resolve an issue with your product or service. This will help you understand what your customers expect from you. It's important to keep in mind that some issues take longer than others to resolve. For example, if a customer has a problem with a new feature, it might take several days before he or she gets a response. However, if a customer has an old complaint, it might take only a few minutes to resolve. 4. Set Expectations TTR is measured by the number of days between when a customer contacts your business and when they receive a response. If a customer calls your business and asks for help with a problem, then waits three weeks before receiving a response, you should consider them unsatisfied. You need to set expectations early so that customers feel comfortable calling back. If you're not sure how much time something will take, set expectations with your customers. This helps them understand when they should expect to hear back from you. 5. Be Transparent Customer expectations are constantly changing. You need to keep up with them by being transparent and providing clear communication. If you aren’t able to answer questions or explain things clearly, be honest with them. Then, go back and find answers for them as soon as possible. Acting quickly to remedy the mistake and taking steps so it will not happen again is the message you want the customer to hear. 6. Take Feedback Customers will often ask you questions when they interact with you. They might even complain about something. It’s important to respond quickly and take feedback. This helps you understand how well you’re doing and where you need to improve. 7. Plan Based On Busy Periods Monitor your ticket volume and find out busier and lighter periods of work. Based on that data, you should make sure to be well-staffed during busy periods so that your agents can share the workload and solve issues promptly. Being understaffed during busy periods will increase your Time To Resolution. 8. Create an Action Plan Once you've identified the root cause of the problem, you need to create an action plan. This includes identifying who needs to do what, when, and how. If you're not sure where to start, ask yourself these questions: Who is responsible for resolving this issue? What steps do I need to take to fix it? How long does it usually take me to solve this type of problem? This is the chance for the agent to be proactive. Use goodwill gestures to win over the customer. 9. Automate Processes Using tools such as a chatbot or artificial intelligence-powered search will automate the issue resolution process. If you have a slow first response time, you can solve it by using these tools. They do not require human assistance. They improve efficiency and decrease Time To Resolution. Implement a system to identify the problem category of issues. This way, the cases can be assigned to agents based on their expertise in a particular problem category. 10. Know When You're Done It's easy to get caught up in the details of solving a problem. However, once you've done everything you can think of, it's time to stop. Communicate to the customer that the issue needs more investigation or that you do not have an instant solution. Once you solve the issue, make sure to add the solution to the knowledge base, so that next time the same issue crops up, you can solve it way quicker. How do benchmark Time To Resolution? Methods used for measuring Time To Resolution might be different across industries and the kind of service provided. Here are some things you should consider while calculating TTR: What customers expect The average time it takes for your employees to resolve issues Benchmark against other companies with similar products How Ascendo Supports Time To Resolution Ascendo provides predictive, artificial intelligence-based solutions to customer issues. It allows for a consolidated view of data which enables agents to access the knowledge base, previous interactions, problem categories and relevant solutions in an easy manner. Customers can transfer from self-service to agent seamlessly and don't have to repeat themselves. With the help of Ascendo, customer support leaders can know who the expert is for a particular type of issue and assign cases accordingly. Ascendo's self-service feature provides a human-like AI Bot or smart search for websites, through which customers can find answers to their queries on their own without having to wait for an agent's help. With primary predictions and actionable secondary predictions, Ascendo aims to decrease Time To Resolution and increase productive outcomes. Ascendo's virtual agents are also available on direct channels like Slack and WhatsApp, making the process more interactive, more accessible and faster than ever before. Ascendo's tools are designed for the continuous improvement of customer support operations. Ascendo fundamentally helps companies to turn proactive in the way they offer support services to their customers. Learn more, Innovative B2B Automated Self-Service Knowledge Intelligence in Customer Service

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?

Businesses today aim to offer better customer experiences while lowering service costs, and they increasingly realize that automated self-services like chatbot software may help them achieve these ends. By enabling users to find answers on their own and directing them to efficient solutions, chatbot software tackles the high volume of repetitive questions and reduces backlog. As a result of this, support staff can devote their time to resolving complex problems rather than answering simple questions. Since its inception in 1950, chatbot technology has evolved massively. At present, unfortunately, Chatbots and AI bots are often considered to be the same thing, leading to confusion and disappointment. Despite both being automated self-services, there are some glaring differences because of which technology is better than the other. What is a Chatbot? Computer programs or devices that can "chat" with you are called chatbots. Text messaging via a chat interface or a messaging app is the most typical way to communicate with chatbots. They converse by following predetermined guidelines (if the customer says "A," answer with "B"). Sometimes the interactions are structured as a decision-tree workflow where users can choose the appropriate responses based on their use case. These bots resemble automated phone menus in which the user must select a number of options before finding the desired information. This technology is perfect for resolving common customer complaints and FAQs. What is an AI Bot? Essentially, an AI bot is a higher-level idea of chatbots. AI bots are frequently embedded in web pages or other digital applications to respond to consumer enquiries without the need for support agents, thereby offering economical, hassle-free customer service. In order to provide a more personalized customer experience, an AI bot combines artificial intelligence (AI) technology with other technologies. Let us understand the key technologies used in AI bots. NLP or "natural language processing" enables a computer to comprehend what we say using our natural language. That is, using slang, acronyms, and other speech patterns that are typical of us. For instance, an AI bot may understand you even if you don't use precise grammar. Machine learning or ML implies that the computer can gain knowledge from interacting with humans. Therefore, it enables the AI bot to learn from its interactions and produce future conversations that will be more insightful and effective. Chatbot vs AI Bot According to Zendesk user data, customer service teams dealing with 20,000 support requests per month can save more than 240 hours in a month by using chatbot software. The key differences between Chatbot vs AI bot as listed below will delineate which is the better software to opt for. Operating Channel(s) In a constantly changing digital landscape, with more social and messaging apps, providing customer service across platforms and being where your customers interact with your brand is critical. Unlike chatbots, AI bots only need to be built once. After that, they can be deployed to multiple channels, with information from each channel streaming to a central analytics hub. Sadly, chatbots require rules, process definition and training to use them in different channels. Flexibility An AI bot with complete access to a database and API has the contextual flexibility necessary to engage in fluid conversations with users. For instance, if a user changes their mind in the middle of a chat, a conversational AI interface will automatically begin catering to the new needs of the user. But, a chatbot is constrained by the script and rules it has been given previously, and it is unable to produce any output that has not been manually added to its flow. Understanding An AI bot uses various technologies to parse and interpret inputs rather than depending on a pre-written script. Chatbots, in comparison, may appear to understand words and sentences while actually only adhering to a strict set of rules. While chatbots focus only on keywords when dealing with customer queries, AI bots can comprehend the context of queries and the intent of users. Maintenance An AI bot draws data from a variety of places, including websites, text corpora, databases, and APIs. It also immediately incorporates any revisions or updates made to these sources. Chatbots, in contrast, need ongoing, expensive manual maintenance to keep their conversational flow productive and efficient. Customer Experience AI bot is distinguished from traditional chatbot by its ability to have natural, human-like conversations. As a result of this, an AI bot is a great choice as a self-service option for customer support. Furthermore, as opposed to chatbot, AI bot can understand complex problems and provide more precise responses. Its user-friendly nature will reduce customer frustration and lead to better customer experiences. Transform Customer Support with AI Bot If you've read up to this point, it must be clear that AI bot is a human-like, cost-efficient, and automated self-service technology that can deliver seamless customer experience across many channels. AI bot's features overcome the shortcomings of a traditional chatbot. Ascendo offers an intelligent, conversational AI bot that can cater to your support needs, irrespective of the industry. You can integrate AIbot in conversational support channels like Slack, Teams and WhatsApp. Ascendo AIBot searches upon knowledge from multiple data sources to provide solutions to customers quickly. AI bot's interactions with customers are saved and analyzed to understand the patterns of customer concerns over time. In the rare case where the AI bot is unable to solve the customer issue, there is a smooth handover to a live agent who can view the customer's question and the solutions provided by the AI bot. This way, customers don't have to repeat themselves when passed on to an agent. To contact Ascendo, click here.

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!

There are several ways to calculate the financial value generated by software, equipped with artificial intelligence and proactive support, which has generated for a business or organization. One common approach is to consider the revenue that the software, along with modern support services and automation, has helped to generate, the cost savings it has facilitated through support outsourcing, and any other financial benefits it has brought about, such as improved customer experience and real-time sentiment analysis. This can be further enhanced by utilizing customer relationship management (CRM) systems and knowledge management tools for effective self-service and predictive analysis. Additionally, the role of a Chief Customer Officer can play a vital part in ensuring a positive experience with customer service and addressing any anomalies or backlog effectively. The voice of the customer can also be incorporated through various techniques, including customer service experience and predictive analysis. Another way to calculate the value of software is to consider its non-financial value, such as the value it has brought to users or customers in terms of convenience, efficiency, or improved quality of life. This can be more difficult to quantify, but can still be an important factor in determining the overall value of the software. To calculate the financial value of software, you may need to gather data on the costs associated with developing and maintaining the software, as well as any revenues or cost savings it has generated. You may also need to consider factors such as the size of the user base, the length of time the software has been in use, and any changes in the market or industry that may have been influenced by the software. Additionally, analyzing the impact of the software on customer service, customer experience, and customer support can provide valuable insights into its overall value. Factors such as proactive support, CRM integration, and the expertise of a Chief Customer Officer can further enhance the software's impact. Evaluating the effectiveness of modern support services, self-service options, and automation can contribute to assessing financial value. Real-time analytics, sentiment analysis, and knowledge management play a crucial role in understanding customer needs and driving improvements. Considering the backlog and incorporating the voice of the customer through predictive analysis can also contribute to a comprehensive evaluation of the software's financial value. Various methods and approaches can be used to calculate the value of software, and the specific method chosen will depend on the goals and objectives of the analysis, as well as the data and resources available. How to Calculate a Value for AI Software in the Customer Support Domain? The effectiveness of AI software in solving customer issues: If AI software can effectively solve customer issues, it can provide value to the organization by reducing the workload of customer support staff and improving customer satisfaction. Standardize support quality Support quality is standardized through tools provided to expert team members that revamp the traditional approach. The cost of onboarding an additional agent that might also result in delayed or incorrect responses is mitigated through this process. Leader insights from trends and patterns Leader insights from trends and patterns, coupled with the utilization of artificial intelligence and predictive analysis, can empower organizations to build a customer-centric organization. This approach eliminates the need for creating fire-fighting SWAT teams and spending resources on manual data fixing and spreadsheet creation to gather Voice of the Customer. By leveraging real-time data and embracing automation, organizations can make informed decisions promptly, without incurring the costs associated with delayed or incomplete information. This shift towards modern support services and knowledge management enables a proactive and customer-focused approach, reducing the reliance on support outsourcing and minimizing the backlog. Ultimately, the integration of sentiment analysis and CRM systems enables organizations to effectively capture the Voice of the Customer, facilitating the development of a customer-centric culture. The cost of implementing and maintaining the AI software: Organizations need to consider the upfront cost of implementing the AI software, as well as any ongoing maintenance and support costs. The potential for cost savings: If the AI software, powered by artificial intelligence, is capable of efficiently handling a large volume of customer inquiries, it can potentially save the organization money by reducing the need for human customer support staff. The ability to improve customer satisfaction: AI software, driven by artificial intelligence and proactive support, has the potential to significantly enhance customer satisfaction and loyalty by providing timely and accurate responses to customer inquiries in the customer support domain. Organizations can calculate the value of this software by taking into account factors such as customer service experience, modern support services, and the implementation of knowledge management tools. By analyzing data related to customer inquiries, including volume and types, as well as customer satisfaction and cost data, organizations can determine the value that AI software is likely to bring to their business. The software can assist in managing support outsourcing, addressing anomalies, and reducing backlog, while also enabling real-time sentiment analysis and self-service options. Additionally, incorporating predictive analysis and the voice of the customer can further enhance the value generated by AI software.

Role of the CCO The Chief Customer Officer role is ever-changing and differs significantly. At the minimum, the CCO role is responsible for Customer Support, Customer Service, Customer Success, and Customer Data teams. They are responsible for Customer Experience metrics. In some companies, they extend to include Customer journey definition and mapping. This makes a CCO role to be truly cross-functional across (marketing, sales, operations, and product). When it extends to include employee experience as it maps to customer experience, this role overlaps with the people division. We also see that the role names are different based on the industry it represents. Chief Customer Officer Chief Experience Officer Chief Commercial Officer In 2016, McKinsey's study showed only 39% of the organizations had a customer-centric role. A Gartner study in 2020 shows that 90% of organizations have a customer-centric C-suite role. The Technology sector, including IT, SAAS, Software, and Internet Services, has led to the increase in the role of the CCO, with 46.5% of CCOs working in this space. The CCO role did not start at a tech company though! History of the Chief Customer Officer Role The Chief Customer Officer role is still quite new, the first appointment to which was Jack Chambers, the CCO of Texas New Mexico Power (TNMP), in 1999. In 2003 there were fewer than 20 people in the world with the obscure title of Chief Customer Officer. These included a small handful of trailblazers including Marissa Peterson of Sun Microsystems, Doug Allred of Cisco, and Jeff Lewis of Monster.com. Most people at this time had never heard of the title and many of these couldn’t fathom the need for another member of the C-Suite. Early CCOs were more of a “Chief Customer Service Officer” whose primary focus was on customer service and retention. Their efforts centered on helping their company “play nicer with customers.” Some were focused on finding ways of keeping customers from suing the company, and some are unfortunately still stuck here. Despite much success, some of the early pioneers faced such an uphill battle that they ended up leaving or retiring early. Some were forced out the moment the company’s revenues hit a speed bump. Another had a heart attack, retired, and left the field altogether. However, not all suffered such dramatic fates. Many succeeded and carried the torch forward, paving the way for others to follow. We have also written about the Evolution of data before implementing AI if you are curious about data. The Genesis of the Chief Customer Officer Because the role still forming, several Chief Customer Officers still have to explain what their role is. It used to be that the CCO used to be reporting to the CMO. But, almost in all cases, CCO reports directly to the CEO. Check about Modern Support Experience for CEOs. The Chief Customer Officer must be the ultimate authority on all things customers, understanding customers better than any other individual in the company and driving the company towards common Experience goals. This role is most effective by being the senior-most role. The Chief Customer Officer role is evolving into more of a “Chief Customer Strategy Officer,” focused primarily upon driving profitable customer strategy at all levels of the company with the express goal of acquiring, retaining, and serving the right customers for greater profits. It is no longer a “nice to have” designation; for many companies, it is a business critical and a primary source of competitive advantage. In contrast to the wider picture, the 'Customer' sector is a great place for gender diversity, with 46% of Chief Customer Officer and Chief Experience Officer appointments being female and of those, 6% of CCOs and 9% of CXOs Chief Experience Officers, were from under-represented ethnic groups. The Chief Customer Officer of Today There were just 500 Chief Customer Officers in the world just a few years ago. A quick search on LinkedIn shows around 9400 execs in this role. As every business is turning to be "As A Service" business - Service is the new Product. Customer Experience is at the forefront of business model innovation. The CCO role is evolving into more of a “Chief Customer Strategy Officer,” focused primarily upon driving profitable customer strategy at all levels of the company with the express goal of acquiring, retaining, and serving the right customers for greater profits. Their teams are transforming in the subscription economy. Even though initially people with marketing backgrounds came to this role, because of the strategic nature, we are seeing people with operational experience and data background are critical and very successful in these roles. The role is most commonly very data-driven and is responsible for creating actionable insight for use to improve Customer Experience and commercial profitability. AI and Responsibilities of the CCO The Chief Customer Officer by definition is designed to drive customer and corporate strategy into the C-Suite and throughout the company. Because CCOs can provide the authoritative view of the customer, they are uniquely qualified to shape corporate strategy to guide the company in the coming years. As well, CCOs must be the authors of customer strategy to define customer portfolios, prioritize customer retention and acquisition efforts, create greater customer value, and increase loyalty. All of the strategies that come into play require a data-oriented approach. Chief Customer Officer Metrics and Goals With customer and operational data split across many systems, AI must be at the heart of the metrics for any CCO. Chief Customer Officers, regardless of their industry or tenure share three common goals: Drive profitable customer behavior: To help customers use products more, and more often, the CCO must focus on initiatives such as value matrix, product usage, customer retention, customer loyalty, satisfaction, and improving the customer experience. As well, many CCOs will use in-depth customer insight to inform the sales and marketing efforts to acquire more of the “right” and profitable customers. Create a customer-centric culture: One of the most important roles of the CCO is to help create a strong, customer-centric culture complete with accountability and ownership at all levels in the company. CCOs that fail at this imperative incessantly put out fires and burn out as nobody else takes ownership. CCOs must prioritize customer initiatives to drive the most profitable initiatives with the greatest customer impact. This is done by change management and change management becomes easy when you can show data, trends, patterns, anomalies, and benchmarks. They must put a face on customers and help employees (especially the non-customer-facing employees) remain focused on driving customer value. Delivering and demonstrating value to the CEO, the Board, Peers, and Employees: Because the CCO role is new and some are not fully defined in every organization, the CCO must strive to deliver demonstrable value to all stakeholders, not the least of which is the CEO, the board, and peers. AI provides the ability to show results in real-time. It can provide patterns of product issues, experience gaps, and even data gaps to drive the strategic change that the board needs to take. This is the only way to cement the role and has to be done effectively. It provides value to the CCO as well as to the peers. In summary, the CCO role is the key most data-centric role in every organization. Artificial intelligence, Machine Learning, and Natural Language Processing techniques along with semantic inference models like Ascendo can greatly help with external and internal goals of Customer Experience metrics. CCOs are seeking to build out data-driven customer journeys and operationalize internal processes around customer journey data. Ascendo fits perfectly in this vision and helps to turn the organization proactive towards customer experience while elevating the experience of internal team members and customers in every interaction. Reach out to us - We will provide you with a free data analysis and guidance on what can AI do for you. Great service with great products will create a market leader.