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

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

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

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

Being a customer-centric organization means putting the needs and wants of your customers at the center of your business operations. We are often asked what it means to be a customer-centric organization. When Do You Know You Are a Customer-Centric Organization? A company can be considered truly customer-centric by understanding a combination of quantitative and qualitative factors. Here are some signs that a company is on the path to being customer-centric: High Customer Satisfaction: The interactions with customers come up with high predicted ratings along with high ratings on customer satisfaction surveys. Customer sentiment from interactions is tending better along with customers reporting positive experiences with the company's products or services. Proactive Customer Engagement: The company regularly reaches out to customers for feedback and actively listens to and addresses their concerns or suggestions. The company knows issues before they happen. The company understands trends of problems that are happening and can notify customers of risks and plan to remediate them. Customer-Focused Decision Making: The company's decision-making process is driven by a desire to meet the needs of customers, rather than solely by financial considerations. Employee Empowerment: Employees are trained, have the tools, and are empowered to make decisions that benefit customers. They are rewarded for delivering great customer service. Data-Driven Insights: The company uses data and analytics to understand customer needs and preferences and understand the pulse of the customer, the ability to tell a story to the rest of the company to bring them along. Continuous Improvement: The company is committed to ongoing improvements in customer service and regularly implements new strategies to enhance the customer experience. It's important to note that becoming a truly customer-centric organization is an ongoing process, it can take time and continuous effort from the leadership team and all the employees, and it requires constant adaptation to changing customer needs and preferences and regular feedback and measurement of the effectiveness of the strategies. Best Practices to Become a Customer-Centric Organization Here are a few practices on how to become one: Understand this is a journey. A sherpa once told me on a hike in Bhutan - think of it as one step in front of another. Take small steps. Enjoy and celebrate the journey as well as the destination. Set smaller milestones and goals to achieve. A good first step that we see - I want to take care of employees first and help them with tools that my support team needs. Use your product You have to feel what the customer feels. Meet the customer where they are at. B2C companies do this well - eg., bicycle company execs using the bike every day. At Ascendo, we use Ascendo to support our customers. From day 1. We feel the same joy, pain, and growth that our customers see. Say a story As a support lead, you ARE the voice of the customer for the product. Don't just rely on metrics that may only capture one side of the picture or may not be real-time. Your product is changing and is becoming complex. So are your deployments and integrations. Look at data across customer interactions and bring out that story so you can prioritize what is really important and bring the rest of the company along with you. Strategies Helpful for Customer-centric Organization Here are some strategies that can help: Understand Your Customers: Conduct research to understand your customers' demographics, preferences, pain points, and goals. Use this information to create buyer personas and tailor your products, services, and messaging to their needs. Listen to Feedback: Encourage customers to share their feedback and make it easy for them to do so. Use customer feedback to identify areas for improvement and make changes to your products, services, or processes accordingly. Empower Your Employees: Make sure your employees are trained on the importance of customer service and are empowered to make decisions that benefit the customer. Encourage them to think about the customer's perspective when making decisions and to take ownership of resolving customer issues. Personalize Your Interactions: Use the information you have about your customers to personalize your interactions with them, whether it's through targeted marketing, personalized product recommendations, or tailored customer service. Continuously Improve: Continuously monitor and measure your customer satisfaction, and use the feedback to identify opportunities for improvement. Continuously test and implement new ideas to improve the overall customer experience. Lead from the Top: Make sure that the leadership team is committed to being customer-centric and creating a culture of customer service throughout the organization. Encourage them to engage with customers and actively listen and respond to their feedback. Being a customer-centric organization is an ongoing process. It is essential to frequently review and update the strategies to keep up with the changing customer needs and preferences.

When you are working with clients on long and complicated projects, nothing is more important than maintaining good lines of communication. Having constant and transparent communication with your clients can help improve your relationship with the client while ensuring that everyone’s time and money are being used effectively. While email and phone calls are the primary forms of communication in business, these methods can be ineffective uses of time and lead to instances of misinterpretation. That is why, when it comes to long-term customer relationships, using a messaging service like Slack or Microsoft Teams can help boost productivity and client satisfaction. These services allow you to stay in constant contact with your customers and shorten the feedback loop, allowing your team to deliver the highest quality product or service promptly. Although these messaging services were built with internal communication in mind, adapting them to client communications takes a bit of adjustment. One popular method of adapting these existing systems for a new purpose is using plugins like Ascendo . These tools provide additional capabilities to handle the different needs of being in constant communication with clients. Unlike Slack’s or Microsoft Team’s normal features, Ascendo allows businesses to better manage customer issues, concerns, and questions and collaborate effectively. With Ascendo you can use ticketing tools, lead discussions about certain files, leverage AI-powered search for easy use, and unlock customer insights not seen before. Companies using Ascendo have already seen huge improvements in customer satisfaction scores and net promoter scores. Download the full whitepaper to read more on this.

In the dynamic world of customer service, adapting to modern technologies and methodologies is inevitable. One meaningful change that customer service teams may encounter is the transition from self-assigning tasks to automatic assignments. This shift can streamline processes, enhance efficiency, and improve overall team performance. However, implementing such a change requires careful planning, communication, and consideration for the team members involved. A recent interaction among professionals delved into the question of how much notice should be given to agents before implementing the switch from self-assign to automatic assignment. Let us explore their insights and experiences to understand the significance of providing adequate notice and gathering feedback throughout the transition process. Most leaders drawing from their experience emphasized the importance of allowing a reasonable period for the transition. They suggested giving at least a month’s notice before considering the change. This duration allowed ample time for presenting the change to the team, addressing individual concerns, and incorporating feedback into the plan. They highlighted the necessity of understanding and addressing agents’ apprehensions, such as fears of being assigned challenging tasks or concerns about the fairness of the automatic assignment system. Building on this perspective, leaders recommend extending the notice period to accommodate adjustments and adaptation to the new system. They proposed giving an additional month for agents to benchmark and adjust to the change, especially if it involves modifications to performance metrics like scorecards. Leaders emphasized the importance of providing a buffer period for agents to acclimate to the new workflow effectively. Reflecting on the suggestions provided, leaders expressed gratitude for the insights shared by the team. Their response underscored the confidence gained in advocating for a more comprehensive approach to the transition process. By acknowledging the value of adequate notice and feedback, leaders highlighted the pitfalls of rushing into changes without proper consideration for their impact on the team. The conversation portrays a collaborative effort to navigate the complexities of transitioning from self-assign to automatic assignment. It underscores the significance of proactive communication, understanding individual concerns, and allowing sufficient time for adaptation. Here are key tips to transition from Self-Assign to Automatic Assign: Communication is Key: Transparent communication regarding the impending change is essential. Providing ample notice allows agents to prepare mentally and emotionally for the transition. Feedback Facilitates Adaptation: Gathering feedback from team members enables leaders to address concerns and tailor the transition plan accordingly. It fosters a sense of inclusion and empowers agents to voice their perspectives. Allow for Adjustment Period: Transitioning to a new workflow may require time for adaptation. Providing a buffer period allows agents to familiarize themselves with the changes and adjust their working methods accordingly. Avoid Hasty Rollouts: Rushing into changes without proper planning and consideration can lead to confusion and resistance among team members. Taking the time to plan, communicate, and gather feedback mitigates the risks associated with abrupt transitions. In conclusion, transitioning from self-assignment to automatic assignment requires careful planning, effective communication, and a collaborative approach. Organizations can navigate the transition smoothly by providing adequate notice, soliciting feedback, and allowing for an adjustment period while fostering a positive environment for their customer service teams. Learn more: Overcoming the Challenge of Rule-Based Chatbots: Unveiling Gen AI Capabilities Empowering the Future of Work With Customer Support: Innovative and Key Features

Imagine a world where your CRM (Customer Relationship Management) not only stores data but also becomes a proactive partner in your service and support journey. Generative CRM heralds this transformative era by synergizing the prowess of generative AI (Artificial Intelligence) with your customer data. It is more than just a tool; it is a game changer that augments productivity, efficiency, and customer relationships across industries. In the dynamic landscape of service customer relationship management (CRM), traditional systems have long relied on ticket-based approaches. Most of the reason is they were built for sales and do not fit the landscape of servicing and supporting products.  However, a change in basic assumptions is underway with the emergence of generative service CRM—a revolutionary concept that transcends the limitations of conventional methods. The Evolution of CRM: From Tickets to Interactions Generative service CRM represents a quantum leap in CRM evolution by embracing an interaction-based model. Unlike its predecessors, which compartmentalize customer interactions into discrete tickets, generative CRM seamlessly integrates with various communication channels such as Slack, Teams, bots, emails, phones, and tickets. This fundamental shift empowers businesses to glean additional context surrounding customer engagements, fostering deeper insights and more meaningful interactions. What Sets Generative CRM Apart? Generative CRM transcends the boundaries of conventional CRM systems. It is a dynamic fusion of cutting-edge AI and your invaluable customer insights. Through continuous learning and adaptation, it evolves into a smarter, more intuitive ally with each interaction. Empowering Productivity Bid farewell to mundane tasks that consume your precious time. Generative CRM automates repetitive chores, allowing you to focus on high-value initiatives. Whether it is crafting compelling emails, summarizing complex data, or refining customer service interactions, this innovative tool streamlines your workflow with unparalleled efficiency. Accelerating Time-to-Value In today's fast-paced business landscape, time is of the essence. Generative CRM drastically reduces time-to-value by harnessing the vast potential of AI. By distilling relevant information from the digital noise, it delivers actionable insights at your fingertips, empowering swift decision-making and proactive engagement. Liberating Human Potential Say goodbye to tedious data mining and futile searches. Generative CRM liberates human potential by automating repetitive tasks and providing real-time intelligence. Now, you can devote your energy to nurturing meaningful connections and fostering genuine relationships with clients and prospects. Trust and Security Security and privacy are paramount in the realm of generative CRM. Upholding stringent standards, this technology ensures the confidentiality of sensitive data while harnessing the collective wisdom of both public and private sources. Trustworthy and reliable, it paves the way for seamless integration into enterprise ecosystems. The Competitive Edge of Generative CRM Generative CRMs not only streamline operations but also foster innovation and agility in customer-centric endeavors. By embracing interaction-based frameworks and granular persona mapping, businesses can stay ahead of the curve, anticipating and addressing customer needs with unprecedented precision and efficiency. In an era defined by rapid digital transformation and evolving customer expectations, generative CRMs emerge as the cornerstone of sustainable growth and competitive advantage. Conclusion: Embracing the Future of Customer Engagement Generative service CRM represents more than just a technological advancement—it embodies a paradigm shift in how businesses engage with their customers. By transcending the constraints of traditional ticket-based systems and embracing interaction-based models, generative CRMs empower businesses to forge deeper connections, drive innovation, and deliver unparalleled customer experiences. Embrace the future of customer engagement with generative CRM and embark on a journey of transformation and success. Generative CRM is not just a technological marvel; it is a catalyst for innovation and transformation. Embrace its potential, and embark on a journey towards enhanced productivity, enriched customer relationships, and sustainable growth. With Generative CRM, the possibilities are limitless, and the future is bright.

In today's rapidly evolving business landscape, the integration of AI tools, particularly Large Language Models (LLMs), has become indispensable for enhancing operational efficiency and staying competitive. However, this technological advancement brings along significant responsibilities and challenges, necessitating a nuanced approach toward its adoption. Businesses are increasingly leveraging LLMs to reshape customer interactions, refine sales strategies, and streamline administrative tasks. These AI-powered tools automate processes like lead scoring and provide instant call summaries with actionable insights, revolutionizing traditional workflows. Yet, as the potential of LLMs unfolds, it is imperative to acknowledge and address the accompanying risks. The cornerstone of LLM (Large Language Models) integration lies in data – the most valuable asset in today's digital economy. As businesses entrust sensitive data to LLMs, they must navigate complex data custody issues. This demands a new playbook from solution providers, emphasizing the utmost importance of security and privacy. To navigate these challenges, initiating transparent conversations with technology partners is paramount. Here is a framework of questions to kick-start these discussions: Data Privacy Assurance: How do you guarantee that LLM answers derive solely from my data? Data Usage and Accessibility: Are my LLM interactions used for training purposes? How accessible is my data outside my organization, and does this comply with my data privacy policy? Security Measures: How do you prevent confidential data exposure to unauthorized individuals, both outside and inside my organization, capable of manipulating LLMs? Accuracy and Error Mitigation: How do you ensure LLM accuracy and address inaccuracies, such as hallucinations? Performance Benchmarks: What are your performance benchmarks for LLM interaction? Beyond these initial discussions, businesses must also consider broader ethical implications, regulatory compliance, and practical implementation strategies: Ethical Considerations: Exploring the ethical implications of LLM usage, including biases and potential misuse, is essential for responsible AI deployment. Regulatory Compliance: Adhering to regulatory frameworks such as GDPR (General Data Protection Regulation) or CCPA ensures that LLM integration aligns with legal requirements and protects consumer rights. Practical Implementation: Providing actionable strategies  for data anonymization, employee training, and cybersecurity measures to enhance the seamless integration of LLMs into existing workflows. Moreover, incorporating real-life examples, case studies, and visual aids can enrich the discussion, making it more relatable and accessible to readers.  At Ascendo, we have an AI workshop that caters to all of the above. In conclusion, as Enterprises embark on the journey of integrating LLMs into their operations, prioritizing data privacy, security, and ethical considerations is paramount. Transparent communication with technology partners, adherence to regulatory guidelines, and a proactive approach toward addressing challenges will pave the way for successful and responsible AI adoption. Let us navigate this transformative era with caution, foresight, and a steadfast commitment to ethical AI practices. Learn More Why do we take SOC 2 seriously? How to Scale Support to a Large Number of Customers?

In the ever-evolving landscape of customer-centricity, the Voice of the Customer (VOC) emerges as a key driver, intricately woven into the fabric of Customer Experience (CX). This article explores the critical role of VOC in support teams, presenting a structured sequence of steps to implement a robust VOC program. From defining its purpose to fostering collaboration with key stakeholders, each step contributes to a transformative journey toward enhancing customer satisfaction and loyalty. The Strategic Blueprint for VOC Success 1. Defining the VOC Program and Its Purpose The foundation of any successful VOC initiative lies in a clear understanding of its purpose. Articulate the goals, scope, and intended outcomes to ensure alignment with broader customer experience objectives. 2. Identifying Target Customers Crafting a comprehensive list of target customers, with criteria set collaboratively by support teams and management, establishes a focused approach. This step lays the groundwork for targeted feedback collection. 3. Collaboration with Account Management Engage with the Account Management team to glean insights into customer backgrounds, product nuances, and overall health. Effectively communicate the VOC program’s objectives to build a shared understanding and commitment. 4. Timing Is Key Assess the readiness of both internal teams and customers. Collaborate with stakeholders to determine the optimal timing for initiating the VOC program, ensuring it aligns with customer touchpoints. 5. Crafting a Pre-VOC Interview Checklist Prepare a comprehensive checklist to streamline pre-VOC interactions. This step ensures that all necessary groundwork is laid before engaging customers in meaningful conversations. 6. Designing an In-Depth Questionnaire Construct an interview questionnaire encompassing critical categories such as Support, Product, Account Management, Cloud Maturity, and Leadership. Include a numeric section mirroring Net Promoter Score (NPS) questions for quantitative insights. 7. Gathering and Analyzing Feedback Consolidate feedback from customers and collaborate with cross-functional teams, including Account Management, Product Management, and Engineering. This collaborative approach ensures a holistic evaluation of customer sentiments. 8. Creating an Action Plan Translate feedback into actionable insights. Work in tandem with Account Management, Product Management, and Engineering teams to formulate a strategic action plan that addresses identified areas of improvement. 9. Closing the Loop Transparent communication is key. Share the action plan with customers through Quarterly Business Reviews (QBRs) or emails, closing the feedback loop. This not only demonstrates responsiveness but also reinforces the commitment to customer success. The AI (Artificial Intelligence) Frontier: VOC in the Digital Realm In the era of artificial intelligence, extending the principles of VOC to the digital landscape becomes imperative. Establishing a Customer Advisory Board grouped by customer journey facilitates a structured approach to gathering insights, including Customer Effort Score (CES), Customer Satisfaction (CSAT), feedback, and bug reports. Sharing scores, input, and roadmaps derived from the VOC program ensures a collaborative relationship between support teams and customers. This AI-driven extension of VOC not only enhances customer understanding but also positions support teams as proactive partners in the evolution of products and services. In conclusion, the strategic implementation of a VOC program is not just a methodology; it is a commitment to continuous improvement and customer success. By following the outlined steps and embracing the digital frontier, support teams can leverage the power of customer insights to drive meaningful change, foster loyalty, and stay at the forefront of the ever-evolving CX landscape.  Learn more: Uncovering Trends and Redefining Success in Customer Support with AI-Powered Precision Voice of the Customer