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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.

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

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

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

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

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

In an earlier article, I wrote about service trends and automation. At my company, we're always asked if machines will eventually take away support jobs. Do computers substitute humans or, are they complementary to existing workers? In the last two decades, as globalization has come into the picture, we have started to compete more as humans. This often means that if there is a lower-cost resource that could do the same job as a higher-cost resource, we switch out those resources. This trend will only continue, as all humans have the same basic needs  —  food, shelter and luxury items. A machine does not need any of the above. Innovation is the key to customer support. 82% of those who are in charge of making decisions for their companies believe their business' support practices must evolve to remain competitive. What will your business do to stay relevant, and how will machines be a part of that change? What Does It Mean To Be Complementary? Let’s look at a simple example . In 2012, teams competed to develop the best machine learning algorithms to identify dog breeds in ImageNet's library of dog photos. We engineers marveled at what the technology here could do. Then, we paused and realized that a 2-year-old could easily point to any type of dog and verbalize a description without any training. This made us recognize that humans are a marvel; we have a long way to go to make machines the same. We, humans, excel at making plans and forming decisions in very complex situations. Computers aren't quite the same. They excel when there are loads of data to process, but they struggle when it comes to making simple judgments that humans are used to. While we can understand and make sense of multidimensional situations, computers can process multidimensional data. We are categorically different. We are complementary. We have used computers for these complementary scenarios in various parts of our lives for decades  —  fraud detection, network switching, efficient processing power, fast transactions, etc. As we reach optimal infrastructure levels (our phones are way more powerful than the massive computers of old), many hope to bring this processing power into enterprise decision making. Our human minds are also slowly being trained to understand the power of what it means to be "complementary." We use computers to narrow down our flight searches, find the fastest routes to a destination, discover efficient ways to find the lowest price for an item, correct our mistakes, find the words to finish our sentences and more. As millennial minds come into the enterprise world, they are questioning how we can use this technology to perform complex multidimensional data analysis and pattern recognition. The Handshake Between Man And Machine Let’s look at decision making in customer support and customer service. This support happens across industries and verticals. Whether the support for a physical product or a software product, it is essentially focused on solving problems in three dimensions: Solve today’s problem, something that a customer has already pointed out. Make sure the same problem doesn’t happen again by making changes to the product, employee training or organizational behavior. Predict trends and problems (more applicable to physical products) before they happen. Problem prevention and problem elimination are the core of customer support, whether it is solved by self-service, the community, an agent or field or auto-support. With smart physical machines and advanced software, the data that comes in from customers has increased considerably. Customers are also smart enough to know the nature of the problem they are facing and expect a quick response. Let’s drill into this to see how computers can help. In customer support, we often solve customers' problems with tiered approaches and/or swarming. In both cases, humans spend hours through patterns in community forums, FAQ pages, knowledge bases, product patches and release notes, dump and log files and running proprietary scripts to reproduce or gather information from customer scenarios. Essentially, what we are doing is looking at various patterns of previous incidents to see if we have seen a similar situation before. This level of multiple-dimensional data crunching is what computers do best. But while it is cool to have access to machine learning algorithms that can handle these tasks, computers alone will not solve all issues. Many companies are already looking at how computers can help humans solve hard problems. This process is very different from traditional service management. Businesses can search, using natural language processing and machine learning, for relevant data that can then be sent to agents. This helps make decisions across all three dimensions of problem-solving. Along with providing relevant data, businesses can also use feedback mechanisms, which shed light on what the machine predicts to be the root cause of the problem, as well as possible solutions that have occurred before. This feedback mechanism is the critical handshake between what the human interprets and what the machine predicts. Even still, customer support automation may not be the right choice for every business, even with its complementary nature. For a business to see some sort of return on investment with this approach, it needs to be ready to invest in the solution. This may include assigning a member of management to oversee the process. Also, if your support team is less than two people — or if you deal with little digitized data — it is hard to see value in this approach overall. The Future Of Customer Support We will never know what unknowns the future of computing will bring. Some people are a bit wary of the future, but if we use machines the right way, we will hit the cosmic lottery. If we use them incorrectly, we can expect Skynet to take over. Nevertheless, we are at least a century away from this. Until then, there is time for you to use the complementary aspects of machines to build a vastly better support organization — that is, if the costs are worth it for your business. Learn more, Knowledge Management To Improve Support Experience Do You Have Enough Data For Machine Learning?

An organization that sees its customer service department as a cost center is ignoring a significant opportunity to increase customer loyalty and lifetime value. Software-as-a-service (SaaS) is taking software that you pay a little bit less in the beginning but regularly. Monthly, yearly, sometimes longer depending on the contract. Customer support and customer success teams are the ones that interact much more closely with the customer after the final sale of the contract. “Service to sale” thinking needs to be cemented in customer support. Customer support is now the primary human touchpoint a brand has with buyers! End customers are trending towards self-service during the buying process. This reduces direct personal interaction with sales. When buyers are not able to fix simple customer service issues with self-service tools, customer support is often the first human contact they have with a brand. That makes interactions with support a critical and strategic advantage in building customer loyalty. ​ Every support interaction is an opportunity for a company to build a more personal relationship with a buyer. At the end of customer service interaction, a happy customer can be showcased in a marketing case study, events, G2, or Trustpilot review. Every unhappy customer can be called out for a follow-up to improve a business process, training, or product improvement. ​ Companies that don’t take a revenue-oriented approach to their customer services teams fail to unlock a lot of sales opportunities. Top Reasons why Customer Support is Revenue Customer retention is far less expensive than customer acquisition. Excellent customer support improves public persona and strengthens the brand. Existing customers are more likely to buy from you than new customers. Your customers stay with you longer. Word-of-mouth marketing is the best kind of marketing. Great customer support opens doors for organic partnerships and opportunities. Providing phenomenal product support results in a reduction of overall problems. Support is the Voice of the Customer that improves the product drastically. How AI can enable the pivot for Customer support into Sales ? Today, AI-based platforms show the current and historical sentiment of the customer based on current and historical interactions. They also show product issues that are open to the customer. All this can be used to gauge when the time is right for a support agent to transition into a sales role. A happy customer, a customer who is thrilled after an issue is resolved, a customer who expresses a Wow moment of using a software feature, all of this is factored in. ​ When the agent is ready, the AI platform also suggests cross-sell and up-sell opportunities available for the customer enabling the ease of transition and an ability to pull a salesperson in at any given time. This is especially true for B2B sales, where issues are rarely closed in a single call. ​ So the question is- “Should support leaders advocate customer support as a revenue generator?” ​ Here is where you might agree, or perhaps differ- and this is the conversation that Ascendo AI and Support Driven are trying to facilitate! Our speakers for the dialogue- Ahmad Shabazz and Mo McKibbin engage in an insightful conversation to bring to you their unique take on several topics related to ‘Growth Through Support’. Check it out in our Events section. ​ Learn more, Ways To Improve Customer Services With AI How to Scale Support to a Large Number of Customers?

During the pandemic, businesses have learned that managing key customers is essential to surviving difficult situations like economic downturns, supply shortages, and more. A business’ key customers are the clients that have long-standing relationships with and make up a large, consistent share of a company’s revenue. Many businesses rely on their key customers for recurring revenue and would be devastating to lose. Therefore, systems like Ascendo that can improve customer communications are vital to maintaining these important relationships. What Are Key Customers? Managing key customers can be incredibly difficult because just like any business, they are susceptible to market downturns and will walk away from deals if pricing and services are no longer worth the cost. An easy way to determine your key customers is by looking at the following: 1. The customer’s lifetime value 2. Level of influence and/or authority in the market 3. Additional revenue and referrals they contribute 4. Level of commitment to shared partnership goals Why Are Key Customers Important? Focusing on what products or services drive revenue with key customers allows the business to understand which activities are most profitable. Ultimately, this can determine a company’s mission, products and services, and overall market strategy. The best way to approach key customers is to view them as a separate segment of the market. A company’s key customers will likely have higher demands and lower growth than newer customers but are more important. Due to the prolonged relationship, many key customers will rely on a business like a support team rather than a supplier. A support team needs to be able to respond quickly to business communications and remain agile in order to properly manage these key customers. This type of relationship comes with major benefits when handled correctly. Key customers are great for pitching new ideas and testing new marketing strategies since they are more loyal than newer clients. However, key customers can also be demanding of a company’s time and sometimes boundaries need to be set. It can be common for key customers to demand freebies or contact account managers on the weekends, which is a bad practice to maintain. That is why using a communications platform like Ascendo can help bridge gaps in communication and strengthen feedback loops in order to improve relationships with your key customers. How to Communicate and Manage Key Customers? The relationships with key customers are often complicated with longstanding contracts, communication precedents, and big egos, it’s important to treat these relationships with care. Companies should be available to quickly respond to all sorts of issues like technical support, product questions, and more. Maintaining a constant feedback loop as the support team for your key customers is crucial for keeping the relationship in good standing. This can look like bi-weekly check-ins, email newsletters, or an occasional conference call to gauge emotions on both sides. The constant feedback loop can help identify issues with a client, or a larger problem with the products or services. Further, when working on a project or sales pitch, support teams should provide regular updates to make sure both parties are aligned on the progress and goals. Even if a client reaches out during a meeting or busy day, it’s important to send an email or quick message to acknowledge the issue and let them know you’re on it. Finally, another important area of communication support teams should be aware of is identifying broader customer requirements. Through Ascendo, support teams can quickly identify if issues are common amongst multiple clients, making it easier to identify larger technical issues. When done proactively, this will lead to improvements in products and services and a better relationship with the key customers. Gauging Client Satisfaction The best way to understand a problem is to analyze it with data points, which is where Ascendo comes in. Through Ascendo, support teams can quickly find common problems across multiple key customers in a fraction of the time it would normally take. Another method support teams can use are Client Satisfaction Surveys (CSAT Surveys). Most companies will send out a CSAT survey at least once a year to all clients and potentially more frequent or longer surveys to key customers. CSAT survey can help a company identify issues within their own reporting structure or other problems like communication, product/service issues, and more. Another important metric to look at is a company’s client turnover or churn. This measure shows how frequently a company retains or loses business. While high rates of churn are normal for certain industries, every company should understand what’s normal for their industry and own company to gauge past or present turnover rates. Finally, employees can be a great indicator of client satisfaction. Support teams should ask their own employees how the relationship is with their key customers. If key customers are being extremely demanding or difficult, it can cause high-performing employees to leave which will ultimately hurt the company and the key customer relationship. Management should always work to protect their employees and step in when the situation becomes unreasonable. Mitigating the Avoidable and Unavoidable Problems Support teams are generally going to tackle two types of issues, avoidable and unavoidable. No matter how much experience or planning a business does, there will always be both types of problems that need to be handled regularly. Unavoidable issues like client turnover and technical malfunctions should be prepared for as they are almost certain to happen. Having standardized practices in place to handle these issues will inform employees of their responsibilities and the appropriate actions to take place. Avoidable issues like missed meetings and general unpreparedness require a different approach. These types of issues can be mitigated by outlining best practices for your company so employees understand how business should be conducted. Different Business Models Offer a Variety of Solutions Another way companies have adapted to difficult situations is by implementing new business models. For instance, many B2B and B2C operations have adopted freemium models for customers who can’t afford the upfront cost of the product or service. This is most popular in mobile gaming and social media where companies make money by advertising to the customer rather than charging the customer for the product or service. By testing different business models, a company can segment different buyer groups and evaluate which strategies are most effective. This too can determine a company’s objectives and strategies moving forward. Educating your Team on Key Customer Management Understanding how to manage key customers is only half the battle. Making sure the entire support team understands how to manage and interact with key customers is a huge factor in achieving revenue goals. Since this is so important, management should never assume that employees already know how to manage key customers, even if employees are experienced and highly trained. Management should create a system of best practices and standards for key customers that should be reviewed with all support teams. Further, this should be done on an annual basis since overall company strategies and market sentiments can change how support teams should approach key customers. For instance, if a company has experienced downsizing, it might require stricter communication boundaries to be implemented for employees to stay efficient. Key Customers are the Key to Company Success The value of key customers cannot be understated. Key customers are the bread and butter of any operation and are generally what companies count on for revenue goals and financial outlook. By using Ascendo, support teams can close the feedback loop with their more important clients. Through Ascendo’s platform, support teams can analyze communications data and discover issues that can improve products and services in a fraction of the time. When managed properly, key customers can be incredibly useful beyond the revenue they contribute. These customers are more likely to refer others to use your products and services and can also help fine-tune and identify issues and opportunities for a company. As long as there is constant, transparent communication, your key customers will stay satisfied. Learn more, Great Customer Service and Products Create Market Leaders Role of Customer Support Team in Engagement and Churn

Companies that make great products distinguish themselves from the competition through even great service. What is Customer Service? Service means the given product works most of the time and when there is an issue or question, they get solved quickly. Customers demand support on their terms – any time – any channel/mode of interaction – any type of issue. They want expert help and their perception of the product comes through how they receive the service. Better service they receive better their perception and they stick to the brand longer and buy more from the company. Service Model Opportunities Companies strive to offer support on multiple channels but the core of their support model is around some kind of a ticketing system that inherently translates in all their service delivery models – be it chat, email, portal, web, phone, mobile, SMS, and other media. The service model is built around more “chat” and less problem resolution. Customers are forced to provide a lot more information and are involved in a lot of back and forth before the issue becomes clearer to the support agent at the company. Even a 24-hour chat service that is available to the customers lacks actual troubleshooting and resolutions and is more fine-tuned for simple questions or just chat (pun intended). For companies that extend customer service to include Field Engineers and/or Professional Services teams, the challenges are even higher. The troubleshooting and resolutions require one or more visits to the customer site in addition to spending time on live phone calls or conference calls. In today's world, customers are more interested in talking about their roadmap, value, and success instead of coordinating and spending time on investigation and troubleshooting. It is not that companies are not interested or want to solve issues quickly. The opposite is true. They want to increase self-service resolutions, they want to know customer issues before even their customers become aware of them, they are keenly interested in knowing silent “brewing issues” before they become a fire drill, they want fewer escalations, they want to spend more time in their customer roadmap, their engineering team wants to collaborate with the support team so they can improve the product in a way that drives better customer experience when they need to hire new engineers don’t want to spend months training them, they want to teach and transfer the know-how built over time by a handful of experts to the rest of the members, prioritize to work on important things that matter to their customers, and avoid spending hours on phone and conference calls on customer escalations. When their professional services and field engineers get involved, they want them armed with game plans and needed materials (in case a physical component is involved in the repair services) so they can avoid scheduling multiple visits to the customers to solve issues. Logistics and quality want to spend less time on RMAs and stocking parts and more time increasing the lifespan and optimizing their logistics operations. Simply, companies want to do less chat during support and more conversations on customer experience and success! Interestingly the goals of both the customers and companies are aligned – spend less time on unnecessary chat and more time on issue resolution and avoidance. Then why is this becoming a constant challenge and theme in customer service? The reasons are: Customer support and customers spend more time deciphering the symptoms to understand the root cause. Finding the right solution to the given problem. Finally, understanding and predicting proactively the potential issues that might come up so the companies can be better prepared with optimum solutions and materials, where they are applicable. Ascendo has been built and continuously enhanced to solve this challenge. We are making the technology built with Artificial Intelligence and ML for this specific purpose and making it work for the demands of customers and objectives of companies when it comes to customer service. Ascendo helps companies to: Do less “chat” and resolve fast. Serve more, fast, accurate, and reduce escalation. Replace less and repair quickly. Stock less and avoid downtime. Working with some of the wonderful brands in both Business to Business (B2B) and Business to Consumer (B2C), we are elevating the experience in every interaction by the customer and agents. Learn more, Tips To Stay In Continuous Touch With Customers 10 Ways to Improve Customer Services With AI