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.

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

The field service management (FSM) industry revolves around incidents that are handled by end employees. The incident life cycle begins with the initial submission and ends with a resolution to the problem. Incident tracking or ticketing systems have been around for years. Moving these systems to the cloud has been the biggest tech-based evolution in the industry so far. The Current State Of Service And Why It Needs To Change Today, the customer service industry is complex. Support technicians need lots of time to resolve issues, use more parts than they need, and are bombarded with having to support multiple products and versions. At the same time, hardware and software product companies are shifting toward service-driven revenue growth. Unfortunately, all aspects of service and FSM / ITSM systems are workflow-driven, time-based or monitoring-based and reactive. Automation of these systems alone will not fulfill the needs of the enterprise. It requires a new class of solutions that taps into the systems of record to create a system of intelligence through which decisions can be made. There are four primary reasons why faster resolution of service incidents should be made more effective: 1. The scaling issue: Incident tickets do not come from humans alone; there are also product-generated alerts. The assignment of a remote service agent can take a while. Agents need to be quick in diagnosing the problem as well as the resolution to the problem. Even with human-generated tickets, products getting so complex, with shorter product life cycles, that remote staff members need help in diagnosing problems and providing quick resolutions. It costs an average of $250-$1,000 per ticket. For hardware companies, costs go further up when having to send a technician for repeat fixes. SaaS companies resolving 86% of customers say they will pay more for better service. By 2020, customer service will drive sales more than product or price. 2. Complexity: According to Field Service’s Future Trends report, 81% of companies believe smart connected products will be implemented in the next five to 10 years. With the advent of robotics and innovations in manufacturing and materials, digital equipment out in the field is complex. Problems are diagnosed remotely. When an issue can’t be diagnosed, a dispatched technician goes in to diagnose the problem and then makes multiple visits to install the part in place. Instead of worrying if a technician has expert knowledge in regard to a particular problem, automation can help identify a problem, make sure the parts are in place and provide detailed instructions for fixing the issue so a field technician can focus on the customer relationship and procuring new opportunities instead of the diagnosis. 63% of agents are solving difficult problems in high-performing organizations versus 43% at under-performing companies. 3. Staff skills are changing: Service staff that supports legacy systems also need to be trained to support new systems. Baby boomers are retiring at a rapid rate, and as they ride off into retirement, companies are losing tribal knowledge. Sixty-five percent of service training is outdated. I’ve seen firsthand how millennial recruits no longer have the touch-and-feel experience or understanding of the equipment, and they don’t consider field service as a long-term career. So, new recruits need to be trained constantly. These new recruits come in with college degrees that companies want to utilize. Businesses also want to put the soft skills of new recruits to good use to increase revenue in customer-centric environments. Before a dispatch happens, a problem is identified, the part that’s required is procured, and cheat sheets on how to resolve the issue are readily sent to the field technician’s device. Having an automatic problem and solution resolution with cheat sheets on how to replace a part allows staff to focus on the customer rather than figuring out how to solve the problem. 4. The business model is evolving to a “pay as you go” model: As companies explore this model, the only front end to the customer is field service. With the new model, companies are forced to have an efficient way for incidents to be automated. As the business model evolves, there is also a servicing model evolution with democratized field service. As of this article, there are around 9,000 data science engineer jobs versus 34,000 field service engineer jobs posted on LinkedIn worldwide. If your company is considering automating aspects of its field service operations, here are a few tips to consider to ensure it’s a successful endeavor: Understand the evolution of data within your organization. Make sure you have the relevant data for the business problem you are trying to solve. Get agreement on the usage of automation from all stakeholders. The last thing you want is to implement something that has organizational pushback in regard to adoption. One of the best ways to increase adoption is via a value calculator. Changes in the field service business environment warrant new technology to help take it to the next level. In the next article, we will discuss the long-term benefits and value calculation/return on investment (ROI) of solutions that address the automated service world. Learn more, 10 Ways To Improve Time To Resolution Innovative B2B Automated Self-Service Technologies

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