Category Archive: Uncategorized
Businesses are always looking for ways to improve their service management practices to increase efficiency and make customers happier. One way they’re doing this is by integrating Artificial Intelligence (AI) into their service delivery processes, which helps them achieve their goals by transforming how they deliver their services.
Joe Lang, VP of Service at Comfort Systems USA, recently joined Aquant’s Service Principal, Sidney Lara, for a webinar, Close the Skills Gap & Cut Service Costs: Unveiling Aquant’s 2024 Field Service Benchmark Report.
Joe and Sidney discussed strategies for implementing AI during their session. They highlighted the Shift Left concept to shorten the service lifecycle and significantly reduce service costs. Organizations can improve customer outcomes and build better customer relationships by streamlining service delivery with AI.
Let’s dive into the key takeaways from the webinar.
The Six-Step Framework for Transformation
- Collect and understand data: The first step involves gathering comprehensive service data to understand customer and workforce interactions thoroughly. Organizations can set a robust foundation for accurate insights by standardizing and sanitizing their data. By analyzing documented data, Sidney illustrates how orgs can identify opportunities to resolve issues over the phone or through self-service platforms, such as chatbots, significantly enhancing customer satisfaction while reducing the need for in-person visits.
- Dive deep into service issues: Understanding the nature of service issues is crucial. Joe’s experience at Comfort Systems USA spotlights the necessity of moving beyond symptom-fixing to address the core problems. AI facilitates deeper understanding, which sifts through historical data and expert insights to pinpoint effective solutions.
- Leverage expert knowledge: During the session, Sidney pointed out that 30% of solutions are derived from highly skilled technicians’ unspoken or implicit knowledge. Incorporating this human element into the AI engine ensures that the most effective resolutions are identified and applied.
- Operationalize AI for targeted solutions: With insights in hand, the next step is operationalizing AI with predictive analytics. Joe shared how Comfort Systems is taking this step by releasing Aquant’s Service Co-Pilot to empower technicians to solve issues independently before escalating them, enhancing first-time fix rates and overall operational efficiency. Organizations can significantly improve service delivery and cut costs by investing in technology that enhances technicians’ capabilities and reduces reliance on external help.
- Establish continuous improvement loops: The journey doesn’t end with the implementation. Continuous measurement and feedback are vital to refine and enhance service interactions. Maximizing the benefits of new technology requires empowering your team during onboarding.
- Shift Left for proactive problem solving: Ultimately, the goal is to solve problems before they occur. Sidney and Joe stressed the potential of self-service and remote diagnostics in reducing the need for in-person visits, enhancing customer satisfaction, and lowering costs.
Why It’s Time to Shift Left Now
The stakes in service management have never been higher. Rising customer expectations demand immediate, effective responses. Sidney’s insights reveal a world where customers prefer self-service options.
Moreover, the talent shortage and skills gap present formidable challenges. Joe emphasizes the significance of data in bridging gaps, while Sidney advocates for utilizing AI, which provides a clear path for service organizations.
Aligning every team member’s capabilities to match the top 20% of your workforce could reduce your service costs by up to 22%. And it all starts by utilizing valuable insights, tools, and benchmarks.
The journey that Sidney and Joe embarked on is not just a story about adopting new technologies. It also represents the strategic, data-driven mindset required to succeed in today’s service industry. By following their six-step framework, organizations can navigate the complexities of modern service delivery, achieving efficiency and cost reduction and building lasting relationships with customers.
The time to embark on this transformation is not tomorrow; it’s today.
Ready to adopt the Shift Left approach? Request a demo today to learn more.
Courtney Stafford, Marketing Programs Lead, Aquant
Hi there! I’m the Marketing Programs Lead at Aquant. My passion lies in creating unique in-person and virtual experiences, crafting engaging content, sharing best practices—and, most importantly, empowering service teams like yours to thrive.
Generative AI is one of the most groundbreaking technologies in recent years, fueling innovations in every field from manufacturing to service. It’s also one of the fastest-evolving technologies. Here’s a primer on the AI concepts you need to understand in the context of service and the customers you support.
Bookmark this generative AI glossary — and have it ready for your next meeting. We won’t tell anyone.
- Bias: Unfair or skewed judgments, decisions, or outcomes in machine learning models or algorithms. Bias can arise from biased training data or the design of the algorithm itself and can result in discriminatory or unfair behavior, particularly in areas like automated decision-making, lending, and hiring.
- Chatbot: A computer program or AI application that simulates human conversation through text or speech. Chatbots can be used for various purposes, including customer support, information retrieval, and virtual assistants. They use natural language processing techniques to understand and respond to user queries or commands.
- ExpertSync: Aquant proprietary process by which we datafy the knowledge of your seasoned experts into the diagnostic AI solutions. ExpertSync has proven to elevate the quality of problem-solving in service through the unique combination of Aquant AI drawing from a specifically curated mix of historical data, unstructured technician notes, and validated expert solutions.
- Generative AI: A subset of artificial intelligence that focuses on creating or generating new content, such as text, images, music, or other media. Generative AI models, like GPT-3 and GANs (Generative Adversarial Networks), have the ability to produce creative and realistic content based on patterns and data they’ve been trained on.
- Generative Pre-Trained Transformer (GPT): A family of large-scale language models developed by OpenAI. GPT models are pre-trained on vast amounts of text data and can be fine-tuned for various natural language understanding and generation tasks. GPT-3, for example, is known for its ability to generate human-like text and perform a wide range of NLP tasks.
- Horizontal AI: Artificial intelligence technologies and applications with broad and general functionality across various industries and use cases. These AI systems are not industry-specific and can be applied horizontally to address many problems. For instance, NLP tools used in service, finance, e-commerce, and other sectors are considered horizontal AI.
- Large Language Model (LLM): An artificial intelligence model trained on massive datasets to understand and generate human language. These models, such as GPT-3 and GPT-4, are known for developing coherent and contextually relevant text, making them useful in various natural language processing tasks.
- Natural Language Processing (NLP): A field of artificial intelligence focusing on the interaction between computers and human language. NLP technology enables computers to understand, interpret, and generate human language, making it valuable for tasks like language translation, sentiment analysis, and chatbots.
- Service Language Processing (SLP): Aquant’s unique NLP engine, which is designed to read service language and identify observations/symptoms and solutions described in free text.
- Vectors: Numerical representations of words in a high-dimensional vector space. These representations capture semantic relationships between words, allowing algorithms to understand the meaning and context of words based on their proximity in the vector space. Word vectors are fundamental in natural language processing tasks like word similarity, sentiment analysis, and machine translation. They are typically generated using techniques like Word2Vec or GloVe.
- Vertical AI: Artificial intelligence systems and applications for specific industries or niches. These AI systems are highly specialized and optimized to address particular tasks, challenges, or workflows within a specific vertical market. For example, vertical AI solutions may include healthcare AI for medical diagnosis or manufacturing AI for optimizing production processes.
Data & Accuracy
- Data Cleansing: Identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This includes removing duplicate records, handling missing values, and rectifying formatting issues to improve data quality. It is also known as data cleaning or data scrubbing.
- Data Ingestion: Collecting, importing, and preparing data for machine learning or data analysis applications. It involves acquiring data from various sources, transforming it into a suitable format, and storing it in a data repository for further analysis.
- Data Integrity: The accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data is not corrupted, altered, or compromised, maintaining its quality and trustworthiness for analysis and decision-making.
- Data Validation: A set of checks and procedures to ensure data is accurate, consistent, and compliant with predefined criteria or standards. It involves verifying data quality through various validation techniques, such as cross-validation, to assess model performance or data reliability.
- Hallucinations: A situation where a model generates incorrect or fabricated information in its output. It occurs when the model produces text not based on factual or accurate data, leading to misleading or erroneous results.
- Parameters: Internal variables or weights a machine learning model uses to make predictions or decisions based on input data. These parameters are learned from training data and are essential for the model’s ability to generalize and perform well on new, unseen data.
- Prompt Engineering: Designing and crafting specific instructions or queries (prompts) to elicit desired responses from natural language processing (NLP) models. It involves formulating prompts that guide the model’s output toward the intended information or behavior.
- Reinforcement Learning: A machine learning paradigm in which an agent learns to make decisions or act in an environment to maximize a cumulative reward. The agent learns through trial and error, adjusting its actions based on feedback from the environment. This approach is commonly used in applications like game playing, robotics, and autonomous systems.
- Guardrails: A set of predefined rules, constraints, or ethical guidelines that are put in place to guide the development, deployment, and usage of artificial intelligence systems. These guardrails serve as boundaries or limits to ensure that AI technologies are used responsibly and ethically. They help prevent AI systems from engaging in harmful or undesirable behaviors and promote transparency, fairness, and accountability in AI applications. Guardrails may include guidelines for addressing bias and discrimination in AI models, ensuring data privacy and security, and complying with legal and regulatory requirements. They are an essential component of AI governance and help organizations and developers navigate the ethical and societal implications of AI technology.
- Privacy: Protecting individuals’ personal information and data when AI systems are involved. It ensures that AI applications and data processing activities adhere to privacy laws and regulations. Measures may include data anonymization, consent management, and access controls to safeguard sensitive information.
- Security: Protecting AI systems and their data from unauthorized access, breaches, and cyber threats. It encompasses measures such as encryption, access controls, secure coding practices, and regular security audits to mitigate risks associated with AI deployment. Security is crucial to prevent AI systems from being exploited or compromised.
Aquant’s Service Co-Pilot offers a distinct edge over other generative AI tools because of its deep understanding of service and the quality of data it collects. Request a demo to learn more about how these terms and tools fit into your AI journey.
Standards for customer experience are higher than ever. On each episode of Aquant’s Service Intel Podcast, we sit down with leaders raising the bar and creating incredible experiences for their customers. These top names in the industry have all agreed to share what they’ve learned about navigating today’s service landscape so our listeners can not only get inspired, but put their own bar-raising service plans into action.
As technology evolves and customers demand more efficient and cost-effective solutions, third-party service providers are increasingly important in delivering high-quality field service.
In a recent podcast interview with Service Intel, ABB’s Vice President of Service, Salvador Accardo, discussed the rise of subcontractor work and third-party field service teams and how his company adapts to this changing landscape.
According to Salvador, several factors are driving the growth of third-party service providers. First and foremost, there’s an increasingly finite amount of resources among teams, and at the same time, customers are looking for more flexible and customized solutions. They want services tailored to their specific needs rather than a one-size-fits-all approach.
In addition, many companies are looking to reduce costs and streamline their operations. By outsourcing their field service needs to third-party providers, they can focus on their core business activities while ensuring that their customers receive high-quality service.
For ABB, this shift towards third-party service providers presents challenges and opportunities. On the one hand, it means that ABB needs to be more flexible and responsive to customer needs. They must be able to work with a wide range of service providers and integrate their systems and processes with their partners.
On the other hand, it also presents an opportunity for ABB to expand its reach and increase its customer base. By working with third-party service providers, ABB can offer a broader range of services and solutions and tap into new markets and industries.
So how is ABB adapting to this changing landscape? Salvador explained that the company is focusing on three key areas:
Partnership: ABB is partnering with a range of third-party service providers to ensure they can offer their customers the best possible service. This includes developing strong relationships with partners and sharing knowledge and best practices. This requires a balance between internal vs external. There needs to be an understanding and clear communication regarding responsibilities and swim lanes between all workers – both internal and external – because oftentimes, internal folks can view external workers as a threat.
Technology: ABB invests in technology to help them work more effectively with third-party service providers. This includes developing software and tools that can integrate with their partners’ systems and provide real-time data and insights.
Training and tracking performance: ABB is providing training and support to its employees and third-party service providers to ensure they have the skills and knowledge needed to deliver high-quality service. Regarding tracking performance, ABB conducts monthly health checks and quarterly quality reviews to ensure they’re meeting both company and customer standards.
Overall, Salvador emphasized the importance of understanding the competitive environment, given that most contractors are likely also working for competitors, safeguarding intellectual property presented to third parties, and ensuring the quality of work through regular auditing. He noted that while the rise of third-party service providers presents challenges, it also presents an opportunity for companies like ABB to innovate and improve their services.
Lastly, Salvador stressed not to be afraid of the third-party model. The field service industry is undergoing a significant shift, and third-party service providers are playing an increasingly important role. By partnering with these providers, investing in technology, and providing training and support, companies can adapt to this changing landscape, continue delivering high-quality service to their customers, and maintain a competitive advantage.
Listen to the full podcast here!
Today’s service delivery culture is an on-demand one.
When customers need to troubleshoot an issue or order replacement parts, they want quick and efficient solutions. And in customers’ eyes, it’s even better if organizations can offer a self-service option available around the clock.
Innovative service leaders are approaching this new reality with AI in mind. They are finding ways to leverage intelligent self-service chatbots to:
- Intelligently troubleshoot product issues
- Direct customers to the replacement parts in an eCommerce experience
- Provide service 24/7
In this article, we will explore these three significant benefits of AI-powered chatbots for service operations, focusing on how they can reduce costs and increase parts revenue. We’ll also take a closer look at how Aquant is leveraging the power of AI to provide even more self-service options.
The Benefits of AI-Powered Self-Service Chatbots for Service Operations
Reduced Inbound Calls
Hiring a team of customer service representatives is expensive. A customer calling a call center costs companies an average of 10 times more than if they were to utilize a self-service option.
AI-powered chatbots offer customers access to easy-to-use, intelligent troubleshooting apps that help them solve their issues instantly and without human intervention.
These chatbots give your customers direct access to the combined knowledge of your best technicians, not just what’s available on your website. The chatbot asks your customers simple questions about the issue they’re experiencing and dynamically reviews your historical service data to match symptoms with solutions. It can also provide recommended actions, replacement parts, or next steps within seconds.
By reducing the workload of human agents, chatbots can also help businesses save on labor costs, leading to increased profitability. Overall, this results in happier customers and increased cost efficiency.
Intelligent Troubleshooting & Increased Sales of Replacement Parts
Companies are also leveraging AI-powered chatbots to help sell more replacement parts. This seamless experience happens when the chatbot makes recommendations to customers based on the outcome of the self-serve troubleshooting experience.
When these chatbots review customer issues, they often understand precisely which parts should be replaced to get things working again. After the chatbot recommends a solution, it can also quickly link customers to a buying experience on your website.
These are high-intent site visitors with a clear need, so directing them to a part within your eCommerce experience is a slam dunk for increasing part sales and generating revenue.
Customers expect immediate assistance, even outside of regular business hours. AI-powered chatbots can provide 24/7 support, ensuring customers can always access help when needed. This can increase customer satisfaction, as customers do not have to wait until the next business day to resolve their issues.
Chatbots can also handle multiple inquiries simultaneously, so customers do not have to wait in long queues or on hold. Overall, this leads to a more positive customer experience.
Q: How can a chatbot troubleshoot issues for my customers?
A: AI-powered chatbots review your historical service data to align symptoms with solutions. Based on a customer’s observation, the chatbot dynamically pulls the next best question to ask. This helps it zero in on the most-likely fix.
Q: Are these chatbot experiences a static decision tree?
A: No. AI-powered chatbots from Aquant dynamically review data to present the fix with the highest degree of accuracy. The chatbots are given feedback from several sources on their recommended fixes and become more accurate as time passes.
Q: Can chatbots handle multiple inquiries simultaneously?
A: Yes. Chatbots can handle multiple inquiries simultaneously, reducing wait times for customers.
Q: Can the chatbot experience be customized to match my website’s branding?
A: Yes. The chatbot experience can be customized to your needs to create a cohesive brand experience that builds customer trust.
AI-Powered Chatbots and Search Tools from Aquant’s Service Intelligence Platform
Aquant’s AI-powered chatbot functionality drives significant efficiency gains, improved customer satisfaction, and cost savings for service organizations in their Shift Left journey. We work with leading companies to make the minds of their best technicians and customer service representatives available instantly to their customers online and deliver high-intent buyers directly to their digital sales teams’ eCommerce experiences.
These are just some ways that AI for service operations puts service leaders in the limelight. Check out more interactive Service Intelligence demos to see how your organization can benefit at every stage of the service lifecycle.
Today’s service leaders have to-do lists with tall orders:
- Figure out how to quickly train incoming technicians to fix increasingly complex machinery…
- Upskill and teach new hires to be just as savvy as their more-experienced colleagues, who are retiring in waves…
- Create incredible customer experiences, especially in an era where fast, efficient, personalized, and on-demand service is the expectation…
It’s enough to make any service leader’s head spin, but AI for Service Operations can help.
AI-powered technologies have seemingly burst into the news recently—but the reality is that companies have been perfecting these tools in the service space for many years. Built to analyze your entire service landscape, the right AI tools can improve service outcomes by providing prescriptive, data-driven insights. When leveraged correctly, these insights can help companies deliver better, more profitable customer experiences.
If you’ve ever thought, “If only I could just get information out of my veteran techs’ minds and document it my entire workforce…,” AI is ready to spring into action.
Read on to explore the benefits of AI for Service Operations and learn how it can help companies streamline their service operations, enhance customer satisfaction, and boost revenue. We’ll also cover some of the latest AI tools and techniques that companies can leverage to improve their service operations and stay ahead of the competition.
What is AI for Service Operations?
AI for Service Operations is a term that refers to artificial intelligence-powered technologies that can streamline and optimize service operations. This is primarily done through the implementation of Service Intelligence Platforms. AI-powered tools can analyze historical service data, generate fixes for any service issue, improve customer experiences, and reduce operational costs.
Companies that manufacture and service a range of assets—including medical equipment, motor vehicles, printers, food service equipment, and engines—are already leveraging AI for Service Operations. And you can, too.
Benefits of AI for Service Operations
AI for Service Operations offers a range of benefits for companies. Here are some of the advantages of AI for Service Operations:
|Improved Customer Experience
|Get personalized troubleshooting and repair recommendations. Use these suggestions to improve issues quickly and efficiently without ever needing to speak to a customer service rep.
|Quickly identify root causes for service issues. This frees up service professionals to focus on more complex tasks—improving overall productivity.
|Reduced Operational Costs
|Cut service costs by enabling “Shift Left” strategies. This provides wider access to the troubleshooting and repair recommendations typically found in the skill sets of your most experienced technicians. When best practices are made available all (including junior technicians, customer service reps, and even customers!), companies can reduce inbound calls, truck rolls, parts usage, and escalations.
|Dynamically analyze service data to identify top training needs, asset issues in the field, and potential customer escalations before they occur.
How to Leverage AI for Service Operations
To effectively leverage AI for Service Operations, companies must have a clear understanding of the business needs they want to solve, the data they have on hand, and the available AI-powered tools and techniques. Check out our step-by-step breakdown:
- Define business needs across your service landscape: Determine needs and goals at the customer service, field service, engineering, and product team levels. This will be infinitely helpful when aligning AI-powered solutions with overall business strategies.
- Review your data practices: AI-powered tools run on data, so examine what data is already being captured and stored. Additionally, use this exercise to identify missing types of data—these points can be used to fill in your service strategy’s blanks.
- Choose the right AI tools: Decide what type of AI tools are the best fit for your most pressing needs (more on that below!).
- Prepare to drive employee adoption: Prepare to train employees on how to use these tools effectively and integrate them into their service operations. This is especially beneficial as most AI-powered tools get more accurate as they’re used.
- Monitor and evaluate results: Keep tabs on the results generated by AI-powered solutions. This will ensure that they are delivering the desired business outcomes. If not, adjust strategies accordingly.
AI-Powered Tools and Techniques for Service Operations
Here are some of the latest AI-powered tools and techniques that companies can leverage to improve their service operations:
- Natural Language Processing (NLP): NLP can analyze historical service data, organize and label it, and attach root causes to solutions at scale.
- Self-Service: Self-service experiences can provide instant customer support and assistance. Additionally, they provide end-users with direct access to dynamic troubleshooting experiences. The experiences can identify root causes of issues, suggest best fixes, and offer customers direct links to purchase parts—or elevate their issue to customer service.
- Intelligent Troubleshooting: Intelligent Troubleshooting can review historical data in an instant. The findings can present users with a series of questions to help them zero in on the root cause of an issue. It will also provide a recommended solution based on the likelihood of success.
- Prescriptive Analytics: Prescriptive analytics can identify top opportunities for the largest efficiency gains and cost-savings. It will also present directions for how to execute against them.
FAQs about AI for Service Operations
Q: Which industries can benefit from AI for Service Operations?
A: A wide range of industries can benefit from AI for Service Operations, including (but not limited to!) medical, automotive, heavy machinery, and industrial printers.
Q: How can AI for Service Operations improve customer experience?
A: AI for Service Operations can improve customer experience by providing personalized services and recommendations, reducing time to resolution, and reducing the need for future repairs.
Q: What are some of the latest AI-powered tools and techniques for Service Operations?
A: Some of the latest AI-powered tools and techniques for Service Operations include self-service chatbots, natural language processing, intelligent troubleshooting, and prescriptive analytics.
Read on to learn more about AI for Service Operations from Aquant
Aquant’s Service Intelligence Platform is the tool of choice for major OEMs using AI for Service Operations. With Intelligent Troubleshooting and Prescriptive analytics, we’ve changed the way companies like Canon, John Deere, Johnson & Johnson, and Stryker provide better, faster, higher-margin service.
Explore interactive demos of Aquant’s Service Intelligence tools today and see just how far AI for Service Operations has come.
Why is Knowledge Management Essential for Service Operations?
It’s no secret that today’s service operations teams face significant challenges: machines are more complex, an aging workforce is retiring, and the skills gap is increasing.
Additionally, service leaders are struggling to hire, onboard, and upskill new service reps and field service technicians fast enough—especially in this new era of service, where customers expect on-demand B2C experiences.
To address these pain points, service organizations need to find ways to leverage and share their collective knowledge, including intel from top technicians and historical service data. This is best achieved by collecting, analyzing, and sharing data in a format easily accessible and understood by current and future technicians.
And that’s precisely where the right knowledge management system can help.
Built to store and retrieve data, improve understanding, increase collaboration, and align processes, knowledge management helps service organizations by:
- Improving efficiency and productivity: Simple errors, unnecessary parts usage, and callbacks can be reduced (or avoided!) when orgs use their service data to identify the best solutions for different scenarios.
- Enhancing quality and consistency: Orgs can ensure its services meet and exceed customer expectations. Knowledge management systems capture and share details around customer needs, preferences, and feedback, which helps orgs tailor their service delivery accordingly.
- Empowering employees and enhancing collaboration: A culture of knowledge-sharing and collaboration is created when orgs provide employees with relevant knowledge and up-to-date best practices. This encourages teams to leverage their collective expertise and enhance their problem-solving capabilities.
What Does a Successful Knowledge Management Program Look Like?
A successful knowledge management program for service operations should include the following elements:
- Formalized knowledge capture and creation: Service organizations should identify and capture relevant knowledge from various sources. These include (but aren’t limited to) customer service transcripts, free text, technical support, product engineering, product manuals, and historical service data.
- Organization and storage: Teams need to organize their knowledge assets to make them easy to find, access, and use—especially for brand-new employees. Imagine the value of a new field service tech or customer service team member being able to reference the answer to a customer’s questions in real time.
- Sharing and dissemination: Knowledge assets should be accessible by employees, partners, third-party teams that service their products, and even customers in AI-powered chatbots. Intel can be shared in various ways, including training programs, knowledge bases, and chatbot, mobile, and desktop applications.
- Closed-loop knowledge improvement: Knowledge-sharing tools should encourage ongoing feedback. For example, technicians using a mobile troubleshooting app should be able to give the tool feedback about whether or not the recommended fix solved the issue. This update should be looped back into the ecosystem in order to make outputs more accurate.
How to Implement a Knowledge Management Program Within Service Operations
When implementing a service ops knowledge management program, you should remember your business goals. Some common plans include:
- Decreasing overall service costs and increasing service revenue
- Improving First Time Fix rates or Remote Resolution rates
- Offering customers self-service opportunities, especially when the fixes are simple
Follow these steps to put your team on the path to a successful implementation.
1. Pick your objectives wisely.
Decide which teams are the primary and secondary users, assess where knowledge is complete or lacking, and determine ideal outcomes at business and service levels.
Some questions you can ask include:
- Is the program only for your field service technicians — or could your customer service team and partner teams benefit too?
- Who are the experts in your organization, and where do they sit?
- What does the organization’s performance look like six months after a successful implementation? What about a year?
2. Consider what you can (and should) measure.
Planning how you’ll measure success shouldn’t be overlooked.
Companies need to be thoughtful when choosing the KPIs they will measure, mainly because it’s essential to make sure they can be measured in the first place. Additionally, it’s recommended to set realistic goals in advance.
There are the typical standard top five major KPIs every service org should measure. But how can you expand your goals beyond those traditional metrics? For instance, you can consider reducing inbound calls by 20%, increasing remote resolution by 30%, or increasing First Time Fix Rates by 40%.
3. Bring in your experts.
A company’s knowledge management platform is incomplete without your experts’ input. These can be veteran field service technicians, customer service reps, service and operations managers, and even leaders in digital transformation and IT.
To earn buy-in from them, present the opportunity at hand (the “why”) and ask for their input in gathering the knowledge (the “how”).
4. Select the tools that will meet your needs.
Knowledge bases should be detailed, instantly accessible, and available to all relevant teams.
And most importantly: they should get better over time.
The latter can be challenging to manage in a static document. That’s why more service teams are turning to service intelligence platforms that dynamically offer the best fix suggestions, processes, or guides directly in front of anyone that needs it. These include customers, customer service reps, technicians, operations leaders, or partners.
Check out some interactive examples by clicking on the buttons below:
5. Monitor adoption and plan to improve.
Robust knowledge management tools offer analytics that shows changes in service metrics over time. This can help companies identify which teams are engaging with the tools and which aren’t.
Companies should consider sharing adoption and performance metrics across their teams to celebrate early wins and encourage adoption.
Lastly, there needs to be a robust system in place to gather feedback and close the loop so that the knowledge base can continue to grow and evolve with best practices.
Q: What are the common challenges in implementing a knowledge management program for service operations?
A: The biggest challenges most often include resistance to change, poor planning, lack of leadership support, and team silos that form barriers to knowledge sharing.
Q: What are some best practices for knowledge sharing in service organizations?
A: Some best practices include adopting a culture of knowledge sharing, framing the opportunity at a business-wide and individual level, providing incentives for program adoption, using the right technology to distribute knowledge, and rewarding team members who lead the way.
Q: How can service organizations measure the impact of their knowledge management program?
A: Service organizations can measure the impact of their knowledge management program by tracking KPIs, including inbound call volume, remote resolutions, First Time Fix rate, and Resolution Cost.
Service Intelligence from Aquant is your ticket to improved knowledge management.
Knowledge management has become critical for service operations leaders facing talent shortages, demanding customers, and complex fixes.
Taking steps to get your best technician’s knowledge out of their heads and codified for your entire organization is a winning strategy.
Read on to discover the game-changing benefits of AI for service operations.
Shorten the service lifecycle, improve customer relationships, and reduce service costs.
Over the last decade, the evolution of service has resulted in a widening gap between rising customer expectations and growing workforce challenges. Accordingly, organizations have had to get creative and look at new service delivery methods to meet customer demand. But, introducing multiple channels to serve has created more complex service experiences with a lack of resources to address these challenges.
Traditionally, different channels (i.e. call center, field, self-service) operated under various objectives, resulting in a disjointed customer service experience. One common example in service is how call center agents are measured on average handle time. This metric is not the problem in itself, but what if you found out that spending an extra 10% of the time on the phone could result in a reduction of 10% in truck rolls, subsequently improving profit margins? Wouldn’t most organizations want that? But because service is measured in silos, companies fail to create the best and most efficient customer experience.
Service is becoming more strategic than ever, and companies are now evolving to look at the customer journey holistically across multiple channels. With advancements in data and AI-powered technology, organizations can strategically identify the most cost-effective and efficient way to approach a service issue. It all starts with shifting your customer experience left. But what does that mean?
Shifting Left: What Does it Mean?
Simply put, the Shift Left concept is about resolving as many service issues as quickly and efficiently as possible. That sounds like a no-brainer, right? The idea is to use data to strategically identify the areas in your business where you can make a quantifiable impact that improves your bottom line. Whether that means reducing escalations and improving first-time-fix, identifying truck rolls that could’ve been resolved remotely, or even turning simple calls into a self-service experience, shifting left is about taking every service interaction and pushing it to the left as much as possible.
Today, 1 in 3 service calls results in a truck roll (i.e. the most expensive way of servicing customers – costs can be $2500+). Of those, 1 in 4 results in multiple visits. This is not sustainable, especially given the current economic climate. Instead of resorting to a costly dispatch/truck roll every time, service teams are better off resolving the issue through remote resolution, virtual assistance, self-service, etc.
For example, could you have a virtual agent (like a bot) point the customer to the right knowledge base article or some self-solve instruction to be able to fix it? Or, even better, before the product breaks down, could you proactively alert the customer to interact with the device, machine, or whatever product they’re working with, and provide step-by-step instructions to the customer in the most intuitive manner so that they could avoid downtime altogether?
The objective behind the “shift left approach” is to shift the resolution closer and closer to the end customer so that they don’t even need to reach into the enterprise. By shifting even further to the left, customers can quickly access critical information without escalating an issue. And, as highly skilled technicians make their expertise and insight more available to less experienced colleagues, the less-experienced staff can gain organizational knowledge and begin sharing it with their customers. In doing so, an entire organization raises itself to a higher place intellectually. Every time you shift left, there is a quantifiable business benefit: you save money and increase customer satisfaction.
Check out our companion piece, How to Shift Left: Your Six-Step Framework, to learn how your organization can start your Shift Left journey.
Service leaders, we’ll be frank with you: the old way of service doesn’t work, especially in today’s economically-uncertain landscape.
One of the top questions that service leaders are grappling with today is, “What steps should I take to improve service outcomes while managing costs?” If this sounds familiar, grab your (free!) one-way ticket to Service Leaders Spring Break.
New data shows that top organizations are turning away from traditional service models and looking towards a new frontier — one that prioritizes remote- and self-service options, top-tier customer service, and service knowledge on demand. That’s why this year’s event is all about training and retaining all-star teams, data analytics for service leaders, and quickly and efficiently achieving accurate resolutions in an era of self-service.
Here’s how Service Leaders Spring Break offers a fresh perspective on service:
- Learn how to upskill your service team in record time — and with record efficiency. Nearly one in three service leaders reported upskilling technicians and designing training programs as their most difficult challenge. But the best way to hire, retain, and upskill your employees is to invest in technology that offers the best knowledge-retention tools and professional support. Teams that feel empowered to take on the toughest challenges have better retention rates and provide unmatched customer experience. A bonus: if everyone had the knowledge and skills to perform like the top 20% of the workforce, service costs could be reduced by 21%.
- Get comfortable with data analytics for service leaders — even if you don’t have a data science degree. There are many routes that your company can take to start tackling the skills gap, whether it’s via centralizing intelligence, leveraging AI, or using documentation to improve workforce performance. Aquant created the blueprint for collecting, storing, managing, interpreting, and applying data in ways that drive your service goals forward. You’ll learn how to use your data to start taking action, so all emerging solutions will apply to your use case.
- Go from missing apparent signs of a customer escalation to using your data to create the seamless, preventative, low- to no-touch service experience that customers prefer. You can reduce costly service interactions by preventing escalations at every process step. We’ll explore strategies for quickly and efficiently achieving accurate resolutions in every service lifecycle stage.
Big problems require expert solutions, so we’ve assembled an all-star panel of service greats who have successfully charted their organizations’ paths:
- In a chat moderated by Ashley Bewick (Aquant), Tamra Call (Johnson Controls), Amber Porter (3D Systems), and Paige Gourley (Thermo Fisher Scientific) will introduce you to the new faces of service — and share their thoughts about hiring, shifting work environments, and recruiting advice, especially from the female perspective.
- Juan Cruz, Jr. (Haemonetics), Chris Westlake (Generac), and Anthony Billups (Comfort Systems) are providing a crash course on closing the skills gap from the three perspectives that matter most: people, processes, and technology.
- Lotem Alon and Tim Burge of Aquant share expert tips for collecting, analyzing, and applying data in ways that drive service goals forward.
- Sidney Lara and Rich Marchetti of the Aquant team show you how to create data strategies, establish internal benchmarks, and improve your baseline measurements to establish business goals.
If you haven’t yet secured your spot, there’s still time to celebrate Spring Break with us. Register today to save your seat.
The medical device service industry continues to expand while other sectors are experiencing downturns. But increased demand alone can’t save it from costly challenges such as the skills gap, says Aquant’s 2023 Medical Device Service Benchmark Report.
Read on to learn why the medical device industry’s top-performing companies decided that now is the ideal time to invest in data-driven tools that improve service outcomes.
This year’s benchmark data was gleaned from:
- 44 organizations
- More than 4.6 million work orders
- Over 15,000 technicians
- Over $3.4 billion in total service costs
- An average of 3.5 years of service data per company
This year’s critical findings include:
- The medical device industry remained busy. With a 2.5% increase in field events and a 6% increase in technicians, the medical device sector had to keep up with the pandemic’s demands.
- The sector experienced cost savings thanks to strategically-leveraged technology. Many organizations left behind traditional models to adopt remote- and self-service options, as evidenced by the 4.5% increase in First Time Fix Rates and the 34% increase in Time Between Visits. These improvements can be credited to technology, self-service options, and remote tools. Most importantly, this shift resulted in a 4% decrease in Resolution Costs.
- However, the medical device industry still faces serious challenges like the skills gap. Even though there was an increase in the number of technicians on the field, these techs completed 3% fewer work orders, indicating Time to Competency and upskilling issues. As these issues compounded, lower-performing technicians cost their organizations an average of 86% more than top performers. In the bottom 20% of organizations, this gap was as high as 203%.
- It’s essential to make critical changes that bridge service divides. Thriving organizations are combating high service costs, upskilling challenges, and other service delivery hurdles by:
- Creating trust in data and normalizing data sanitation.
- Prioritizing a workforce with diverse skills.
- Investing in relevant tools and training.
- Shifting to proactive service models.
- Leveraging shared knowledge.
- Empowering your team is worth it.
To get ahead—and stay ahead—medical device service organizations should invest in crucial technology initiatives to enhance knowledge, strengthen teams, and make strategic, data-backed decisions that put them at the forefront. Investing in the right tools, such as AI, to help your team perform like the top 20% of technicians can reduce your service costs by 30%.
Read Aquant’s 2023 Medical Device Service Benchmark Report to understand the state of the sector and how your org stacks up.