As a dedicated customer service manager, you’ve convinced the higher-ups that AI is the coolest and fastest sidekick your team could ever have. Now, it’s time to throw on your CSI hat (Customer Service Investigator, of course) and uncover the mysteries of AI’s influence, and measure its outcome and ROI.

Why you should measure the impact of AI on your customer service performance

Tracking the impact of AI on your customer service isn’t just about numbers; it’s a strategic move in a landscape where customer expectations are always evolving. By monitoring key metrics, you get insights into how well AI processes are working. This helps you spot areas for improvement, streamline workflows, and, most importantly, boost the overall customer experience. This proactive approach lets you make smart decisions, use resources wisely, and keep your customer service strategy in sync with what your clients need. In a world where customer satisfaction is king, knowing how AI influences your operations is the key to staying ahead and providing top-notch support.

Also, don’t forget to flex those ROI muscles in front of your boss. It’s not just about impressing them with numbers; it’s about earning your credibility stripes. Demonstrating the return on investment isn’t just a pat on your own back – it’s your golden ticket to showcase that every dollar invested in customer service tools is a dollar invested in elevating team efficiency. So, when you’re on the budget battleground, armed with data-backed results, you’re not just asking for funds; you’re asking for the ammunition to increase your team’s productivity and boost your CS performance. Show them the digits, and watch your boss give you the nod of approval, knowing you’re not just spending but investing in a future of seamless customer service.

Which are the key KPIs to monitor when analyzing the ROI of AI-driven solutions

In customer service, every business has its own key metrics to track – what matters most varies from one company to another. For some, the focus might be on FCR, ensuring customer issues are resolved quickly in the initial interaction. Others might prioritize CSAT, aiming to gauge and improve how happy customers are with the service they receive. Ultimately, the right metrics to monitor depend entirely on the specific goals and needs of each business.

The crucial question then becomes: what impact does implementing AI features have on these chosen metrics?

But remember that when you check how well AI is doing in customer service, it’s not just about AI chatbots and automation handling tasks. It’s also about how AI helps human agents do their job better: instantly giving them all the information they need, helping them personalize their interactions or doing on-the-fly translations. So you should pay attention to two things: what the AI itself can do, and how much more effectively agents can work with AI’s help. 

It’s important to consider both of these improvements to really understand how AI is changing your customer service.

Based on our customers’ experiences with implementing AI, we’ve identified the following as the top metrics to track:

#1 Average Handling Time (AHT)

Once you’ve implemented an AI solution, track the AHT to assess how quickly your customer service resolves inquiries. A shorter AHT often indicates that your business is becoming more efficient in meeting customer needs. For a comprehensive view, it’s important to measure the handling time separately for requests resolved entirely by AI (without agent intervention) and for those that required human agent involvement. This will give you a clear picture of how AI is impacting your service efficiency and where it stands in complementing human efforts.

For instance, our customer tink, a frontrunner in European open banking, saw an impressive 39% reduction in AHT within just three months of deploying an AI agent assistant.

As a side note, after reducing AHT, we recommend you also evaluate how effectively AI tools free up human agents to tackle more complex issues and upskill to perform new tasks (like providing support in a new language). Improved agent utilization indicates increased efficiency and smarter use of resources.

#2 Response Time

If you’ve rolled out an AI chatbot or equipped your agents with an AI assistant, closely watch the response times to customer inquiries. Faster response times not only boost efficiency but also significantly enhance customer satisfaction.

For example, we’ve seen one of our customers, a leading pet tech company, experience a 30% reduction in response time by integrating an AI agent assistant for their agents.

#3 Ticket Volume Handled per Time Unit

Analyze how many customer tickets your team, regardless of size, manages daily, weekly, or monthly. This metric will help you understand if using AI tools, like an AI agent assistant, lets your team handle more without needing more people. 

It’s crucial for businesses, especially scale-ups, as it shows how well they can grow their customer service efforts without adding more staff, thanks to AI efficiency. 

Peak seasons are a particularly good time to monitor this metric, as a good implementation of an AI solution should allow you to navigate seasonal peaks without the need for new temporary hires.

#4 Resolved on Automation Rate (ROAR) 

Many companies are now using this innovative new metric to assess the effectiveness of AI in customer service. ROAR measures the percentage of customer inquiries resolved through automated systems, like AI chatbots or AI-driven tools, without human intervention.

It gives you insights into how well automation handles customer issues, reducing the workload on human agents and speeding up resolution times. By keeping an eye on ROAR, you’ll be able to monitor the success of your automation strategies and make informed decisions about further investments in AI technologies. 

So what can you expect? Take, for example, one of our customers, a European market leader in consumer tech subscriptions. They managed to automate 50% of their inbound conversations within just one week after launching their AI chatbot across all markets (US, Spain, Netherlands, and Germany), resulting in a ROAR increase to 50%. Additionally, they observed a 70% reduction in negative social media mentions!

#5 Customer Satisfaction (CSAT)

Gather customer feedback to evaluate the AI’s service quality. Positive experiences with AI will reflect in higher CSAT scores. Remember, the goal of AI is to speed up service without sacrificing quality. Regularly monitor your CSAT scores to ensure they meet expectations. 

We also recommend you compare scores from human and AI/automated interactions to gauge performance and strike the perfect balance between speedy service and happy customers.

Finding the sweet spot between efficiency and satisfaction

When looking at AI’s role in customer service, it’s vital to weigh both efficiency and customer satisfaction. Efficiency metrics, like how many queries AI can handle and its ability to manage several tasks at once, show the speed and capacity of our service. But that’s only half the story. You need to make sure that these services are not just fast but also meet customer expectations. This is where satisfaction metrics like CSAT, net promoter score (NPS), and customer effort score (CES) come into play. They give you a snapshot of how customers feel about the service, ensuring your push for efficiency doesn’t compromise the quality of their experience.

If you’re looking to enhance your customer service operations in 2024, an AI-powered knowledge base is the golden ticket you need.

An AI knowledge base is a game-changer for customer service efficiency, fundamentally transforming your support strategy and boosting customer retention. 

So how does an AI knowledge base work, and why is it a must-have for your customer service tech stack?

What is an internal AI-powered knowledge base?

An internal AI-powered knowledge base is a digital repository that uses AI to store, organize, and manage information. 

It gives your agents instant access to accurate information:

• Answers to common questions
• Product details
• FAQs
• Company policies
• Tips, tricks and troubleshooting

Unlike traditional static knowledge bases, an internal AI-powered knowledge base learns from interactions. It can anticipate your team’s needs with timely, relevant suggestions.

Why is an AI-powered knowledge base essential for your team?

An AI-powered knowledge base is a must have for any forward-thinking customer service strategy. With 80% of customers wanting faster responses from companies, it’s time to give them the quick answers they’re looking for.

Here are seven ways an AI knowledge base can improve your CS performance.

1. Improve team efficiency and reduce escalation

Relying on multiple apps and frantically searching for information is frustrating. The customer is waiting for an answer, while your agent is trying to speed-read everything. 

With an AI-driven knowledge base, your team gets access to a single source of truth, removing the risk of giving inaccurate or conflicting information. It gives them needed context and suggests relevant answers, enabling agents to respond swiftly to queries and significantly reducing average response times.

And for trickier customer questions, it also decreases the need to escalate to a colleague or manager! That’s excellent news considering 45% of customers feel a business is effective if it doesn’t transfer them to another colleague. 

2. Enhance agent experience

Agents want to do their job well – and that means giving customers timely, reliable answers. As an agent, there’s nothing worse than not finding the information you need, or having to sift through ten different documents to obtain it. That experience can be incredibly irritating and highly inefficient.

Traditional knowledge bases can be cumbersome. If you don’t word the query quite right, you’ll get hit and miss results. An AI-powered knowledge base takes semantics and context into account. Your agents will value receiving relevant answers that consider each customer’s history and concerns. They will also feel more empowered to resolve issues, knowing the information they provide to customers is verified and is coming from a trusted source.

3. Enable multilingual content for your team 

Offering multilingual content in your AI-powered knowledge base is a significant advantage, especially for agents proficient in multiple languages serving a diverse, global customer base. By enabling content in various languages at the organizational level, agents can access a wealth of information tailored to different linguistic needs.

This flexibility allows agents to effortlessly switch between languages, ensuring they can provide accurate and culturally relevant information to customers from different countries. It not only enhances the customer experience by providing service in their native language but also empowers agents by utilizing their language skills to the fullest, fostering a more inclusive and efficient customer support environment.

4. Create a seamless omnichannel experience

Have you ever reached out to customer service and got different answers to the same question, because you spoke to a different rep or used a different channel?

I think we’ve all experienced that at some point. An AI knowledge base saves your customers from that frustration. Because your agents are all drawing from the same robust source, they can give consistent answers between agents and across channels.  

Your customers get the information and help they need, no matter which channel they use. And if they’d rather solve issues using a self service portal first (88% of customers expect an online self-service portal), they can. 

5. Delight customers and increase CSAT and NPS

Tracking and analyzing customer satisfaction is vital if you want your company to keep thriving. Happy customers are loyal customers (and loyal customers tell their friends and colleagues why you’re worth spending money with.)

The quality of your knowledge base impacts your CSAT and Net Promoter scores. Inaccurate, slow, or irrelevant answers are sure sources of anger and diminishing trust. But fast, accurate, and personalized answers are a game changer. Your customers feel seen and valued, and their perception of your company improves.

6. Onboard and train new team members faster

An AI-powered knowledge base is a comprehensive learning resource for new hires. Use it to reduce training time and get your new agents ready to handle anything.

Because all the information they need is in one place, new agents know where to find answers, rapidly gaining confidence in their roles.

Your existing team doesn’t have to worry about accidentally missing out on information while training your new hires. If an agent leaves, their unique knowledge doesn’t leave with them – it’s all stored in the knowledge base.

7. Easily maintain and update your knowledge base

Keeping an old-school knowledge base up to date takes an immense amount of time and energy.

Updating an AI knowledge base is so much easier. It continuously learns from interactions. From articles that need a fresh approach, to new articles to be created, your knowledge base can suggest priority updates. It’s a dynamic tool that’s constantly growing and learning.

This adaptability is key in navigating the rapidly changing customer service landscape.

Get started now building a strong AI-powered knowledge base

If you haven’t yet implemented an AI-powered knowledge base, now is the ideal time to blend it into your customer service operations and increase productivity.

Your agents will be much more effective thanks to having instant access to accurate and updated information. 

Build a knowledge base that adapts and evolves with your customers needs, and you’ll create stand-out customer service experiences in an increasingly competitive market.

As we move into the new year, many companies are still feeling the pinch – and so are their customers. Budget-conscious buyers are picking and choosing carefully when it comes to where to spend their money, with half of customers saying they’re willing to switch to less expensive brands to help them save.

Smart brands know that makes now the ideal time to focus on customer retention. Build a base of loyal repeat customers and you can stay ahead of the competition with:

• Lower churn rates
• Better lifetime value
• Increased opportunities for cross-selling and offering upgrades
• Natural brand ambassadors who want to share their positive experiences with other potential customers

I know creating consistently exceptional experiences, especially at scale, can be daunting. Enter AI! Use AI in your customer service strategy to enhance customer service and harness the magic of AI for customer retention and loyalty.

Ready to get started? 

5 strategies to use AI for customer retention

1. Adopt an AI-chatbot

Your customers want quick and relevant answers to their questions. But how can you compete with giant multinationals that have a team of assistants on hand 24/7?

The answer is an AI-powered chatbot. A chatbot helps you offer 24/7 availability, without compromising on customer service quality.

Chatbots help you answer queries faster, by handling common questions, and equipping agents with relevant information for each customer. 

Using a chatbot frees up your agents’ time to focus on more complex queries. Agents get to ditch the routine questions and customers get focused and personalized attention – that means both parties are happier.

Customers can ask your chatbot for quick answers, or let it know they need a human agent. They can choose how they want to get help.

We know there are risks with AI hallucinations, with one study finding that 80% of respondents are actively weighing the risks that generative AI could have on their business. To ensure your AI-chatbot only gives accurate information, choose a training-free model that leverages the content from your own knowledge base to draft responses.

2. Empower your team with an AI assistant

AI is the assistant your team didn’t know they needed. Use AI in customer service to help your agents provide the honest and timely communication your customers crave.

Plug your AI into your knowledge base and it can draft instant and accurate responses to customer queries. It can also give your agents an overview of all customers’ past interactions so they enter the conversation with all the information at hand, and customers don’t need to explain themselves repeatedly.

AI can also help you:

• Automate routine tasks
• Streamline order processing
• Manage customer accounts 

Keep your team prepared for all queries with the help of AI.

3. Use AI to turbocharge personalization

Customers want to feel understood. Seventy-one percent of consumers expect companies to deliver personalized interactions. And seventy-six percent get frustrated when this doesn’t happen. 

Using personalization to tailor interactions is an effective way to show customers that you have their back.

AI can analyze customer sentiments to help agents better understand their feelings, and draft appropriate responses. We know some people worry that AI will make interactions less human, but AI can use data to create more human and empathetic answers.

Keeping personalized interactions consistent is a challenge. AI can help your agents create seamless, relevant, and consistent personal experiences across channels.

Whether you want to help your agents draft responses, provide better customer recommendations, or suggest knowledge base articles, AI can help. AI assists in tailoring interactions to each customer’s specific needs and preferences.

With AI in your back pocket you can deliver personalized experiences at scale, creating a welcoming atmosphere where customers feel seen and valued.

4. Harness AI’s data science capabilities

AI can clearly pull its weight in direct customer interactions and assisting your team. But AI can also be the data scientist you need.

Use AI to analyze customer service data and distill it into data-driven insights your CS team can use to find trends, and identify areas for improvement.

For example, AI eliminates the need for CS administrators to manually sift through extensive numbers of CSAT responses. With AI, this task can be accomplished with just one click, not only saving precious time but also encouraging CS managers to review feedback more frequently and efficiently. Additionally, AI’s ability to process CSAT data in various languages cuts costs and time for global companies. This enables your customer service team to more easily leverage CSAT for continuous improvements.

Similarly, looking at customer service data helps you understand the customer’s journey better. Customers might run into problems at different stages, such as during initial website exploration, while making a purchase decision, during the checkout process, or post-purchase when they might face issues with the product or require support. 

Your data can tell you where customers are in their journey when they need your assistance. Then you can work to reduce friction at these specific points, making the process smoother and more intuitive for customers, and increasing their loyalty.

5. Let AI be your customers’ multilingual guide

Customers want to interact with your brand in their preferred language. But if you have a global customer base, it can be difficult to communicate in all those different languages. If you can’t afford a team of multilingual global agents, it can feel next to impossible.

The good news is, you don’t need a multilingual team of agents. AI-powered translation tools can help you communicate across the globe. Communicating in customers’ preferred language can help with engagement and satisfaction.

You can also use AI to carry out sentiment analysis on logs in different languages, to find out how your global customers feel about your products, and customer service. 

How can you measure the impact of AI on customer retention?

There are lots of ways to use AI to boost customer retention. But you need to know how well it’s working so you don’t waste your time or investment.

Is your AI working for customer retention? Find out by by taking a look at these key metrics:

• Customer Retention Rate (CRR). Keep an eye on the percentage of customers retained over a specific period. Compare the CRR before and after implementing AI.
• Customer Satisfaction Score (CSAT). Ask customers for feedback on the quality of service received after AI integration. Compare historical data before AI to help you gauge whether AI is having a positive impact.
• Net Promoter Score (NPS). Check NPS trends to assess the likelihood of customers recommending you after receiving AI-enhanced customer service. Higher NPS scores indicate positive customer sentiments, and that means more potential for retaining those customers.
• Average Handling Time (AHT):  How long does it take to handle customer queries now that you’re using AI? Reduced AHT suggests that AI is speeding up the process, and helping to create a more efficient customer experience.
• Response Time: How fast can your team respond to queries with the help of AI? Faster response times contribute to improved customer satisfaction and can positively influence retention.

Monitor these metrics to get insights on the impact of AI on customer satisfaction, and use the data to refine your customer retention strategy.

Craft a winning AI strategy for your customer service 

Customer service AI is the ideal teammate, ready to help your CS agents deliver first-class customer experiences. AI’s analytic abilities and automation capabilities can help you provide a level of attention and personalized assistance that will win customers’s hearts and loyalty.

That’s good news for your reputation, and your wallet. In 2013, merchants lost on average $9 for every new customer acquired, but today merchants lose $29, a 222% rise in the last eight years.

Start your AI strategy with a thoughtful implementation plan that incorporates AI into your CS operation in a way that supports customers and agents alike. Be sure to start tracking its effectiveness early, so you can keep refining your strategy.

Carefully planned AI for customer service helps you go above and beyond for your customers, so that when they’re considering where to spend their money, you’re the clear choice.

Businesses nowadays measure everything. From marketing campaign ROI and recruitment statistics to customer satisfaction and website performance, there are few stones left unturned. One area that’s just as, if not more, important to measure is customer service team performance, and one look at the statistics will tell you why.

83% of execs say that bad CX puts their revenue at risk. Meanwhile, 73% of consumers say a good experience is key to influencing their brand loyalty. There is no shortage of surveys and research that has unearthed similar findings, and they all arrive at the same conclusion—customer service is critical to the health of a business. 

Evaluating and measuring the performance of your customer service team, then, is something that you need to stay on top of if you want to attract new customers and retain existing ones. But evaluating customer service performance is easier said than done; it can mean different things to different organizations, and this is where leaders can trip up and make mistakes. 

With this in mind, here are the top three mistakes that you should avoid when evaluating your customer service performance. 

Mistake #1: Confusing individual performance with team performance

Team performance metrics (for example, number of conversations solved) and individual agent performance metrics (for example, conversation escalations) are two very different things, yet it can be easy to conflate the two and arrive at incorrect conclusions and interpretations. It is, therefore, important to look at and evaluate the two in isolation. Customer service platforms like Dixa include reporting functionality that enables CS team leaders to track this information separately.

Evaluating individual customer service agent performance

When analyzing the performance of individual agents, you need to look at the actual, direct impact of an agent’s contribution. It’s no good looking at metrics that form part of a wider team effort or at the last assigned agent because this can cause confusion around who is responsible for a satisfactory resolution or a failed first contact resolution.

The best way to measure an agent’s direct contribution is through event metrics (e.g., replies, internal notes, successful resolutions). This helps you to determine exactly who is having a positive or negative impact on the team’s overall performance. 

It is also key to put those metrics into perspective by taking into account the workload and the specialization of your agent: an agent working on escalation will usually have lower productivity as the tickets treated are more complex or require additional steps. These agent performance metrics can help you gain a deeper understanding of an individual’s contribution. 

Customer service team performance metrics

When looking at team performance metrics, focus on customer experience rather than the individual outcome. For example, look at things like the number of conversations per channel, contact reason, customer priority, and any other metrics or aggregates that link back to your CX strategy. 

We recently came across this exact situation while onboarding a new customer in the digital services industry. They were trying to figure out how many conversations were being closed per agent per day. At first, they were looking at how many conversations were being closed per day and which agents were assigned to those. 

Prior to Dixa, the problem with this approach was that one customer issue could be worked on by several agents, and only the person who closed the conversation would be counted, even if that agent wasn’t the person who provided the resolution. With Dixa, they can now track which agents were involved in a resolution through side conversations and escalations. 

Mistake #2: Being misled by interpretation biases

One of the most common metrics that organizations look at when evaluating customer service performance is customer satisfaction or CSAT. 

This sounds obvious, right? After all, customer satisfaction is sure to be a pretty good indicator of whether your customer service team is doing a good job because, by definition, customer satisfaction is the measure of how products and services supplied by a company meet or surpass customer expectations. 

The right answer is that it can be a good indicator if you look at the data properly. The issue with relying solely on customer satisfaction (CSAT) data is that it is a metric that is very rarely collected. According to statistics, the average return rate of a CSAT survey is between 13-15%, so looking at this data in isolation would make for a rather grim reading. 

Instead, look at your CSAT data in conjunction with the number of ratings that have been returned to ensure that the data you’re looking at, and the inferences you are drawing from it, is a true reflection of the actual situation. One way to achieve this in Dixa is to define a minimum number of values that are required to compute a metric, which ensures that you aren’t basing your inferences on small, misleading datasets. 

Mistake #3: Not understanding the context of key metrics

Two more metrics that are often used to evaluate customer service performance are first contact resolution and reopening rate:

• First contact resolution is the number of queries resolved during the first contact with an agent.
• Reopening rate is the number of queries that have been reopened after an initial resolution. 

The trouble with metrics like reopening rate is that a conversation can be reopened for a variety of reasons, and this doesn’t necessarily mean that the answer provided didn’t solve the customer’s problem. For example, a customer might reopen a conversation by sending a thank you email, by coming back to the same conversation with an entirely new query, or because an agent accidentally closed a conversation before it was resolved.

As a result, it’s important for customer service leaders to understand the context of their key metrics instead of looking at them blindly. While a high reopening rate might look bad on the face of it, the true reopening rate—for example,  the number of tickets reopened because the solution wasn’t adequate—is likely to be much lower. 

You can conduct additional research with QA tools to investigate the reopening causes, and statistically draw conclusions about your real reopening rate.

KPIs need context when evaluating customer service performance

Your customer service key performance indicators (KPIs) are very important, but they are only useful when properly understood and interpreted (using the right scope of data). 

To avoid making mistakes in assessing customer service team performance, customer service leaders need to ensure that they are looking in the right place and at the complete picture, including all available context, and that they properly understand what everything means. Quality assurance is an important part of tracking and improving customer service performance. Read The Fundamentals of Quality Assurance to get started.

Dixa is different. Our customers are different. And, of course, our approach to technology is different. That’s why, when we launched our first bleeding-edge AI features earlier this year, our approach and focus were different from the rest of the industry. 

While other industry leaders focused on software to deflect (one of my least favorite words, ever) customer service queries, Dixa started with a more multifaceted approach. An approach that started with the humans at the center of customer experience. This led us to build a suite of tools that improve experiences for customers, agents, and service leaders. These features do more than improve efficiency, they make life a little easier, a little better, and a little more delightful for humans. 

That’s why today, I’m particularly proud to share more about our vision and philosophy for AI — something that we call our Human + Artificial Intelligence (HAI) approach. 

The choices we make with AI

There’s a lot of doom and gloom predictions about AI. I’ve sat in rooms with CTOs who have detailed their plans for drastic workforce reductions. I’ve talked with CEOs about “when they can fully automate the customer service function.” And, like everyone else, I’ve heard the talking heads on TV threaten mass unemployment and major societal disruption. 

Here’s the thing: that future might be possible. But it’s not inevitable. I don’t even think it’s probable. Humans are still better at solving complex problems. Humans understand fine distinctions. Humans are up to date on current events and emerging trends. And most importantly, humans crave connections with other humans. I’d still rather talk (or chat) with a person in a critical customer support situation. There are times when we want to know that someone else understands our problem and wants to help us fix it. 

And it’s not just that humans are better than AI at some things. We have a choice about how we leverage new technologies and the role they take in society. I think this tweet sums up my stance pretty well:

At Dixa, friendship is our DNA. We’re a company founded by customer service professionals for customer service practitioners. We build tools to make customer service as easy and natural as talking to a friend — because we believe that connections between humans are important. 

Our customers agree. Far and away, the biggest common thread between our customers is that they love their customers. Whether they’re selling pet supplies, meal delivery services, insurance, great sporting gear, or designing bespoke vacations they know that connecting with their customers is critical to their brand. That’s why humans are at the heart of how Dixa looks at AI. 

Tools should make humans better at work

Look, I’m a product and engineering guy — so I’m also trying to understand how AI will affect my job, and the people that I work with everyday. Maybe it’s just me navel-gazing, but one of the most compelling use cases I’ve seen for AI is its application in software development. 

And because software people are generally obsessed with data, we are already seeing some early studies on how AI is affecting developers at work. In a study from GitHub focused on their Copilot software, a program that helps developers write code, they found that developers that used the AI tool were able to complete a benchmark task 55% faster than those that wrote code alone. 

Productivity is important, but as someone who is responsible for the health of my department, not just how much work gets done, the speed gains from AI were definitely not the most exciting finding to me. In addition to becoming more productive and feeling more productive, 60-75% of developers that used Copilot said that they felt more fulfilled by their jobs, were more focused, and could spend more time on satisfying work. 87% said that they spent less mental energy on repetitive tasks. Faster, less mentally exhausting, more fulfilling work — that’s the type of impact that we should be looking for from AI. 

I absolutely want my teams to feel more fulfilled and engaged — and ultimately, I think this is where we’ll see the biggest gains from AI. I believe so wholeheartedly in this that I’ve centered our product strategy on making sure that the humans who use our products benefit from AI and aren’t just marked for replacement by it. Developers aren’t fundamentally different from customer service professionals and they both want tools that help them solve tough problems efficiently. I believe that Dixa will deliver the productivity and quality of work improvements that CS folks need and deserve. 

Designing AI interventions for complex systems

One challenge I’ve always loved about building products at Dixa is that we have a complex system that serves a bunch of different people. We have customers that reach out to business with support questions who want fast, accurate resolutions to their problems. We also have service agents that need tools that help them understand who that customer is, why they’re reaching out, and how to solve their problems. We have service leaders who want to make sure that everything is running smoothly, that quality remains high, and that the cost to serve customers stays low. And finally we have a host of other folks that support the journey, whether they’re developers who maintain the system, QA folks that help with quality and training, content teams who provide knowledge and answers, team leads who manage and develop agents, or data gurus who leverage CS data to support better business decisions. 

That’s another reason that we’ve taken a multi-track approach to how we leverage AI at Dixa. Each of those groups needs a unique set of tools to help them accomplish what they want to do. That’s why we’re bringing multiple AI products to market:

• For customers: Our new natural language chatbot, Mim, ensures that customers get accurate answers to their questions 24/7, in any language. Mim runs on top of your knowledge base, so there’s no set-up, answers are accurate, and even the tone is on brand. Coming later this fall, Mim will also be able to automate a variety of functions like changing order dates, canceling subscriptions, and performing other actions in backend systems. 

• For service agents: Our agent assistant provides a number of useful features to help you across the service journey. From providing conversation summaries, to translation, to writing and grammar improvement on the fly it gives you the power of GPT, right at your fingertips.

• For service leaders: Mim can dramatically reduce service volumes and shorten wait and handling times. Plus, intelligent handovers get agents up to speed quickly when the bot can’t solve the problem on its own. Our agent assistant can improve both productivity and job satisfaction, reducing training time and turnover. Finally, we’re working on new AI tools that allow you to gain more insight into what’s going on in your service organization so that CS becomes a business intelligence center.

I’m really excited about what we’re bringing to our customers today, and, of course, all that we have planned for the future. 

Seize the moment

Microsoft’s CEO Satya Nadella says, “This moment is like when PCs first showed up at work. The beauty of our industry at some level is that it’s not about who has capability, it’s about who can actually exercise that capability and translate it into tangible products.” The tech is here to stay, it’s all about understanding how to harness it and bend it to your greatest advantage. Just like PCs became the go-to tool for knowledge workers, AI will become an essential part of our work routine. But to do that, AI needs to work for workers – not the other way around. 

At Dixa, we believe that we’re giving you the tools you need to make customer service one of your brand’s biggest differentiators. This means leveraging technology to make sure your human resources have what they need to do something that only they can — delight your customers. If you’re interested in how you can leverage AI to make your team better, happier, and yes, more productive, we can help.

It becomes more clear with each passing year. 

Delivering an optimized and rewarding customer experience is critical for almost any brand’s success. In fact, McKinsey & Company recently reported results of a customer care survey that confirmed customer service is now a strategic focus for companies.

But, take a closer look and you might find that what an organization prioritizes may differ from what customers care about most. 

For instance, we know that tracking the time it takes to resolve a customer issue is important to most companies. Meanwhile, the quality that the service customers get is extremely important to them. It sounds like common sense, but this just confirms that your customers care more about whether their issue is resolved than the time it takes to resolve it.

Deloitte explored a question that sums up this dilemma in a recent survey of their own. They asked if we are focusing on the right customer satisfaction performance areas.

Based on responses to their survey, the areas cited as frequently used to measure agent and team performance included:

• 73% Quality of service/contact/fulfillment

• 51% Time taken to resolve an issue

• 44% The number of issues solved in a given period

• 40% First contact resolution (FCR)

These kinds of call center agent performance metrics are crucial for measuring the effectiveness of your agents and the team as a whole. What you measure matters; by analyzing these indicators, organizations can identify areas for improvement and develop strategies to enhance the customer experience.

The metrics most critical for you to track may depend on your strategy or the industry you compete within. But focusing on the set of metrics most aligned to your business objectives  can also result in the customer agent behavior you would most like to see. 

Let’s take a closer look at 10 critical agent performance metrics you should know about. 

First response time (FRT)

First response time refers to the time it takes for a customer to receive the first response from a customer service agent after submitting their query or request. It’s typically measured in minutes or hours and serves as an indicator of how quickly and efficiently customer inquiries are addressed. The shorter the response time, the better. Longer first response times can lead to frustration and dissatisfaction among your customers, so organizations should aim to keep response times as low as possible.

First contact resolution rate (FCR)

A first contact resolution rate measures the percentage of customer inquiries or issues that are resolved during the initial contact with a customer service agent, without requiring any further follow-up or escalations. A higher number here indicates better performance.

In fact, a high FCR rate generally indicates that an agent is knowledgeable and able to resolve customer issues. And that should lead to higher rates of customer satisfaction.

It’s important to emphasize that CS agents won’t always be able to resolve all inquiries on the first try. However, in many cases, making a “next best action” available to suggest can help to improve FCR rates. When an agent offers customers a clear action plan that outlines steps the customers can take to resolve their issues quickly, it’s usually appreciated. By proactively providing customers with the next best action, agents can help ensure that follow-up interactions are as efficient and effective as possible.

Rate of answered calls

The rate of answered calls refers to the proportion of incoming calls that are answered by agents, divided by the total number of incoming calls, expressed as a percentage. The metric indicates how effectively an agent is managing the volume of incoming calls and ensuring that customers are not left waiting for extended periods. 

If you believe your agents need to improve their rate of answered calls, there are a number of steps you can take. Call routing technologies can distribute incoming calls evenly among available agents so some agents don’t get overwhelmed with calls, while others have fewer. You can also evaluate your call scripts and processes to identify areas where they can be improved or streamlined. 

Average handling time (AHT)

Average handling time is a metric used to measure the average time it takes for an agent to handle a customer interaction — from first contact until its completion. A lower average handle time generally indicates that a customer service agent is able to handle interactions more quickly and efficiently, but the whether or not the customer’s issue was resolved is also key to consider here.

If you’d like agents to improve their average handle time, there are many actions you can suggest. For instance, your agents should have a comprehensive understanding of the products and/or services they support. This will enable them to address customer inquiries more swiftly and accurately, reducing the need for prolonged research. 

Agents can also improve their performance here with the assistance of knowledge bases. Having a comprehensive knowledge base or internal FAQ resource readily accessible can significantly reduce research time and improve AHT.

After-interaction work (AIW)

Simply put, after-interaction work measures the time and effort an agent spends on tasks related to a customer interaction after the conversation or interaction has ended. Perhaps one of the best ways to improve on this metric is to practice taking comprehensive, accurate notes during all interactions. When your agents can document relevant information, updates, and actions taken during the customer interaction itself, it reduces the need for extensive post-interaction documentation and ensures accurate and timely record-keeping.

Overall resolution rate

Overall resolution rate is a key performance indicator used to measure how effective customer service agents are in resolving customer issues. If an agent needs help improving their overall resolution rate, a more active listening approach can help. Paying attention to details and asking clarifying questions can help ensure a more comprehensive understanding. 

Agent adherence to schedule

This metric measures how well customer service agents adhere to their scheduled working hours or shifts. Evaluating this KPI can provide insights into an agent’s punctuality, availability, and compliance with their assigned work schedule.

Quality assurance score (QA)

Another critical metric for measuring agent performance is quality assurance scores. QA scores measure how well agents adhere to company guidelines during customer interactions. Factors that can be tracked might include tone of voice, level of politeness, and product knowledge. QA scores are often determined by a team of quality assurance analysts who evaluate recorded calls or chats to ensure that agents follow the correct procedures and provide high-quality service.

Organizations can use QA scores to identify areas where agents may require additional training or support. By regularly monitoring these scores, businesses can ensure that their agents provide consistent service that aligns with company values and standards.

It’s important to note that while high QA scores are desirable, they should not be the sole focus of an organization’s efforts. Organizations must balance the need for high-quality service with the need for operational efficiency. By using a combination of metrics, including response time, FCR rates, CSAT, AHT, QA scores, and agent adherence to schedule, organizations can develop a comprehensive understanding of their customer service team’s performance and make data-driven decisions to improve it.

Agent churn

Agent churn rate measures the rate at which customer service agents leave or discontinue their employment within a given period. This metric can provide insights into the turnover or attrition of customer service agents within an organization. A higher agent churn rate indicates a higher turnover of agents, which is usually not ideal.

Customer satisfaction score (CSAT)

Customer satisfaction is another critical metric for measuring agent performance. CSAT measures how satisfied customers are with the service they received. This metric is often measured through customer surveys or feedback forms. A high CSAT rate indicates that customers are happy with the service they receive, while a low CSAT rate indicates that improvements are needed.

Summary & takeaways

No doubt, agent performance metrics are crucial for measuring the effectiveness of a customer service team. But many of these are also useful for agents themselves to be aware of. CS agents need to be able to see the value they bring to the organization, so they feel they make a difference.

By analyzing these metrics, organizations can identify areas for improvement and develop strategies to enhance the customer experience. 

Some of the most important metrics include response time, first contact resolution, overall resolution rate, and customer satisfaction. Organizations should aim to improve metrics like these to ensure that their customers are satisfied and loyal.

You can read about our recommendations for what you should measure in your contact in our previous articles Rethinking Contact Center KPIs – Part 1 – and Rethinking Contact Center KPIs – Part 2.

Why customer service quality assurance matters 

Customer service teams are under mounting pressure as customer expectations continue to rise and economic headwinds make for a more cautious consumer. In this hyper-competitive environment, consumers want their problems resolved quickly and efficiently, with minimal effort.

But, too often, customers experience poor quality service.

In the so-called “Switching Economy,” this poor customer service has two critical consequences for companies:

First, it leads to high customer churn, which is up to 25 times more expensive than the cost of retaining a customer.

Second, businesses miss out on opportunities to improve their reputation and, as a result, their sales. Customer support agents are, increasingly, customers’ first and only point of contact. As such, customer service has become a critical commercial lever for companies.

Quality assurance (QA) is the process of taking a sample of interactions (conversations) and reviewing them to check the overall quality of the service delivered. You can check for many different things, but the ultimate goal of quality assurance is to improve people, processes, and the customer experience.

How does customer service QA benefit my company?

By implementing quality assurance at your company, you ensure that your team is providing consistent and excellent customer care, while running efficiently.

In fact, it’s an integral part of building and running a modern customer support organization. But why?

1. Dissatisfied customers are expensive: In fact, businesses lose more than

$75 billion a year thanks to poor customer service.

2. Every message counts: Customer support is no longer just about solving customer issues. The support that is given to customers prior to purchase is equally as important as solving a post-purchase problem. 

3. The voice of the business: In the majority of digital-first businesses, customer support is the only place where customers can interact with your brand in a more personal way. Two customers that ask the same question shouldn’t get two different answers – your brand must have the same, unwavering, voice whenever a customer contacts you.

4. Treat your people well: Many agents are thirsty for feedback and want to know how to improve. This is especially true for team members who are earlier on in their careers – they’re keen to know how they are performing so that they can grow.

So, by enabling QA in your service organization, you can: 

1. Reduce customer churn: by improving the effectiveness of each interaction, you make customers happier – and happy customers stay. Remember, it’s 5-25x more expensive to sign a new customer than to keep an existing one!

2. Improve customer acquisition: the modern customer holds word of mouth in high regard. 92% of consumers trust recommendations from friends and family above all other forms of advertising.

3. Unify the voice of your brand: When you unify your communications, you strengthen your brand. Your customers will also appreciate that, no matter how or when they contact you, they get consistent answers.

4. Improve agent satisfaction: Happier teams mean more engagement at work, and reduced turnover in a line of business where this is commonplace.

How does customer service QA benefit my customer service department?

Before we jump in, let’s take a quick look at the PDCA framework:

Plan 

Prepare for a new product launch or an expected peak in seasonality by implementing a new process, creating new documentation, etc.

Do

Deal with the added contact volume with what you’ve put in place.

Check 

How did you perform? What worked / didn’t work? Where could you have improved?

Act

Use that information to update processes and documentation and give better feedback to your agents.

Quality assurance, by its very nature, is the “Check” part of the process – that’s obvious! However, when done properly, QA fuels the “Act” part of the process, too.

The goal should not just be to check conversations, but to use QA as an opportunity to learn something and to gain a concrete insight into an aspect of your customer service operations. Here are the two most common ways in which QA is used to improve a support team:

1. Help your people grow (introducing the famous Feedback Loop)

You are most likely familiar with this already, but let’s do a quick refresher: 

A Feedback Loop ensures agents get regular feedback about their work. Half of it is checking to see if your agents are doing a good job; the other half is helping them to become even better by showing them where they can improve. This involves reviewing conversations, leaving comments, checking QA score data, and hosting 1:1s.

It’s important for:

Employee satisfaction: By giving your team valuable, skill-building feedback they will feel more seen and more satisfied with their role.

Employee engagement: A QA process gives agents the opportunity to speak about their work in the form of 1:1s. Giving them constructive feedback and opportunities to be more involved in the process (for example, using peer-to-peer reviews), gives them the chance to grow.

Employee retention: When agents are more satisfied and more engaged with their work, then they are less likely to leave. This has huge benefits for company culture as well as the balance sheet (saving budget on hiring, training, and transferring knowledge).

Whilst building a Feedback Loop is the most common use of a QA solution, the most efficient teams use it to put in place a Comprehension Loop. More on this in the next section!

2. Understanding and improving (the Comprehension Loop)

Quality assurance can also be used to reveal insights that won’t be immediately apparent when looking at your metrics. Imagine, you identify a problem when checking your KPls, but you can’t quite see where the issue is stemming from.

The Comprehension Loop is about conducting reviews to understand the effectiveness of your support team in any area you desire. Conducting targeted reviews will give you a unique insight into processes, documentation, training, and more.

For example, it’s great for establishing internal and customer-facing best practices. You may have the documentation to support your agents in their work, but how do you make sure that it’s being used or if it’s helping? How do you know that your training is working? How do you know if you have all the necessary processes and tools in place to address customer requests following a new product launch?

Feedback & Comprehension Loops

QA helps you to:

• Improve individual performance: Provide helpful feedback that improves the work of your agents and helps them to grow professionally.

• Improve team & organization performance: Find wider issues by looking at your data holistically.

• Monitor operations and catch problems: Run checks on existing processes to catch a problem at its source before it does any damage.

• Understand and improve: Solve and identify specific problems by identifying the “why” when the data presents no immediate solution to the problem.

It’s important to know when to put these two loops into action, and whether they are continuous or one-offs. This table breaks it down:

Feedback Loop

Individual performance

This is the part of QA that you are probably most familiar with. Essentially, doing spot checks of your support agents and their conversations with customers to assess a number of different things. This could be to see if they are using the right tone, correct grammar – or whether they are doing everything they can to give an effective answer to a customer’s query, the first time round.

It’s not about assigning blame!

QA shouldn’t be viewed by agents as a way of pointing out their flaws – managers should avoid this thinking, too. QA is simply a way of ensuring that the universal quality of interactions with customers is high, and it is done randomly – so it’s inherently unbiased.

An efficient process should bring people together, unify them, and make the team stronger. It will point out common flaws, show you where knowledge gaps appear, and where documentation is lacking. When reviews are being discussed with agents, it should be from the perspective of coaching them and trying to develop their skills, not micro-managing them.

Improving feedback and coaching by deep diving into comments left by reviewers

We believe that this part of QA is more about the feedback and coaching that you give agents than the grading of the conversations themselves.

It’s really important that you take the opportunity to tell agents how they can improve and not just where.

Examples of good and bad review comments:

✔️ Make sure you link the customer to this knowledge base article when

dealing with this shipping issue.

❌ You won’t stop the customer from contacting us again if you don’t give them all the information they need.

1:1s

The majority of our customers do bi-monthly reviews for their agents. One longer meeting and one shorter check-in meeting.

1 hour: This is the time to go through an agent’s reviews, discuss their KPls, and see how they are trending for that month. It’s also the time to give them as much constructive feedback as possible. This session can be supported with your QA tool or Agent Development Plan, where you highlight specific things that you want them to improve on, as well as check and see if previous targets/objectives were achieved. Remember, it’s a two-way street and they should come prepared to ask any questions that they may have, too.

15 mins: This shorter session is just a check-in on how they are doing and to get updates on any projects they may be working on within the company. They can raise any quick questions with you here.

Adapt the process to your team

Not everyone needs the same level of attention or the same number of reviews. Personalize your process to each team member to save time. 

Customer service team & organization performance

All the reviews that you’re conducting are helping you to frame a picture of how your team, as a whole, is performing.

Providing effective feedback to agents and giving them the chance to develop their skills is important on an individual level, but looking holistically at all the reviews you’ve conducted is also very important.

Using the example above, you can see that this agent, let’s call her Alice, has no problem with “Tone of voice.” If we were a reviewer, we would know that our team also has no problem with it, and we could assume that the “Tone of voice” across the company is also not a problem. No harm done there, but it’s never good to assume, as we’ll see in the next example!

When we consider “Politeness,” things change. After reviewing our whole team, we could (wrongly) assume that the whole organization is bad on this front, when really it’s our team’s issue. Alice has got it right, like the rest of the organization – but we should set our team the goal of tackling this issue – fast! In our 1:1s, we should ask agents about what it is they are struggling with in terms of politeness.

Most importantly, without a proper assessment of “Product knowledge” across “Agent,” “Team,” and “Company,” we could come to the wrong conclusions. We might assume that Alice is at fault because she is not performing like the rest of our team. This could be true, but after the analysis we see that the company as a whole has a problem with product knowledge. It’s now down to us, the team that’s performing above the rest, to share our knowledge and ways of tackling issues. If we didn’t do this, we might not be able to help the rest of the company as quickly – and Alice might still feel bad about having below-average product knowledge.

Plan

Alice isn’t showing the same level of product knowledge as the rest of the team for requests related to a new line of products. I need to find out whether this is because of poor adoption, or if there is a problem with the documentation itself (unclear, confusing information).

Do

We carry out the reviews to see why Alice is struggling, but also to see if she is the only one. I review customer queries related to these new products in my team and samples from other teams.

Check

It seems that Alice isn’t the only one. The First Contact Resolution (FCR) rate for the rest of the organization is lower than it is for the rest of the agents on my team.

Act

We check the documentation with the responsible parties, explaining that some of it is confusing/contains incorrect information, and we update all agents to improve their knowledge. We continue to check related queries.

If numbers improve and we are satisfied that we have solved the issue, we can consider the problem closed. If not, we repeat the process and keep looking.

Comprehension Loop

Monitor operations & prevent problems

The first part of the Comprehension Loop forms what we would consider the standard process by which you check your quality across the board. These QA checks are put in place to monitor activity related to new things (products, processes, or other changes), and even if there’s no signs of any trouble, it’s the pure control/safety loop.

The goal is to detect issues early (before they become visible through metrics) so that they can be addressed before they become a bigger problem.

Identify & solve specific problems

As we’ve said before, QA will help you identify why something is happening when it’s not immediately obvious just from looking at the data. In other words, QA is incredibly useful for ultra-specific deep-dives. This could be to investigate the effectiveness of any of the following:

• A new process
• New documentation
• Adoption (new process adherence, documentation use, etc.)
• Training
• Knowledge of new products/services

But it’s also a chance to capture new insights and detect emerging trends.

This is where the PDCA framework can be applied for maximum effectiveness. Let’s take the above “Product knowledge” example again, but let’s imagine it in the context of a new product launch. Let’s say you’re an eCommerce company that is launching a new line of shoes.

Plan

You’ll plan ahead with answers for questions that could arise about these new shoes. For example, questions about where and how they were produced, sizing, colors, potential discounts, delivery, etc. You’ll update your internal and external knowledge bases, create templates, and train your agents so they know where to find all the information they need to solve customer queries. 

Do 

You deal with the new requests that come in as a result of these new shoes. It seems that a lot of the new requests coming in are not being solved the first time around, which is evident when you conduct your reviews. The data confirms this: you notice a suspiciously high re-engage rate for conversations under the “new shoes” category. You can see that your agents are doing as instructed and sending customers to various knowledge base articles that have been set up to help. However, some of them simply aren’t doing the trick.

Check

Now you need to see what the problem is. Are your agents just linking to the wrong articles? Or is the content itself an issue – are customers reaching out again when they are sent a specific link?

Here’s where you assign reviews to your reviewers to get to the root of the problem. You automatically assign them customer conversations with the following criteria to investigate: Channel: Email or Live Chat; Reason: New shoes; Conversation re-opened: Yes.

Act

Let’s say that there are three articles that contain the incorrect information. Through the review process, you have identified this. The action you take might be to update all three of the articles and also to investigate why they were wrong (in this case, it could involve some discussions with the manufacturing team).

Once you’ve corrected this, the process repeats itself:

Plan: Inform agents of the change and show them where to find the new materials.

Do: Agents use the updated documentation.

Check: Carry out reviews and see if the issue has been resolved and, if not,Act: Take the necessary action(s).

Want to learn more about customer service quality assurance?

As you can see, implementing quality assurance into your customer service organization will help you stay ahead of potential problems and solve issues faster. You’ll see improvements in your efficiency as well as get access to meaningful metrics that help demonstrate your team’s contribution to company goals. 

Now that we’ve given you the fundamentals, we hope you’re ready to get started on your QA journey. Book a demo with us today to learn about Dixa’s QA solution. 

In the customer service industry, we like talking about channels. A lot. Which is strange, in a way, because customers themselves don’t think in terms of channels at all – they decide how to get in touch simply based on context. So, if they’re already on your website, they’ll start a chat. If it’s an urgent matter, they’ll make a phone call. If they’re lounging on the couch, they’ll use social media. 

With that in mind, there’s no doubt that finding the right customer service channels (and channel mix) for your business is key to creating a great customer experience. And that starts by putting yourself in their shoes. 

While it’s admirable to have all channels enabled for all customers, all the time, it’s not the most efficient or effective approach. Building your customer support offering on a channel-by-channel basis will only lead to inconsistency and negative experiences. For the best results, you need to identify which customer service channels are suited to your customer base and feed this information into your strategy. 

Spoilt for choice

Let’s look at the most common customer support channels that you should be exploring, and what to consider when figuring out which ones will be most effective for you. Keep in mind that this is by no means an instruction to adopt them all. By considering what’s available and taking time to review your customers’ behavior, you can understand where best to focus your support resources.

Phone

Phone accounted for 31% of conversations across all Dixa customers in 2022, and we find it to be the best channel for first contact resolution. It’s a personal approach that offers immediacy as well as an opportunity for follow-up questions. In some cases, moving chat or email conversations to phone results in a faster resolution… 

You read that right – while digital channels continue to increase in popularity, traditional methods of customer communication still very much have their place. The detail lies in the demographic. According to Salesforce, even though Gen Z are 1.6 times more likely than Baby Boomers to engage through digital channels, 43% of customers overall prefer a non-digital route.

Chat

Live chat combines the control of email with the speed and immediacy of phone support. The benefits are such that 63% of customers said they’re more likely to return to a website with live chat enabled. Best of all, customer support representatives can handle multiple live chats simultaneously, which improves productivity. 

However, just like customers don’t want to wait on hold during a phone call, they also don’t want to wait for a chat to begin or have a lag time between responses. Hubspot reports that 60% of customers expect an immediate response when contacting live chat. 

Want to take your live chat game to the next level? Add a chatbot to the mix. Optimized self-service options like intelligent chatbots can help people help themselves and reduce customer attrition.

Email

Email is the most popular channel amongst Dixa customers – it accounted for a whopping 61% of our customer conversations in 2022. While it meets a wide range of support needs, it can be limiting for conversations that need back-and-forth dialogue, as both sides will be waiting for updates. That being said, we’ve found that customers prefer to wait a little longer for a response that resolves their issues completely, as opposed to getting a quick reply but having to reach out multiple times to resolve the issue fully. 

Which leads me to the next email limitation… trying to scale customer support with a single shared email account will quickly cause frustration for both customers and staff. The McKinsey Global Institute estimates that the average worker spends 28% of the work week managing email and nearly 20% looking for internal information or tracking down colleagues who can help with specifics.

The solution? Conversational customer service software with a built-in knowledge base. Instead of long, confusing email threads, teams can track complete support history across all contact channels, all in one place. This allows customer service representatives to work more quickly and save time resolving customer issues.

WhatsApp, Facebook Messenger, & Instagram

Most consumers use at least one form of social media on a regular basis. And with the growing role that social media plays in our everyday lives, it’s become natural for customers to contact companies through these channels. 

In fact, a study from Statista shows that 47% of respondents have a more favorable view of brands that provide customer support responses over social media. However, it’s important to keep in mind that due to the immediate nature of social media, when customers do reach out, they expect a speedy reply…

SMS

Text messages are seeing rapid growth, especially as more customers recognize the channel as a hybrid between phone and chat. It offers an asynchronous conversation via a synchronous media like a phone. But again, just like you would expect an immediate SMS response from your friends and family, customers will expect the same from a business they contact in this manner.

5 steps to designing the right customer service channel strategy

Before you start adding multiple new channels to your customer service setup, it’s essential to consider the impact on your customers. Ultimately, their needs should dictate the support avenues you offer. 

You only need a few carefully selected channels to succeed, and which ones you add depends on your channel strategy. Here’s what you need to consider:

1. Be available

Make sure every channel experience you provide is excellent – even if it means offering fewer channels. Once you know what you can offer and what your customers want you to offer, it’s time to understand the industry expectations. One of the many reasons customers churn is because companies don’t offer the experience they want, need, or have found elsewhere…

2. Help customers work smarter – not harder

Customers frequently choose the wrong channel to resolve their query, which leads to channel switching, extra effort, and frustration. Rather than enabling customers to use any channel, you must guide them to the channels where they can resolve their issues with the least effort. 

While there are always exceptions to the rule, customers from different generations show typical channel preferences. If your customer base is dominated by one generation over another, their preferences can help you decide where to put resources and boost customer retention. As a general rule:

• Baby boomers prefer phone and email.
• Gen X can move between email, text, and chat.
• Millennials prefer social media.
• Gen Z lean towards social media.

3. Do the math

It sounds obvious, but you must make sure you have the right number of customer service representatives working at the right times. And keep in mind that not every customer service agent will be suited to every channel. Some people do well with fast-paced chat, whereas others excel via email. Pay attention to how your employees perform, and staff channels with the best-equipped people. 

4. Work out the “why?”

Collect all the customer service data you can, and build an insight-packed picture of your customer base. Correlate the contact reasons with CSAT, and keep an eye on first contact resolution, time to first response, total time to resolution, and average handling time. If, for instance, most of your support requests are technical, and you find complex technical queries are often better handled via email, it may not be as essential to open up a channel like SMS.

5. Find room for improvement

Audit your customer support offering and review performance across each channel. The best way to do this is to monitor how your customer satisfaction score has progressed over time. If you notice that one of your channels is suffering, focus on improving that channel before you add any more. 

Meet customers where they are

Finding the right balance of customer service channels will help you meet customer expectations while increasing your efficiency. But remember that the right channel mix will depend on your customers, your product, and even the kind of support you offer. It’s an equation and approach that should be unique to your business.

Are you excelling in all of your current channels? If not, adding a new one probably isn’t the right move. Be available on the channels customers need, but remember to guide them to the channels where they can resolve their issues with as little effort as possible.

By offering customer service on the channels where your customers are most active, you can make their lives easier, nurture customer loyalty, and help grow your business. Win-win-win!

This year, it’s safe to say that AI has gone from alluring buzzword to outright game-changer. 

ChatGPT has led the charge and is currently disrupting entire industries and ways of working. We’re just beginning to understand its time-saving and productivity-boosting applications, advancements that were once only available to those with a certain level of technical know-how but are now open to the general public.

Is ChatGPT here to steal our jobs?

Despite all the positives, there is still some trepidation, especially when it comes to ChatGPT potentially replacing customer service jobs – we even held our most popular webinar ever on this (check out the on-demand version here).

Though this hesitancy is totally understandable, the reality is, there’s no need to worry. Human agents are more necessary than ever, with AI applications simply automating some of the most tedious aspects of an agent’s day-to-day and giving them more time to focus on important tasks that require a human touch. This has the potential to improve both employees’ lives, with better, more interesting tasks and your company’s bottom line.

An ideal customer service team consists of employees whose skills complement each other. AI chatbots are not exempt from this. When you introduce AI into your team, it should complement the skills your employees already possesses. So, let’s get into why your service team is about to fall in love with customer service chatbots. 

3 reasons your customer service team will love chatbots 

1. Better & more meaningful work

Data entry is for machines, not for people! Chatbots are great for this very reason: they can take over repetitive, manual tasks on a scale that saves your team a ton of time every week.

Customer service teams enjoy an unfortunate tradition of low levels of engagement at work which goes hand-in-hand with high employee turnover. This is expensive and bad for the customer experience. 

But providing opportunities for meaningful work can directly counteract this. According to a Gallup report, “the workforce of today is motivated by opportunities to develop and possess a sense of purpose with regard to their work.” Unfortunately, customer service representatives spend a substantial number of hours on manual, repetitive work that doesn’t contribute to a sense of purpose.

Introducing chatbots to your service channel mix doesn’t mean replacing employees with technology, but rather applying tools to make their work more engaging, less repetitive, and fundamentally more valuable. The tools are already here; you can use AI to get agents up-to-speed on a customer issue, to draft responses for agents, to respond to a customer’s query via self-serve, and more.

Algorithms can be far more powerful than humans in big-data analysis or pattern recognition, and chatbots are much more adept at quickly and accurately collecting contact tracking information. When applied to customer service, they can help identify underlying issues in the customer journey, and improve the customer experience as a result. 

2. Improved working conditions

Customer service runs on empathy, but at the same time, it’s not exactly known for being an easy gig with great hours. Virtually no one contacts customer service because they’re happy with something. Working in customer service inherently means dealing with unhappy customers who need help solving an issue. Keeping your team’s tank running on empathy and not on empty is vital.

It’s well known in the industry that stress, pressure, and angry customers can have a toll on the well-being and mental health of employees. This is where customer service chatbots can step in and take some of the heat; they can be used as gatekeepers, fielding incoming requests and helping to lighten the overall workload and lower the stress levels of your agents. 

Great customer service representatives are, by definition, very empathetic people – but even if their “empathy reserves” are larger than average, they are still using a mental resource with limitations. Repetitive requests and angry customers can cause “compassion fatigue,” which lowers service quality. 

But it’s not only about repetition of tasks or angry customers. Many customer service departments have tough shift limitations and demanding weekend work, which can impact employee morale in the long run. Many organizations also apply “vacation blackouts” during peak seasons. 

For employees who pursue a career in customer service, it gets harder and harder to balance a demanding work schedule with friends, family, or even personal time. Ultimately, this stress can cause your best and most senior employees to leave their jobs.

AI-powered chatbots can help out with this, too. Available around the clock, they can reduce the need for weekend work or night shifts and can help mitigate the sky-high demands of the peak season. 

Looking after the well-being of your customer service employees is not only about treating your team with compassion (though, this is the most important thing!), but it’s also a wise business decision long-term. Reducing employee turnover will have a significant impact on onboarding, training, and overall hiring costs, not to mention overall customer satisfaction and service quality. 

3. Automation makes people happy and more satisfied 

An interesting thing about automation is that it actually makes your staff happier: 92% of companies have seen an increase in their overall employee satisfaction after introducing automation.

This is because people inherently want to feel like their time and work has value. Employees don’t want to feel like they’re wasting their time – they want to do valuable work that’s good for the business. 

So from an employer’s standpoint, AI chatbots are actually a big opportunity: rarely does saving costs in the service department also make staff happy. Automation and AI can be an affordable and efficient way to increase employee satisfaction on the service floor and retain that top talent that defines the service quality and ensures your customers keep coming back for more.

Help your team help you

Great customer service agents genuinely love helping people and taking this trait for granted is a big mistake. By applying the right kind of technologies in your service organization, you can help your team shine and do what they do best. Download our eBook, Beginner’s Guide to Customer Service Chatbots to get started today.

AI is rapidly transforming the customer service space, most notably with Open AI’s ChatGPT, allowing businesses to offer customer support that is both personalized and efficient. By leveraging machine learning algorithms, natural language processing, and other AI-powered tools, customer service teams can now provide faster, more accurate, and more personalized customer support while also reducing costs and improving overall customer satisfaction.

What are the benefits of AI in customer service?

One of the most tangible benefits of implementing AI in customer service is its ability to automate routine—but time-consuming—tasks, such as answering frequently asked questions, routing inquiries to the right agent, and even resolving simple issues without human intervention. This not only frees up agents to focus on more complex tasks but also ensures that customers receive timely and accurate responses to their inquiries.

Another benefit of AI in customer service is its ability to provide personalized support to customers. By analyzing customer data, such as purchase history, browsing behavior, and social media activity, AI-powered systems can offer tailored recommendations and support that meet each customer’s unique needs and preferences. This improves the overall customer experience and helps businesses build stronger relationships with their customers.

AI in customer service also has the potential to transform the way businesses measure and improve customer satisfaction. By analyzing customer feedback in real-time, AI-powered systems can identify patterns and trends to help businesses identify improvement areas and make data-driven decisions to enhance the customer experience.

Is AI the future of customer service? 

Short answer: Yes. While AI is transforming the customer service industry as we know it, it’s also changing the nature of work for customer service teams. As more routine tasks become automated, some customer service jobs may become redundant or require new skills. However, AI will not replace human workers altogether. Instead, AI will likely augment human capabilities and create new roles requiring a combination of technical and soft skills. 

To prepare their teams for the future of work, businesses must invest in reskilling and upskilling programs that help employees develop the skills they need to thrive in an AI-powered environment. This includes technical skills such as data analysis, programming, and machine learning and soft skills such as empathy, critical thinking, and communication.

In addition to training programs, businesses must create a culture of continuous learning and innovation. This means encouraging employees to experiment with new tools and technologies, sharing best practices across teams, and fostering a growth mindset that embraces change and adaptation.

By investing in their workforce and embracing the potential of AI in customer service, businesses can create a win-win situation where customers receive better support and employees are happy and empowered to do their best work.

What are the challenges when implementing AI in customer service?

While AI has numerous benefits for customer service, implementing it can be challenging. Companies may need help with several obstacles that can hinder the success of their AI-powered customer service initiatives.

One of the critical challenges is data quality. AI algorithms rely on large amounts of data to learn and make predictions, but if the data is incomplete or accurate, it can lead to flawed insights or incorrect recommendations. To overcome this challenge, businesses must ensure that their data is clean, consistent, and relevant to the problems they are trying to solve.

Another challenge is integration with existing systems. Many businesses already have complex IT infrastructures that include multiple systems and platforms. Integrating new AI-powered tools into these environments can be daunting and requires careful planning and execution. The key is identifying areas where AI can add value and prioritizing integration efforts accordingly.

Finally, user adoption can also pose a challenge. While some customers may welcome the convenience and personalization offered by AI-powered customer service tools, others may hesitate or resist change. Similarly, some customer service agents may feel threatened by the prospect of automation and may require additional training or support to embrace new technologies. To address this challenge, businesses must communicate clearly about the benefits of AI-powered customer service solutions and provide adequate training and support for customers and employees.

By addressing these challenges head-on, businesses can maximize the potential benefits of AI in customer service while minimizing risks and ensuring a smooth transition toward an AI-powered future of work.

5 examples of companies that have successfully implemented AI in customer service

Many companies have already successfully leveraged AI to transform their customer service operations and drive business outcomes. Here are some notable examples:

1. American Express

American Express has implemented an AI-powered chatbot called “Amex Bot” that provides 24/7 support to its card members. The bot is designed to answer frequently asked questions, provide account information, and even make payments on behalf of the user. Since the bot’s launch, American Express has seen a significant reduction in customer wait times and increased customer satisfaction.

2. KLM Royal Dutch Airlines

KLM Royal Dutch Airlines has implemented an AI-powered tool called “BlueBot” that provides personalized travel assistance to customers via Facebook Messenger. BlueBot can help customers book flights, check-in for their flights, and even provide real-time flight status updates. Since the launch of BlueBot, KLM has seen a 40% increase in customer interactions on Messenger and significantly improved customer satisfaction.

3. H&M

H&M has implemented an AI-powered virtual stylist called “Ask Anna” that provides personalized fashion recommendations to customers based on their preferences and purchase history. Ask Anna uses natural language processing to understand customer requests and provide tailored recommendations that match each customer’s unique style. Since the launch of Ask Anna, H&M has seen a significant increase in online sales and improved customer engagement.

4. On Running

On Running is a Swiss running shoe company that has implemented AI to enhance its customer service operations. The company uses AI-powered chatbots and personalized messaging to help customers find the perfect pair of shoes for their unique needs and preferences. By analyzing customer data, such as running habits and foot size, On Running’s AI-powered system can provide tailored recommendations and support that improves the overall customer experience. On Running’s chatbot has achieved a 70% self-service rate, a 40% live chat wait time reduction, and a 72% CSAT. Some impressive stats!

5. Hobbii

Hobbii, a Danish yarn and hobby supplies retailer, has implemented an AI-powered chatbot called “Hobbiibot” to assist customers with their inquiries. The chatbot is designed to provide personalized support by asking questions about the customer’s needs and preferences before making recommendations. Since the implementation of Hobbiibot, Hobbii has seen increased customer satisfaction and reduced response time for customer inquiries. 

Hobbiibot handles 22,000 monthly conversations, has an 81% self-service rate, and has delivered a 74% improvement in the company’s overall CSAT. Hobbii’s Head of Customer Success, Kasper Tvernø Hartvigsen, says, “I wanted a chatbot that could funnel requests to the right place and could help support our growth into new countries in multiple languages.” The company plans to expand its use of AI technology to include predictive analytics for inventory management and targeted marketing campaigns based on customer behavior data.

These examples demonstrate how AI can help businesses improve their customer service operations by providing faster, more personalized customer support while reducing costs and improving overall customer satisfaction. By following these successful cases, other businesses can learn from their experiences and implement similar solutions that fit their specific needs and challenges.

How can you maximize the potential of AI in customer service?

To maximize the potential of AI in customer service, businesses need to invest in the right technology, people, and processes. This includes selecting the right AI tools for their specific needs and ensuring they have the necessary infrastructure and expertise to support them. 

Additionally, businesses must create a continuous learning and innovation culture that encourages employees to experiment with new tools and technologies and fosters a growth mindset that embraces change and adaptation. 

AI is poised to disrupt customer service and boost modern support platforms. By providing faster, more accurate, and more personalized customer support, businesses can improve customer satisfaction, reduce costs, and build stronger customer relationships. As AI technology continues to evolve, we expect to see even more exciting innovations in the customer experience and customer service space in the years ahead!

Check out how Dixa is leveraging AI in customer service in two exciting new features: