As technology becomes increasingly complex, we get more questions per customer and often struggle to deliver excellent customer support as we scale. We all need a way to help more customers with fewer resources. Enter automated customer service.
To keep up with these changes, teams are looking at customer service automation to help fill the gap. But we’re not talking about “automation” like in the days of the early phone IVRs. The painful, not-so-helpful form of automation.
We’re talking about real artificial intelligence (AI) built into customer service software.
Technology has advanced to the point where automation can sometimes be more helpful than a human. Computers have instant access to unlimited data, all historical conversations and can recall any information on demand. The same can’t be said about a human.
While you lose creativity, flexibility, and emotion, you gain speed and consistency. Humans + artificial intelligence can work together to deliver exceptional customer support at scale.
Let’s dig into automated customer service and review what it is, why companies are incorporating it into their support strategy, what benefits they’re seeing, the pitfalls to avoid, and the best practices for getting started.
Ready? Let’s go.
Automated customer service is support that is partially or entirely provided by a system that is powered by artificial intelligence. Automation can come in many forms, which we’ll explore a bit later.
The primary benefits of customer service automation software are:
This technology can reduce the cost of human support representatives and help to provide an exceptional customer experience with less effort.
By 2020, 85% of all customer interactions will be handled without a human agent
Let’s explore why I believe nearly all support teams should be adopting this technology.
Support interactions fall into two categories. It’s either responsive or predictive.
You’re either responding to a customer question (responsive) or proactively helping a customer before they reach out to you (predictive). Predictive support is the newer and obviously preferred option, but it’s much harder to achieve. In fact, it has only become possible in the past year or two with the advancements in artificial intelligence and machine learning.
Data sits at the core of every support automation system. Data allows your team to do more than humanly possible. With data, your system can monitor a nearly infinite amount of conversations and indicators to understand customers often better than humans can. It can find patterns and correlations between topics and proactively solve issues before they become problems.
New employees are hard to find, expensive to hire, and time-consuming to train. On the other hand, artificial intelligence typically requires a one-time setup, configuration and training and then your everyday actions continue to teach the system.
Once trained, your support automation system will never leave you. Hooray!
Now I’m not saying AI-powered customer service will get rid of all your employees, but the cost of maintaining the system is a lot less than the cost of maintaining a team. If you have a business that gets a lot of frequently asked questions, you could see savings above 60% in a short amount of time. This will allow you to scale your support team without linearly hiring new team members.
Machine Learning allows you to scale your support team without linearly hiring new team members
Once programmed, computers act the same way every time. They don’t take holidays, they’re never sick, don’t have emotions and don’t get tired. Save for the occasional server hiccup they’re there when you need them.
This reliability makes a strong case for incorporating artificial intelligence into your current support operation center.
Automated customer service means that you will be able to provide more rapid support to your customers. When someone chats you with a question, you can respond instantly, any time, day or night.
Intelligent automation can be faster than self-service online knowledge bases, faster than human responses and they have unlimited capacity to answer new questions, so they never slow down.
82% say that getting their issue resolved quickly is the number 1 factor to a great customer experience.
Imagine if your average response time was 1 second? Sounds nice, right?
There has been an increase in chat-based support requests.
Chatting for help is becoming the new norm. As we all get used to texting and messaging in our everyday lives, we expect to interact with businesses the same way.
A recent report by Twilio found that 89% of customers want to use messaging to communicate with businesses, but less than half of all companies are available on this channel today.
Chat is preferred and also more efficient. It’s a win-win.
Chat support leads to easy adoption and use of chatbots. Think of chatbots as digital assistants that chat directly with your customers to help answer easy questions and escalate more complex issues to agents. More to come on chatbots.
People want to help themselves more than they used to.
They like finding their answers rather than calling someone or asking for help.
72% millennials don’t like phone calls.
This trend works very well for automating support since it’s giving the customer exactly what they want.
People often think that you can’t get more personalized than a human.
But humans can’t remember every conversation that any agent has had with a customer. They can’t memorize 100% of the answers in the knowledge base either.
AI can do these things. And in real time. Using historical data and customer context, automation tools can often provide a MORE personalized experience than a human can in some instances.
86% of customers will pay more for a better experience.
Historical customer service hiring was linear with new customer adds. Once you add x new customers, you know that you’ll need another person answering the phones.
With AI, that changes. Teams can often add 10x more customers for each new agent hire due to increased efficiencies.
Automated customer service isn’t just for direct-to-customer conversations. It can work behind the scenes to suggest answers to agents so they always have the knowledge they need at their fingertips.
It’s like having your best manager in every agent’s earbud at all times suggesting what to say next. It increases efficiency, consistency, and reduces hold times and transfers.
And last but not least, one of the most compelling arguments for starting to use support automation as soon as possible. It gets smarter every time you use it.
Machine earning algorithms allow your system to gain knowledge and recognize patterns over time, improving its performance with each new data point.
Using this technology in a customer support setting allows the algorithm to run in the background of support related chats, emails, or calls and collect the data coming in to find any pattern that may be present from these interactions that humans wouldn’t be able to catch.
Okay, but what happens when AI goes wrong? It can definitely happen. Here are a few pitfalls to watch out for.
Yes, automation is exciting, but the goal shouldn’t be to replace your support team completely.
We’ve seen teams try this approach and it ends up increasing customer frustration and can lead to churn. You can lose the human connection and personal touch with your customers if it’s not done correctly.
You want customers to be able to get to a live human when they want to. Sometimes questions are hard, and a computer just can’t handle it. Don’t keep the customer is a frustrating loop, quickly pass them off to someone to help.
We’ve all heard someone say something along the lines of, “bad data in means bad data out.”
If you don’t set up your system correctly from the beginning, it’s going to take a lot longer to learn and get to a place where it’s helpful.
Almost all intelligent customer service platforms will include comprehensive onboarding and training as part of their offering to make sure that you are set up correctly from the beginning. Once you’re up and running, the ongoing training and learning is often a breeze.
Automation can happen across any communication channel these days. You want to find the channels that have the most volume and also have easy questions.
For example, if every one of your website visitors asks how late you’re open, that’s low hanging fruit for automation. If every phone call ends up in a complicated troubleshooting session with your engineers, you may not want to try to automate that channel today.
Customer service automation can come in many forms. You can find everything from complete platforms that do everything to specific features that solve one specific use case.
Here are a few of the critical systems to look out for and how you can leverage these systems for automated customer service. Some of these blend, so it’s not a perfect landscape, but should give you an idea of what’s out there.
Full-service customer support software has historically been focused on making sure inbound customer inquiries are routed to the best available agent. You’ll see that these systems that are moving to omnichannel approach to take all conversations from all channels and put them into a single queue.
As this space evolves, the providers are often focused on the intelligence that goes into routing conversations more than solving conversations directly. Virtual assistants are common and they are paired with an agent to create powerful and efficient agents.
We’re seeing a new wave of providers emerge that are built in the past few years on new technology. These software providers are providing a holistic support solution but built around AI. If you have a large team or are looking for the best solution, these offerings are going to be right for you.
Leading platforms often integrate all of the features we are about to discuss in one seamless experience.
Chatbots are one of the primary and most efficient ways teams are automating support interactions today. Think of chatbots as digital assistants that can talk directly to your customers through chat (and sometimes voice) to quickly answer easy questions and route complex questions to the best available agent.
Simple chatbots are easy to integrate and deploy, and I believe that every company should leverage chatbots as part of their support strategy.
When you are looking for a chatbot solution, be sure to confirm which messaging platforms integrate with the product. An excellent chatbot platform will work across many messaging platforms including any native platform, Facebook Messenger, SMS, etc.
Agent Assist technology can often be found as part of a complete solution but doesn’t have to be. When we refer to agent-assist, we’re talking about technology that makes agents more powerful and efficient.
This technology can take the form of ticket routing optimization that uses skills and historical data to get tickets to the best available agent, internal chatbots, or other forms of suggested knowledge that live inside a ticket that guides agents on how best to answer a question.
It can even be as simple as automating note taking and ticket tagging within your system so that your agents don’t have to waste time on those mundane details.
Automated response technology sits between a self-service search-based knowledge base and a fully automated chatbot. These are AI machines that can suggest specific articles or answers to a customer before they connect with an agent. The goal is to reduce the number of requests that agents have to deal with by knocking off the easy questions before a conversation starts.
This type of technology can be highly effective but is a bit less natural and engaging than chatbots. I believe we’ll see this technology morph into chatbots as technology continues to advance.
We’re starting to see knowledge bases pop up that are a bit more intelligent than your standard knowledge base.
These are online self-service knowledge bases but use AI to help direct customers to the best articles and also uses AI to help internal teams identify content that needs to be updated.
This hybrid system is pretty helpful for customer support automation but is most powerful for a team’s internal knowledge base.
If you don’t want a fully built platform or product, you can always use available tools to create your own support automation system. You can find companies that will provide you with the data and toolset to be as flexible as you want. These range from AI APIs, communication APIs, Data transcription services and more.
For example, you can build chatbots on any messaging platform using IBM Watson. You can use Twilio APIs to create call center software and you can use Amazon Connect to set up a voice IVR in a few minutes.
These tools are meant to be used as part of a broader solution but are very flexible if you have the right development resources.
When you think about what channels to apply automate against first, consider your audience.
Young audiences often prefer chat-based communication and appreciate automation because it’s faster and they don’t’ have to talk to anyone. In fact, 76% of millennials have said they don’t like to call someone to get help.
If you primarily serve an older audience, they are more likely to want to work with a real person.
You can quickly address both by providing options for automated help with clear and easy prompts to be transferred to a live agent at any stage.
Humans and machines should work together. Remember, automated customer service tools are here to enhance the experience, not replace employees. Your agents should get as much value out of our automation tools as your customers do.
Often the knowledge and systems you’re creating for customers is the same knowledge as what your agents are referencing. Look for a system that can use this knowledge both internally as well as for customers.
While technology and automation can be fun (at least I think it is), don’t lose sight of the exceptional customer service you provide today. Automating should add to your overall support strategy, not detract from it. Remember, it’s a marathon, not a sprint.
As much as we would all like it to be, automation is not a set it and forget it project.
Well executed personalized automation requires regular reviews to ensure it’s up to your standards. Customer needs change, new knowledge gaps are uncovered, and products change. While the system should get smarter on its own by continually adding new data, it still needs to be “trained” with feedback so that it is using that data correctly.
Get in there every week and test out new scenarios to ensure it is as good as you think it is. Ask hard questions and test the limits. Your customers certainly will.
Don’t try to pretend that you’re not automating certain aspects of your support. Remember, a lot of customers not only like it but prefer it.
Be open and transparent about your automation efforts and constantly ask for feedback from the end users to ensure the customer experience is exceptional.
Don’t try to over-engineer everything in the beginning. Your system will need time to learn and so will you. You will learn a lot from the data that you continually analyze.
Keep the options simple to reduce frustration and make it easy to reach a live agent when needed. This will ensure your customers never get stuck or frustrated.
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