How to improve your customer service with conversational AI

Today’s customers want helpful answers — and they want them right now.

It’s a lofty expectation that’s inspired many customer service teams to turn to automation and AI-powered technology to improve their response times, handle high volumes of customer inquiries, and deliver personalized experiences.

But here’s the thing: Even if artificial intelligence (AI) speeds things up, most customers don’t want to feel like they’re being helped by bots. Customers are still wary of AI in customer support, with a recent Gartner survey revealing that a whopping 64% of customers would prefer that companies didn’t use AI for customer service.

Fortunately, there’s a way you can reap the efficiency-boosting benefits of AI without sacrificing your customer experience. It’s called conversational AI.

What is conversational AI?

Conversational AI is an AI technology that enables a machine to answer customer questions and engage in customer interactions in ways that sound completely human and natural.

For example, imagine that a customer reaches out to your contact center for information about your return policy. With conversational AI, a chatbot would process and understand the customer’s question and then respond using human language — rather than the stiff and robotic stuff you’re likely used to with AI chatbots.

You don’t need to understand the ins and outs of how conversational AI works to use it effectively. But, if you’re curious, the gist is that conversational AI uses a combination of natural language processing (NLP) and machine learning to facilitate real-time customer conversations through chatbots and virtual assistants.

This means that you can move certain common questions or interactions off of your support agents’ plates and streamline your service operations — while simultaneously delivering relevant information and speedy responses that amp up your customer satisfaction.

Put simply, think of conversational AI as the sweet spot for your call center: Your support team gets all of the advantages of AI without your customers feeling like they’ve been passed off to a robot.

4 types of conversational AI to know

There’s a long list of advancements in AI tools in the past few years — and conversational AI is one of the more relevant and impressive ones when you want to optimize your service experience and operations.

However, conversational AI itself is a broad category and there are several different types of this technology under this umbrella:

1. Chatbots

You’re already familiar with chatbots. These automated programs are used on websites or messaging platforms to handle customer queries and provide instant responses with little to no interaction from your human agents. The chatbot typically relies on predefined scripts and rules to deliver programmed responses.

Example: A customer asks your website chatbot about product availability and the chatbot responds with information that satisfies the customer’s needs.

2. Generative AI bots

A generative AI bot is similar to a chatbot as it engages in customer conversations via messaging. But, think of it as the next step up from a typical chatbot.

Whereas a chatbot uses predefined scripts, a generative AI bot uses natural language understanding (NLU) and deep learning to understand the context and generate unique replies based on the user’s input. For that reason, they can handle a broader range of topics and engage in more fluid conversations than a straightforward chatbot.

Example: A customer asks a generative AI bot for help troubleshooting a technical issue. The bot asks clarifying questions before providing tailored solutions.

3. Interactive voice responses (IVRs)

IVRs use pre-recorded messages and voice prompts to guide customers through a series of options or menus by either speaking specific words or pressing a key on their phone.

Example: A customer calls your company and is given an automated menu of options routing them to the correct live agent to help them.  

4. Voice assistants

Like IVRs, voice assistants handle customer requests over the phone. However, these systems are more advanced AI-driven systems that use speech recognition technology to allow the voice assistant to interact with the customer through different voice commands. They provide dynamic responses (beyond simple menu navigation) based on user input.

Example: A customer calls to update their billing information and the voice assistant confirms their address, asks if the customer wants to update their payment method, and provides information about upcoming billing cycles.

What are the benefits of conversational AI for customer service?

According to recent research, 52% of contact centers have already invested in conversational AI — and another 44% plan to adopt it.

So, why are so many support teams throwing their weight behind conversational AI solutions? Because of the many compelling benefits for agents and customers.

Benefits for your support team:

• Smoother workflows: Conversational AI can address many of the FAQs and other common customer requests that are time-consuming for your agents. This frees up their time and mental bandwidth for more complex customer conversations that require a human touch.
  
  
• Scalability: Today’s customers expect omnichannel support. Using virtual agents means you can expand your service offerings (including your availability and your support channels) without having to aggressively hire. Plus, deploying conversational AI can offer cost savings compared to hiring more agents.
  
  
• 24×7 availability: 60% of customers admit they expect always-on customer service, but staffing your call center to offer 24×7 support is a challenge. AI can help you satisfy this customer expectation without burning out your agents.
  
  
• Consistent quality: Because many conversational AI solutions rely on programming (and, in some cases, predefined scripts or rules), you’ll provide a far more consistent customer experience.

Benefits for your customers:

• Multilingual support: Many conversational AI tools are multilingual, which means you can support your customers in their native language — without hiring agents who are fluent speakers.
  
  
• Shorter wait times: Even on their best day, your support agents can’t respond as quickly as conversational AI agents. With this technology, customers benefit from almost instant responses.
  
  
• Self-service options: 81% of customers say they want more self-service options. And, while conversational AI means your customers are technically interacting, they’re able to do so without having to connect with a live agent. Whether a chatbot answers their question directly, refers them to a knowledge base article, or a combination of both, your customers feel as if they’re getting their questions answered on their own.
  
  
• Accessibility: When you’re able to expand your support to various channels like chat, voice, messaging apps, and more, it’s easier for customers to reach out in the way they prefer. 

How to establish best practices with conversational AI in customer service

It’s tempting to think of customer support AI as a “set it and forget it” solution — something you put in place once and then sit back and watch as tasks are magically done for you. In reality, you need to be more hands-on and proactive to effectively use contact center automation and AI. Here’s how.

Understand the ins and outs of your customers

You can’t expect your conversational AI solution to know your customers if you don’t know them. You and your team are responsible for training your conversational AI solution, and that starts with a thorough understanding of your customers.

Look back at your previous customer interactions, conduct surveys, and collect feedback to uncover common customer needs and preferences. That’s information you can use to build prompts and use cases for your AI platform.

The more context you can feed to the AI solution (like previous customer conversations and product information), the better equipped it is to update its algorithms and deliver accurate responses that help (rather than frustrate) your customers.

Prioritize the customer experience

63% of customers say they’re frustrated with self-service options that use AI and similar technologies. But that’s not evidence that you should skip AI altogether — it’s evidence that you need to be thoughtful and intentional about how it fits into your overall customer journey.

While conversational AI is a valuable tool, you still need to prioritize user-friendly interactions and easy-to-navigate conversational flows. Skip complex menus and lots of jargon and instead opt for simple, straightforward journeys and language. Conversational AI allows your customers to express their needs naturally, so lean into that capability.

Ultimately, conversational AI shouldn’t just make your agents’ lives easier. It should make your customers’ lives easier too. If it’s not doing that? You need to make some changes.

Know (and stick to) privacy and security regulations

Training your conversational AI platform will likely involve feeding it customer data. It’s your responsibility to confirm your chosen platform adheres to privacy regulations and protects that sensitive information. Take smart, security-minded steps like:

• Implementing encryption for particularly sensitive information
  
  
• Being transparent with customers about how their data is used
  
  
• Training your agents on what information they can feed to AI — and what should be more carefully protected

These steps foster more trust with your customers, encourage critical thinking among your support agents, and help you run a service operation that’s always above board.

Define a seamless escalation process

Conversational AI can accomplish a surprising amount, but there will inevitably still be times when a human agent needs to step in. When that happens, it’s helpful to have a defined escalation process in place that ensures a smooth transition from chatbot to human agent. 

This process should answer questions like:

• What are the indicators an interaction should be passed to a human agent?

• How and where can the human agent get the context of that conversation so the customer doesn’t need to repeat themselves?
  
  
• Which agents will receive those escalations? Do you have more specific routing criteria?
  
  
• What will be communicated to the customer about that transition?

Ironing out these answers early can minimize frustration and maintain continuity in support.

Commit to continuous improvement

Your agents are constantly learning and your conversational AI platform is too — provided you’re willing to consistently teach it. You can continuously improve your conversational AI solution by:

• Regularly updating training data with real customer interactions, new trends, FAQs, emerging technologies, and product changes to ensure responses are up-to-date and relevant
  
  
• Establishing processes for customer service reps and users to provide feedback, especially when the AI fails to satisfy a customer request or question
  
  
• Frequently evaluating the performance of your conversational AI by analyzing relevant metrics like response accuracy, resolution times, and customer satisfaction (CSAT) scores

Ultimately, conversational AI is only as good as you are. And when you’re getting better at your job, consistent training and updates allow your AI solution to get better at its job too.

Choosing a conversational AI platform for your customer service team

Want to get the most out of this technology? You need to choose the right platform. There are plenty of options (a quick search on G2 yields more than 580 potential products) and sorting through them can feel daunting. Here are five things to keep in mind when weighing your options.

1. Understand your business needs and goals

Much like when making any other decision, start by defining the problem: What does your support team need most? Why exactly are you considering conversational AI? For example, you might want to:

• Reduce response times
• Handle more inquiries
• Improve agent efficiency
• Provide self-service options
• Offer multilingual support

Determine your primary goal and then use that as a lens when reviewing your options. While it sounds simple, a clear objective will help you focus your search on the tools that fit your specific needs.

2. Prioritize natural language processing (NLP) capabilities

For conversational AI to feel intuitive and somewhat human-like, it needs to have strong NLP capabilities. Remember, this is what allows the AI to understand questions in different phrasings and write appropriate and helpful responses.

Getting down to the nitty-gritty of NLP can be technical, but there are a few questions you can ask a solution’s sales or support team to get a better sense of what the tool can do:

• How does your platform handle conversational nuances, such as different phrasing, abbreviations, and spelling errors?
  
  
• Is the AI capable of detecting sentiment and adjusting responses accordingly?
  
  
• What languages are supported, and are all language models equally accurate?
  
  
• Does your platform have customizable language models that we can tailor to our industry and products?
  
  
• How does your NLP model continue to learn from interactions to improve over time?
  
  
• How well does the AI keep track of longer conversations where the customer asks follow-up questions or provides additional information?

Pay close attention to the answers to these questions. A conversational AI solution that doesn’t have solid NLP will end up being more of a hindrance than a help.

3. Consider integrations and scalability

Make a list of the tools your team currently uses and loves — like your CRM, knowledge bases, and customer engagement software. Then, look for platforms that seamlessly integrate with those apps. Doing so means your AI can draw from your customer history and relevant data to deliver more accurate and contextualized responses. 

Additionally, consider your future plans and potential growth. Finding a solution that can grow with your team allows you to support more channels and handle higher volumes as your customer base expands.

4. Evaluate security and privacy standards

You already know that data privacy and security are non-negotiable in customer service, so that’s something you need to keep in mind when evaluating potential platforms. Confirm that the conversational AI solution complies with relevant privacy regulations, offers data encryption, and supports role-based access control.

While you're at it, make sure the platform has transparent policies for how it handles data. That will give you confidence in the platform while also helping you build trust with your customers.

5. Look at customization and training capabilities

The best conversational AI platforms will allow you to tailor interactions based on your unique needs. So, look for a tool that lets you customize prompts, responses, and workflows. 

Training capabilities are equally important. Determine how easy it is to update and refine the AI with new data and scenarios. That’s something you’ll need to do frequently, so it’s worth making sure the process won’t be a pain.

How to measure the ROI of your conversational AI investments

When you make any sort of change on your team, you understandably want to confirm that it’s paying off. The best way to do that is to return to your goal — the one(s) you set when exploring conversational AI in the first place — to see whether you’re satisfying that objective or not.

Of course, when it comes to proving ROI, you’ll want to be able to attach some numbers here. Fortunately, there are many different metrics you can consider when measuring the pay-off of your conversational AI investments, including: 

• Cost savings: Diverting simple questions to AI can free up your resources and reduce your staffing needs, which saves money.
  
  
• Customer satisfaction (CSAT): Compare customer satisfaction scores before and after your AI implementation to see its impact.
  
  
• Resolution rate: Track the percentage of customer inquiries resolved by AI to see how well it handles issues without any human intervention.
  
  
• First contact resolution (FCR): Measure how many inquiries are resolved in the first interaction to see how efficient your AI tool is.
  
  
• Response time: Evaluate how quickly the AI handles your customer inquiries (especially compared to a human agent).
  
  
• Escalation rate: Consider the frequency of tickets transferred from AI to human agents to understand its effectiveness in managing more complex issues.
  
  
• Agent productivity: Track productivity improvements to see if AI has boosted your agents’ efficiency.
  
  
• Customer retention: Analyze whether customers have remained loyal or increased their engagement after interacting with the AI.

While metrics matter, remember to also frequently connect with your support team to collect their feelings and anecdotal experiences about how AI has both positively and negatively impacted them.

Improve your customer experience with Assembled Assist

Assembled Assist can help you seamlessly handle routine questions and interactions, while also ensuring your team has the structure, insights, and bandwidth to jump into more complex customer interactions when necessary.

You’ll see plenty of instances of conversational AI built directly into Assembled Assist. For example, it will guide your agents to perfect customer replies with generative AI. Or you can use the simple workflow builder to design responsive automations — all while using plain language to describe the steps of the workflow.

Put simply, Assembled Assist helps your support team work smarter, respond faster, and ultimately, achieve your top priority: delivering a more reliable and satisfying customer experience.

‍Ready to bring more power to your customer support team? Book a demo today to see how Assembled Assist can transform your service operations.