Virtual agent
Create virtual agents to support your human teams and ensure customers always get the service they deserve. A virtual agent is just one of the many ways RingCentral RingCX can help you deliver seamless, exceptional customer experiences.
Delivering exceptional customer experience is a goal of most businesses, and is never an easy task. Doing it at scale is even more difficult, with fast response times and personalization more difficult with a greater number of queries to handle. That’s where AI-powered solutions like virtual agents come in.
A virtual agent is a tool that can support your actual agents by taking some of the burden from their shoulders. Leveraging artificial intelligence technologies like natural language processing (NLP) and machine learning, virtual agents are becoming more advanced every day. With RingCentral RingCX, you can create and use virtual agents to further bolster the overall omnichannel customer experience. It’s just one of the many ways you can customize the contact center solution to suit your business needs.
What is a virtual agent?
A simple virtual agent definition is that it’s a software program—or feature of a broader software platform—that uses AI technology to interact with and provide guidance to humans.
Ever visited a website and seen a chat interface pop up in the bottom-right corner asking if you need help? That’s one of the most common examples of virtual agents.
Virtual agent vs virtual assistant
The key difference between virtual agents and virtual assistants is that virtual assistants are human agents while virtual agents are software. A virtual assistant is a remote employee that handles tasks assigned by their employer. Some of those tasks might include:
- Scheduling appointments and managing calendars
- Making travel arrangements
- Taking and transferring phone calls.
A virtual agent is an automated software tool that can respond to a customer’s query. The difference between virtual agents and chatbots, however, is a little more complex, so let’s get into it.
Chatbot vs virtual agent
The main difference between chatbots and virtual agents is that chatbots are much simpler and less sophisticated. Any solution that simulates real-time conversation with a human can be described as a “chatbot”. That, then, can include rules-based tools that only answer specific questions or simplistic solutions that are able only to identify certain keywords.
For instance, if you ask a basic chatbot to track your order, it might just see the words “track my order” and send you to the appropriate webpage.
Virtual agents, on the other hand, leverage AI technology in order to communicate more like an actual human would. Thanks to a combination of natural language understanding (NLU), NLP, machine learning, and other similar technologies, they can hold more complex conversations and respond to a broader range of queries.
In short, virtual agents can actively understand what a customer is saying rather than scanning for certain phrases. They can grasp intent and provide personalized responses. In the same example as we mentioned above, an AI virtual agent could take your order number and find the relevant information for you before asking if there’s anything else it can help with.
Simple chatbots, therefore, are relatively well-equipped for common queries and even some more complicated questions. They’re comparatively easy to implement and often at a lower cost. If you only need something to deal with basic questions, gather customer information, or schedule a call-back, then chatbots can do the job. If you’re looking for technology for troubleshooting, account management, or more in-depth tasks, then virtual agents are likely a much better choice.
However, you do need to remember that AI isn’t magic. AI virtual agents are only as good as the datasets they’re trained on, and this is both a good and a bad thing. The good news? You can train them off your particular customer base, and they can develop through use. The bad news? If you don’t have a decent dataset, they’re functionally useless.
Types of AI virtual agent
Virtual agents in AI circles can mean a number of different things. They can work as customer service representatives, personal assistants, and more. Let’s take a look at some of the more common types of virtual agents:
Virtual voice agents
Intelligent IVR
End-to-end virtual voice agents
Integrated virtual agents
AI receptionists
The technology behind an intelligent virtual agent
Virtual agents make it simple for your customers to get answers to questions. However, there’s little that’s straightforward about the technology behind them. Machine learning enables large language models (LLMs) and other cutting-edge technologies to evolve. Decades of AI development and huge advancements in NLP have narrowed the boundary between humans and machines.
Don’t worry about virtual agents taking over just yet. There are plenty of lackluster options out there. Others are chatbots or IVRs masquerading as virtual agents. For these AI embodiments to work properly, they must be fed huge volumes of data and be built for purpose. If you want truly intelligent virtual agents, then you need a fully integrated platform like RingCX.
Virtual agent software capabilities
Virtual agents have come a long way since the primordial, rules-based chatbots. Today’s virtual agent solutions can add an array of impressive capabilities to your contact center:
Natural language understanding (NLU)
NLU is the technology that powers the ability of virtual agents to understand what customers and team members are saying. A natural language understanding model gathers data and improves over time as long as the training data is high quality. Poor-quality training data can prove to be just as problematic as a limited amount of training data.
Conversational AI
Virtual agents use conversational AI to convey understanding and useful information to users. Whether it's your customers or your team members, AI provides human-like responses with audible speech. RingSense AI is a conversation intelligence platform available across the RingCentral platform, improves customer experiences, and unlocks customer insights. It also provides AI-powered coaching tips that help your team deliver consistent, successful pitches, boosting your win rate.
Sentiment analysis
Not all human voice communication is based on the words we speak. Live sentiment analysis is a must for virtual agent services to dig deeper and understand the true meaning and motivation of each speaker. For example, the phrase “that sounds okay.” could mean the customer is happy, content, or disappointed. It depends on the inflection of their voice and the context surrounding the response. Effective virtual agents can parse these details and make inferences. Sentiment analysis provided by RingCentral enables users to detect the tone of conversations and identify any opportunities or issues and respond accordingly.
Task automation
Virtual agents can be set up to take care of tasks such as payment processing or call summaries. Task automation saves your team time by deflecting calls from live agents and offering customers rapid service. RingSense AI’s conversation intelligence reduces after-call work by up to 20% with post-call transcriptions and real-time AI summaries that enable agents to follow up on tasks more effectively.
Omnichannel agents
You want a virtual agent that can meet your customers on multiple channels, including live chat and phone calls. Such agents require the ability to read and write in human-like language. Omnichannel virtual agents help relieve your support reps and meet your customers on their preferred channels. Platforms like RingCentral also enable your team to work from a single pane of glass when deploying, implementing, and interacting with virtual agents, improving their effectiveness.
Integrations
Many virtual agent solutions can integrate with other tools in your tech stack. Connecting to business applications allows the AI to gather and analyze data that helps you gain new insights. Additionally, virtual agent app integrations can improve performance and productivity. For example, you can integrate RingCentral with your CRM or ticketing system. Once the data is connected, our AI-powered virtual agent accesses customer records. The agent uses this information to handle calls more efficiently, providing 24/7 customer support.
Virtual agent example use cases in contact centers
Virtual agents excel in contact centers where they can reduce the workload of your team and manage early communications with customers. Three key areas to implement them are as:
Customer service agents
Virtual agents shouldn’t replace your live chat agents, but they’re an invaluable addition to any customer service team. Many queries that customer service agents deal with are relatively routine:
- What’s the status of my order?
- What’s your return policy?
- One of my items is damaged, what can I do?
- When will this product come back in stock?
- Can I update my account details?
All of these can be answered by a virtual agent in real time. Conversational AI allows the responses to feel more natural. This means that routine queries no longer require one of your team to write an email response or engage in a lengthy phone conversation.
Instead, they’re resolved through the virtual agent. Customers go away happy, and employees have more time to deal with in-depth questions.
Live agents for IT support
Just like customer service, an IT helpdesk will have to deal with lots of routine requests. Password resets, software updates, and basic troubleshooting can all be handled through messaging a virtual agent.
Even for more complicated questions, a basic chatbot or AI-powered agent can take details and create a ticket. Once again, this improves customer satisfaction due to fast responses and frees up your IT staff from having to work on these mundane tasks.
Agents for lead generation
Virtual agents and chatbots can also help your sales department by assisting with lead generation. They provide a quick way to capture user information and work out their intentions.
By having a virtual chat agent take details such as name, email, and reason for contacting, you have enough information for your sales team to start pursuing a lead.
Benefits of a virtual agent
Implementing AI virtual agents in your business has lots of potential benefits for customers and employees alike. Some of the main advantages include:
Available 24/7
Providing customer service 24/7 is challenging for any business. You either need to employ enough staff for round-the-clock shifts, outsource to call centers in other timezones, or provide limited hours. However, with a virtual agent, you can have something available all the time.
Virtual agents can answer most routine questions and for more difficult queries, they can schedule a call-back or email from a human agent. While scheduling may not give an immediate answer, it still leads to increased satisfaction as the customer knows they’ve been heard.
Reduce pressure on human agents
The drive to constantly improve customer experience can put a lot of pressure on customer service agents. Often, staff are torn between wanting to provide in-depth personalized service and wanting to get through as many tickets or calls as possible. By triaging requests through AI-powered virtual agents, you can reduce this. Staff no longer need to spend time on basic, simple problems, reducing the amount of requests they have to handle. While that means the questions that do come through are more complicated, they can spend more time answering them. Using an AI virtual agent is a great way to improve employee satisfaction and customer engagement.
Gather actionable data in call centers
One of the less often considered virtual agent benefits is that they can also help you build up a pool of actionable data from the customer interactions they handle. Combined with other uses of AI and natural language processing, this data can be analyzed to produce helpful insights.
Expect to see answers to the most common questions, information about what products have the most issues, and the main complaints or compliments you receive. You can also gather information on when people contact you, where they’re contacting you from, and how long the average conversation lasts.
This information can be used to build up a self-service knowledge hub, further reducing the amount of communication you receive. Not only that, but remember that actionable data means improved business decision-making.
Artificial intelligence constantly improves
Since many virtual agents are built using machine learning, they have the capacity to continuously improve. Combined with natural language understanding, this means that as their dataset grows, they grow in capability as long as they have access to high-quality data.
The more interactions an AI virtual agent has, the more information it has to work from. It will become better at ascertaining tone and intent. Not only that, but it’ll grow more familiar with the questions asked of it and be able to provide better responses.
Improved customer experiences
Providing excellent customer service is vital to running a successful business. Virtual agents can help with this by:
- Providing personalized answers
- Automating routine tasks
- Reducing wait times/time spent on hold
- Allowing customer service agents to spend longer on each case.
The video below shows how the company Office Gurus has benefited from using RingCentral to optimize its customer experience and contact center efficiency:
Find out how Office Gurus manages over 4,000 sales & support agents with RingCX
Key performance metrics for virtual agents
In order to keep track of whether or not you’re reaping the full rewards of virtual agent software, you should monitor key performance metrics. You can monitor the performance of your virtual agents in much the same way as your human agents. However, AI learns and grows in different ways than the rest of your team. Key performance metrics to monitor for virtual agents include:
- Intent recognition rate: How well does the AI recognize user intent? Do callers often become frustrated, telling the agent, “no, that’s not what I asked” or something similar?
- Percentage in-scope: The proportion of calls that fall within the scope of implementation. This helps determine whether your implementation is effective or being used in the right business areas.
- First contact resolution (FCR): What percentage of customers have their issue resolved on the first try? FCR assesses the ability of AI agents to solve problems and also direct calls to the right human reps.
- Containment rate: How successful your virtual agent is at solving issues without any human involvement. High containment deflects lower-tier calls, saving your human agents for complex tickets.
- NPS/CSAT: Net promoter score and customer satisfaction surveys track the virtual agent experience.
Things to consider when choosing a virtual agent
Every virtual agent is built differently. Some excel in different areas than others. It’s vital to know what to look for when choosing the best virtual agent for your organization. Consider the following when choosing a virtual agent platform:
- Conversational interface: An intuitive interface is built for sales and customer service. It's also programmable by your IT team.
- Automations: What can the virtual agent automate? Can it simplify workflows by taking over repetitive tasks?
- Customization: Can the solution be tailored to your needs or your audience? Does it offer enough flexibility to work in multiple environments?
- Integrations: Does the platform integrate with your CRM and other existing business tools? Does it include an open API that your development team can use?
- Deployment: What technology is required to deploy your virtual agents? How long does it take, and how much onboarding will your team need to benefit from using virtual agents?
- Accuracy of responses: How effective is the virtual agent itself? What’s its quoted success rate, and what case studies or customer success stories back the data up?