More and more companies are becoming interested in augmenting their customer service teams with virtual agents, but as with any new technology, practical information about the process is scarce. That’s why we decided to shed some light on our approach and the specific steps that lead us to a successful implementation. We believe that this flow works because it makes the process transparent for both sides: we understand what needs to be done to get expected results, and our clients have clarity and control over the entire project.
Six steps to a successful AI virtual voice agent implementation
1. Choose the right process to automate with virtual agents
In the first stage, we work with the client to determine which processes should be automated and in what order. We not only check if it’s technically possible but also if the solution will bring a satisfying ROI when implemented. To assess which processes are most suitable for automation, we take into account different metrics like current call duration and volume, the repetitiveness of the case resolution procedure, and the amount of creativity and empathy needed to handle the call.
2. Understand goals and cases for automation
In this stage, we cooperate with the client to gain a deep understanding of the process we’re going to automate. We ask the client for real call recordings, decision trees, and any other support documentation that can help us learn what it takes for a customer interaction to be successful. To precisely identify, gather, and analyze every task and action, we use the help of various tools and methods, e.g., user story mapping.
When we feel confident in our understating of the entire process, we put forward our recommendations for the scope of automation (taking into account investment size vs. ROI). We aim to start with a small project that covers the most important call volume for fast and low-risk implementation.
3. Prepare virtual agent conversation script
When the scope of implementation is established, we can move on to the script preparation stage. The script generally includes the core conversation flow, possible conversation paths, and specific virtual agent and customer utterances. Sometimes our clients already have a script ready, so we analyze it together and tweak it to better fit virtual agent goals and capabilities. If the client doesn’t have a script, we cooperatively create one based on the existing data like call recordings or internal procedures.
4. Build the virtual agent using the Talkie platform
Armed with the script and the story map, our customer success team configures the virtual agent using the Talkie platform. They play the role of customer advocates and make sure that the solution works in a way that makes the human-machine interactions as smooth and natural as possible. Even though we have a dedicated team of specialists, the platform in itself is designed to be used by people without a technical background by offering a friendly, drag-and drop interface.
If the virtual agent needs to be connected to the client’s systems to send or receive data, our developer team works with the client’s dev team to figure out the best way to do it.
5. Test and train the virtual agent
We have a set of tools that help us efficiently test the virtual agent, both from qualitative and quantitative perspectives. Our team uses a suite of automated tests and also manually picks up the phone and calls the virtual agent to test the conversion exactly how customers are likely to use it. We also invite the client team to join this process to ensure that the solution meets their expectations.
In addition to automated and manual testing, our platform has a tool that allows easy improvements of the areas that came up in tests, for example, bulk training of unrecognized queries. This stage ends when the client gives us the green light to go live.
6. Go live
Like live agents, AI-powered ones need to be closely supervised when they start on the job and regularly trained to keep up with ever-changing customer needs. When a virtual agent goes live, we closely monitor the conversations to improve the solution based on real users’ data. We use quantitative data in the reports to see how many calls ended with success and check for interactions that proved difficult to customers. Based on this information, we analyze specific call recordings and transcripts to see what can be adjusted in the conversation flow to make the solution more effective.
This stage lasts as long as the virtual agent works for the client. Of course, after the initial monitoring and adjustment phase, the amount of supervision is significantly lower. Still, virtual agents will always need to be supervised and trained to some extent — just like live agents.
Going beyond the AI virtual agent implementation
We put extra effort into client education from day one of the project, so they never feel like they’re left alone in the dark. Our goal is to make the client feel comfortable using our platform so they can monitor the conversations on an ongoing basis and stay in the loop of what the customers talk about. Additionally, being able to use the platform enables them to make changes to the conversation flow whenever it suits them and become independent of our schedule. We give our clients the autonomy to work on their own but stay at an arm’s length if they need any help and jump back full time once they’re ready to automate more processes.
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Looking for a more specific solution? Check out these virtual agent use cases: