Conversational AI technology is fueling the next way of customer and employee experiences for businesses worldwide. How does it work, what the main benefits are, and which steps to follow to implement the technology within the specific company? In this article, we will provide insights to answer these questions.
What is conversational AI
The developments in voice recognition technologies now allow designing tools and systems that can mimic conversations with real people using digital and telecommunication technologies. Simply put, nowadays, virtual AI-fueled agents can talk to customers, understand them with up to 98% accuracy, and offer relevant answers.
Statistics show that only 1% of customers can distinguish AI-based voice agents from humans. Unlike the “traditional” IVR voice calls where the system is just capable of playing pre-recorded samples of texts or recognizing voice commands, current virtual agent software understands natural language, can understand human speech, detect and mimic emotions.
There are also additional factors as added background noise (office or a call center) or interjections like “umm” and “aha” that sound natural and make the whole dialogue indistinguishable from the conversation with a human agent.
Sounds interesting, but what are the real benefits of such automation for companies and their employees?
Benefits of the conversational AI
Conversational AI allows businesses to provide personalized experiences using the tools that customers like more. As a result, queries are solved in a fast and efficient way. Here is how exactly smart virtual agents can help both companies and their employees.
Better customer acquisition
Virtual agents provide omnichannel communication. For AI, there is no difference between using WhatsApp or email. AI-fueled virtual agents like the ones built by Neuro.net can immediately send any information to the channel of choice, thus increasing conversion rates.
Increased customer satisfaction for reduced churn
Virtual agents can immediately answer the call, and the company can have as many assistants as needed, meaning there will be no lost calls or customers left with no help. This significantly reduces churn and improves overall customer satisfaction.
Predictable service quality
While a human agent may fail to follow the script, choose the wrong tone for the conversation, or make mistakes when tired, AI never gets tired, always follows the instructions, and can learn many more answers than a typical human agent. This allows reaching a predictable and consistently high level of customer service.
As said above, when needed, the company can quickly increase or decrease the number of virtual agents. This allows saving money and efficiently processing the increased volume of calls in hot periods like the holiday shopping season. It would be hard to be flexible with a human workforce, as it takes time to find and train agents.
Improved employee satisfaction
Virtual agents can do routine work, thus leaving human employees more time for solving creative and more exciting tasks. Performing more high-value and meaningful work leads to improved employee satisfaction and reduces churn.
Conversational AI allows businesses to provide personalized experiences using the tools that customers like more. As a result, queries are solved in a fast and efficient way.
How to implement the conversational AI: three practical steps
There are several stages of conversational AI adoption.
First, the company’s management needs to fully understand the benefits this new technology may yield and align it with the current business goals. At this stage, the company will have to answer several important questions, including the overall goal for the Conversational AI implementation, what management principles will be used during the project, and how it will be evaluated.
If there is a match and getting advantages of deploying virtual agents will highly likely demonstrate positive ROI, then the second stage begins.
When the selected conversational AI technology provider trains the system using the customer’s data, it is a pilot project and deploys some agents to test the efficiency. For example, Neuro.net technology requires only 1000 call records to train the NLU engine.
Questions to answer at this stage are what the needed feature set is, what priorities and KPIs are, how the performance will be tested and analyzed, and what the fixes process will be?
Afterward, the results should be analyzed, changes made if needed, and the scaling stage begins. At this stage, the company deploys more virtual agents and continually switching the communications with customers to AI-only mode. The more calls are processed by agents, the more effectively they work. For example, after 100+ thousand calls, the Neuro.net conversational AI ensures 98% recognition accuracy.
It is crucial to understand how exactly the solution can be scaled and what business processes can also be boosting using conversational AI.
It is important that there are no changes in how the business works. The conversational AI technology seamlessly integrates into the current workflows bringing its benefits without influencing how the company is perceived.