A key responsibility of an AI trainer in creating improved customer encounters is to concentrate on crafting excellent conversation for a virtual representative.

A positive customer service encounter stays with you for a very long time. You want to feel that the entire process was handled in a professional and timely manner when you hang up the phone with your bank or insurance provider. You want to feel that you got the answers you needed. A virtual agent should be no different to interact with. Since you are aware that you are not speaking with a human, the stakes are actually higher since you have higher expectations for what a computer is capable of.

The objective of creating dialogue for a virtual agent is to offer a user experience that is at least as good (and ideally better!) than what is typically provided when contacting a company via phone or email. AI trainers must first comprehend how the various types of people who will interact with their virtual agents think in order to accomplish that and kAIron will be able to accomplish that at ease.

It’s a frequent misperception that the AI trainers in charge of creating the conversation flows must be a Ph.D. in data science or have extensive technical knowledge. Simply said, this is untrue. Our AI-powered virtual agents at kAIron enable us to create interactions that are comparable to corresponding with a live customer support representative. While having a foundational understanding of technology can be helpful, it’s equally crucial to be able to draw on a range of expertise from a variety of industries, including customer service, support, sales, marketing, etc.

Training and improving

In order to improve our virtual agents’ cognitive knowledge, we train them. Even though we can’t foresee every possible question that a consumer might ask, kAIron enables a virtual assistant to guess the correct topic with a small number of training phrases. This means that rather than writing a large number of training sentences, we concentrate on making each sentence of higher quality. Understanding the demands of the end-user is crucial since we place a lot of emphasis on developing training phrases with variation.

Writing quality training data is essential for creating engaging dialogues. Customers can tell when a virtual agent feels overly robotic from a mile away, so avoid this at all costs! To avoid this and to demonstrate to the end-user that you understand their predicament, add personality and empathy to the interaction.

Keep in mind that a virtual agent’s maintenance and training are ongoing processes. The model needs to be improved frequently through conversation data analysis, and your virtual agent should adapt to suit changing consumer needs.