Let’s start by looking at the existing methods that help customers find the right answers in a knowledge base before contacting Support.
AI advancements in recent years paved the way to a new kind of tools. These tools can be plugged into existing support channels, capture what the customer query is, detect the customer intent and suggest relevant articles or FAQs mapped to that intent.
Currently, such a tool requires a significant effort to set up and maintain. Reason: the AI technology behind these tools rely on supervised machine learning methods.
Translated in English, it means you (or your vendor) need to build and maintain a large library of intents, with one intent for each possible customer question that could be answered with an article. For each intent, you need to add and maintain a long list of sentences and text snippets your customers use to phrase the same question.
Let’s take an example from eCommerce. You want your AI to “understand” when customers are asking whether you ship products to a particular location, and suggest them an article answering their question. For that, you need to train the AI with many example phrases of this question, such as:
“Do you deliver to Ireland?”
“Where do you ship?”
“Would you post iPhone to France?”
“Can I order from Italy?”
And it’s not just the initial heavy lifting. You need to keep tuning this intent for months after launch, to ensure related customer questions are correctly understood and answered. It might not seem hard for one intent, but imagine doing this for 50 intents, or more.