Passez à la troisième étape de l’assistance client : la recherche basée sur l’IA et l’automatisation du langage naturel

Earlier this month, we continued our three waves of customer support series with the second wave: workflows. We explored how this wave of automation is tackling processes with greater complexity, and talked about how companies have benefited from this scaled approach of servicing customers.

But the question is: for how long? It's no longer impressive for an entire customer journey to be automated from initial research, to purchase, to receipt of a product, to post-purchase support.

It needs to work.

Yext research shows that up to 50% of consumers still call live customer support teams as their first action when seeking support, well over using a chatbot, navigating a company's site, or using search in any form. And yet, this is at odds with the fact that 85% of consumers say that it's important to them to be able to resolve issues without contacting the company. These automated workflows and tasks — while serving as critical steps in the evolution of customer support — still have a gap to close with respect to customer behavior and expectation. There are still nuances that automated workflows aren't fully understanding.

Enter the third wave of customer support: AI search and natural language.

Let's zoom out for a second: today, searching is almost like a sixth sense. As consumers, we search multiple times per day on search engines and websites to find information. Getting answers to our question quickly and without friction is critical to the customer experience – especially when it's support-related. What's more, Gartner research shows that a full 70 percent of people prefer to use self-service digital tools, like search, to resolve their issues.

But for a brand's website to be as effective as a human in resolving customer issues in an efficient, straightforward way it comes down to exceptional language comprehension.

Unfortunately, our research indicates that most website search and bot experiences are nowhere near where they need to be with understanding natural language.

When asked about the main issues on a company's help site, respondents had a lot to say:

60 percent said that "It doesn't understand my question."

53 percent said that "It delivers unrelated search results."

And 39 percent said that "It provides out-of-date or inaccurate information."

That's because the search experience isn't conversational. (By contrast, when you type a question into Google, it understands your intent before you even finish typing.)

In other words, the first two waves of automation — tasks and workflows – made some processes faster, but they're just not cutting it when it comes to answering customers' (natural language) questions.

That's a problem, because customers expect the same kind of intelligence from any search bar, whether it's affixed to the world's largest search engine or their favorite brand. In other words, if someone types in "what is your refund policy?" they expect to get a direct answer, not a list of links to sort through. And when poor customer support is already costing brands $75 billion per year, failing to deliver answers to customer issues is a costly mistake.

This is where an AI Search platform that utilizes language automation shines. It has the ability to process large volumes of unstructured data, like product documentation and help articles, and deliver a precise answer to a customer's question.

AI Search also helps agents respond to inquiries more quickly by understanding the intent of a new ticket and automatically presenting the most relevant information from any knowledge source. This is a big improvement over an agent having to hunt for the many places help-related content exists.

And, in the near future, this language automation platform will even enable Support teams to auto-generate structured FAQs and knowledge articles from ticket responses, white papers, case studies, and even voice transcriptions. Finally, it will also present content and guidance to the customer as they move through their journey based on what's worked best for other customers in similar situations.

Consumers want more independence, and brands can only benefit from granting that freedom to address their support issues — as long as it's effective. Investing in AI search does just that, at a moment when support teams really need a hand.

On the consumer side, 62% of consumers say that they may purchase from a different company because a business couldn't answer their questions themselves. But here's the good news: 56% of consumers say that they'll return to the brand, versus a search engine, if the brand previously provided a direct answer to a question.

This "next wave" of advancement in customer support can help businesses show their customers that they can provide helpful, intuitive support experiences that understand their questions. In turn, they'll be more likely to stay in the ecosystem — and with the brand long term.