As of last year, four out of every five enterprises had adopted machine learning and other types of artificial intelligence (AI) within their core business models. That statistic is made all the more remarkable by the fact that five years ago, this was the case at only 1 in 10 large organizations.
However, this rapid adoption comes with a flipside: Too many businesses today — in the race to adapt their customer experiences for the age of automation — have powered AI programs that have gone nowhere. This is especially the case in the realm of natural language understanding (NLU), where overly simplistic executions fail to capture the nuance needed to serve real human needs.
For organizations looking to deliver on the full promise of NLU within their customer experiences, start with these five key ingredients:
1. Intent Classification
At its core, good NLU solutions do more than digest words and derive possible responses. The best NLU emphasizes the N — natural. What are the natural layers to any conversation, and how can a technology go about decoding the many layers that extend beyond the words themselves? In any conversation, it all starts with intent. What purpose — or purposes — brought the person to the conversation in the first place?
People can walk into a conversation with any number of intentions, and their intentions aren’t always linear. A classifier is an algorithm that absorbs information about a person and seeks to categorize their needs — and needs are often multifaceted. To understand the intent of customer questions with high precision, successful deployment of NLU may require multiple AI classifiers at the same time. The combination of classifiers used should be selected with a company’s use cases in mind, as every organization’s needs are slightly different.
2. Semantic Search
As experts are quick to note, strong AI models shouldn’t require customers to use perfect grammar and full thoughts in order to understand what that person wants. Think of semantic search as “search with meaning.” NLU that employs semantic search does more than just match words and variants of them. It searches for the overall meaning of the query, rather than just its components. This is absolutely essential when it comes to handling curveball questions, which are an inevitable part of customer conversations.
Recent innovation within the NLU space has focused heavily on increasing the ability of systems to understand different speaking styles and patterns, and these enhanced capabilities are expanding the use cases in which NLU can be applied. Today, when semantic knowledge graphs are implemented alongside intent classifiers, companies can tackle a broader array of questions with a broader array of customers through their AI tools. Overall, when compared to the results seen with basic chatbots with surface-level functionality, employing these upgraded capabilities translates to fewer interventions by live agents and overall higher customer satisfaction rates.
3. Tone Analysis
Are your customers feeling content or frustrated? When it comes to delivering a good customer experience, mood matters tremendously. Tone analysis enables AI to interpret a customer’s feelings going into an interaction and serve appropriate responses based on those feelings. For example, a frustrated customer is typically hoping a company can solve a problem as quickly as possible. On the other hand, a happy customer might be willing to spend more time in a conversation and could be interested in upsell offers. Good NLU needs to be able to make that distinction and adjust on the fly. With tone analysis, you don’t have to sacrifice an empathetic, personalized brand voice in order to deliver an automated, conversational experience.
4. Sentiment Analysis
Sentiment analysis goes hand in hand with tone analysis, but it’s worthy of distinction here because of the unique depth it can bring to an automated conversation. In short, sentiment analysis allows for an even more granular look into your customer’s feelings. It can identify the difference between a content customer and an ecstatic one, not to mention the difference between a discontent customer and an enraged one. These distinctions place a customer on the spectrum within a given tone and provide all the more guidance for how to best respond.
5. Emoji Analysis
It might be jarring to suddenly hear emojis — which are often discussed with a level of frivolity –come up in a serious discussion of NLU considerations. But believe it or not, the ability for an automated system to understand the surprising complexity of this pervasive form of communication can be what makes or breaks the fluidity of the customer experience. Emojis are a prime example of modern, informal natural language use. By employing emoji analysis, NLU makes communication easier and more comfortable for customers. Combining emojis with an understanding of tone and sentiment further allows automation to adapt to a customer’s mood.
Taken together, these five elements represent key ingredients for an NLU solution that is capable of heavy lifting within the customer experience. Without these ingredients, AI can drop the ball for your customers, and the bad taste it leaves in their mouths will be inextricably linked with your brand. With customer-facing AI, a person’s experience is far too important to deliver only partial solutions. Don’t settle for less.