Creating better ways to serve customers is a priority for companies. With technology comes the opportunity to innovate and improve the customer experience. One of the most vital tools is AI-powered customer service platforms. They enable real-time customer interactions, often without the need to create a ticket.
Most people have no hesitancy about using self-service options while on a website, but their experiences vary depending on the type of technology. There’s a critical difference between a standard chatbot and conversational AI. It’s all about how they interpret the query and learn from interactions.
Conversational AI combines natural language processing, natural language understanding, and machine learning. With these components, you can delight and satisfy customers, delivering a positive experience.
But how does conversational AI leverage machine learning to get better at responding to customer questions?
1. Machine learning understands intent and sentiment, resulting in greater response accuracy.
With machine learning in your self-service system, the tool can understand intent and sentiment in human language. Traditional chatbots don’t use machine learning. Rather, they only focus on the keywords in a customer’s question. This narrow interpretation often doesn’t resolve the issue. That type of application operates on a set of rules. It cannot learn how to interact with customers more effectively.
Conversational AI can deliver more accurate answers with the ability to comprehend the actual meaning behind the question. Sentiment and intent are the foundation of human language and are crucial to interpreting what a customer actually needs.
Consider all the words and phrases we use that mean the same thing. There are so many nuances to language. Machine learning broadens a tool’s ability to be helpful, which means quicker answers for customers and fewer tickets for your agents.
For example, an insurance company has many types of plans. Often, people who visit a website want to compare these. That individual may engage with self-service and ask, “What are the deductibles for each plan?”
A chatbot would only extract deductibles from the sentence and may return a result explaining what a deductible is. That doesn’t answer the question! Conversational AI understands that the user wants to know about specific deductibles and would provide the user with a comparison page. It’s an entirely different experience, with the latter being a much more positive one than the former.
2. Machine learning helps companies identify new common questions.
Your knowledge base is a library of resources open to customers. The effectiveness and breadth of your knowledge base directly correlate to how many support tickets you receive.
Conversational AI-powered platforms that use machine learning are an excellent asset for finding new information to add to the knowledge base. The ticket tagging feature, which you can customize the parameters of, generates valuable data.
In this analysis, you may identify topic patterns that don’t currently exist in your knowledge base. You can then queue those new topics to your product or content teams for inclusion in your knowledge base.
3. Machine learning supports smart routing.
Not every question is resolvable through self-service. Customers will still create tickets that end up in your agents’ chat queues.
The handoff between self-service and agent chat needs to be smooth. Customers are already cognizant of the time it takes, and they have high expectations for fast resolutions. Machine learning plays a role here as well with smart routing. It classifies the urgency of the request and can also identify if it’s spam.
Since the technology analyzes the entire question—structure, meaning, tone, and so forth, triage is much more seamless. It tags the ticket, prioritizes it, and sends it to the team that can best help.
4. Machine learning continues to be an asset by assisting agents.
After the ticket lands in the correct chat queue, machine learning is still working behind the scenes to assist agents. While your agents have a wealth of experience and expertise, they can also use some supplemental support in the form of machine learning.
The conversational AI engine supports agents by immediately displaying the latest macros, knowledge base articles, and tickets relating to the topic. It’s constantly learning as agents upvote and downvote answers. That feedback improves the performance of the technology in resolving customer queries quickly. That matters significantly to customers in how they grade an experience.
Machine learning is critical to enhancing customer experiences.
Technology should consistently deliver value. In the case of customer support, you want it to streamline and improve experiences. One of the best investments you can make is conversational AI tools that include machine learning. They are powerful components to ensuring customer experiences are memorable in the best way.