Take a quick tour of the AI technologies behind CX interactions—and learn what they could mean for you.
Conversations about AI are pervasive in modern society and we’re in no doubt that it’s set to fundamentally change the way the world works on several levels. We’ve already covered some of these advances and what they mean for the year ahead in our 2024 prediction blog. In this one, we’re breaking down different types of AI with use-case examples for CX.
The customer experience (CX) is an integral part of creating brand loyalty and ensuring customer satisfaction, and there are a host of AI capabilities that can help you provide the frictionless, conversational, anytime-anywhere messaging services today’s customers crave. And the CX benefits only grow as AI technologies become more powerful, accessible, and cost-effective.
However, understanding the difference between similar technologies and terminologies and knowing which is right for you can be a challenge. So, here’s a quick guide to the capabilities supporting AI-driven CX.
Since its inception in the 1940s, when it was developed as a means of translating languages after World War 2, Natural Language Processing (NLP) has come a long way.
Today, NLP is the term used to describe an AI’s ability to understand and interpret written and spoken human language in much the same way you and I do, extracting meaning and context and making appropriate decisions as a result.
The technology is built on a foundation of NLU (Natural Language Understanding) and uses a technique called parsing to dissect sentences into different elements that make it easier for machines to understand. It then uses semantic analysis to identify relationships between words and provide vital context.
The strength of NLP is in understanding the written word, which makes it particularly useful for tasks like sentiment analysis and translation. Even in its most basic form. the use cases for it are vast, varied, and ever-expanding. It can be used for speech recognition, spellchecking, and summarizing tasks among many other things.
Where NLP really comes to its own though, is when NLU is combined with NLG (Natural Language Generation) to respond to prompts in a conversational way, which leads us to…
One of the more common uses of more advanced Natural Language Processing is in chatbots, which use NLP and other AI technologies to simulate full conversations with users. .
Here, NLP can help to accurately identify customer intent and route to the right solutions, reducing agent workloads and support costs while improving accuracy. By taking on a percentage of this time-consuming workload, NLP can also help you reduce wait times and customer frustration, resulting in greater satisfaction and a big boost for your brand.
Chatbots can be rule-based, delivering set answers from prompts, but they can also incorporate machine learning algorithms that enable them to respond to user prompts using more natural language. When at their best, this can lead to authentic and seamless interactions.
You’ll most often see chatbots deployed as virtual assistants or, in the world of CX, automated customer support agents that provide instant responses to queries. Recent statistics show that 37% of companies with chatbots use them to provide user support, 62.5% use them to qualify leads, and 25% use them to recommend products. In each of these use cases, they also act as a valuable tool for data collection.
One way you may consider deploying chatbots is as part of your customer onboarding experience. Research shows that more than 90% of customers feel the companies they buy from can do a better job onboarding new customers, and chatbots offer a great way of bringing new clients up to speed, providing answers to common questions, addressing concerns, and educating people about your offering.
Large Language Models (LLM) are computing systems inspired by the human brain that are designed to incorporate massive datasets to perform a variety of NLP tasks.
Whereas a chatbot may be designed to perform a specific function, an LLM can understand and generate human-like responses across a wide range of topics—but they can also be fine-tuned for specific tasks. It is the closest technology has come to mimicking the human mind, and these models can even be capable of self-learning.
ChatGPT and Gemini are both well-known examples of large language models that have taken the world by storm in recent times, but there are plenty to choose from and the implications for customer experiences are huge. Not only can these advanced models instantly generate contextually relevant and personalized responses to conversational prompts, they can also easily summarize long-form text to simplify support agent handovers.
LLM can also be used for lots of use cases you might not be aware of. For instance, our ‘evaluate’ node in Webex Connect allows users to describe the functions they want to perform and then have generative AI interpret those instructions and produce the required code. This means that users who aren’t IT specialists can create, update, and iterate user journeys without support, which reduces the strain on busy IT teams.
In the realm of customer interactions, LLMs allow routine service tasks to be automated by providing instant, relevant answers to customers. And this means human agents can focus on more complex and intricate interactions. However, programs like ChatGPT do have their limitations. For instance, in pulling information from the internet, they can be culpable of sharing information from incorrect sources. You can take a deep dive into this topic in our ChatGPT blog.
Limitations aside, the core of this technology has the power to revolutionize customer experiences. But it requires a certain amount of expertise and training to integrate these technologies into an existing communications infrastructure. A forward-thinking, AI-aware Communications Platform as a Service (CPaaS) can help with this.
AI has the potential to transform service interactions for good, but understanding what solutions are best suited to your specific needs and knowing how to integrate them can be complicated.
To learn more about how you can bring automation with a human touch to your customer experiences, visit our CPaaS solutions page or speak to one of our experts.
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