Artificial Intelligence in customer service — the complete guide

Artificial intelligence (AI) : When talking about AI in the original meaning, it referes to machines that are capable of imitating ‘intelligent’ behaviour. This includes performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and translation of different languages. AI is the generic term for this kind of intelligent machine.

As an example, let’s look at a chatbot in a customer service setting. When a chatbot answers customer questions, it imitates human behaviour and we can therefore call it AI.

However, this generic term doesn’t cover everything about AI and is a bit deeper. Here’s some of the most important ones:

Machine learning (ML): If you’ve ever used Spotify, YouTube or Netflix, then you’re already familiar with personalised recommendations. These kinds of platforms use algorithms that can analyse data, learn from it, and make predictions and classifications about what you also might like. This is classic machine learning. In our chatbot example, an algorithm based on machine learning would analyse the customer question, compare it with past enquiries and successful answers, and select the best answer from the options it finds.

Based on customer feedback (for instance, “this answered my question/this did not answer my question”), the machine knows whether or not it has done a good job. If it gives an unusable answer, it forwards the question to a human colleague. Ideally, it would also track the human employee’s answer and learn from it to find its own solution to similar questions in the future.

Deep Learning (DL): Deep Learning is a more advanced version of machine learning (ML). It allows machines to make more precise predictions – without human help. Deep learning applications use a layered algorithm structure (Artificial Neuronal Network) to be able to draw conclusions, similar to the human brain. Instead of basing the answer on the retrieval of past successful answers, the chatbot can create its own answers and exchange individual messages with the customer. This requires a much larger database in contrast to the simple machine learning approaches. With enough data, Deep Learning can do impressive things.

Natural Language Processing (NLP): The way people talk to each other, via speech and text, is called natural language. Natural language processing is a technology that helps computers to understand our natural language. In our example of a customer enquiry, NLP would allow the chatbot to translate the question into computer language (commands) and formulate the output (the answer) using meaningful human terms.

This is because NLP breaks down the language into chunks to understand how each of these pieces work together. If you had to colour-code different parts of a sentence in primary school, you would know the process. The goal of NLP is to take raw language data and use linguistics and algorithms to decode text and meaning so that meaningful results can be achieved. For example, if you are scrolling through your spam folder and notice that many subject lines follow the same pattern, NLP is probably responsible for that. It identifies certain words as spam content to decide whether the email ends up in the inbox or trash.

Technology has developed significantly in recent years, as can be seen from applications such as Siri and Alexa.

Answering Frequently Asked Questions

In this day and age, customers expect service to be fast. To take the pressure off your employees, you can use a chatbot to answer the most common questions, or to create an interactive FAQ page. This will not only takes the pressure off your service team, but also allow your customers to help themselves through the FAQ page. The AI helps to improve the users self-service experience by identifying keywords and finding suitable answers in the knowledge base. While the customer is still typing in their query, the system can recommend relevant pages and makes suggestions based on the customers questions.

Chatbots and FAQ pages can also help you identify popular search terms, making you more aware of what your customers are searching for more often. Some topics or questions might need a dedicated page on your website?

Automate text suggestions and messages

An AI-based system helps you to create and send automated responses to simple questions. AI chatbots can arrange meetings and send reminders in an almost human way. They can create subject lines and messages that are targeted to a specific audience by analysing the success of past word combinations.

They can also help create social media messages and gauge their success before they are even sent. To stay active outside of service hours, AI can send instant responses or direct your customers to live chat, your FAQ or contact page.

There are many immature solutions. Cheap ‘out-of-the-box’ chatbots allow companies, large or small, to create a bot with just a few clicks, automate ticket creation and send emails on behalf of your company. These systems are often sold as ‘intelligent’ or ‘smart’, even though they require a lot of manual labour. Data feeding and training can take months, perhaps even years. And errors are practically pre-programmed until the maturity phase. Are your customers patient enough to deal with this?

When selecting a provider for support automation, it’s important to make sure they integrate basic AI functions and meet the expectations of their customers – within a few weeks rather than months or years. Belive it or nord, but good AI can be expensive (but it doesn’t have to be). Building your own intelligent platform can be expensive. It’s like trying to build your own PC instead of buying one off the shelf – nice, but not necessarily worth the investment!

Advanced solutions, such as Lime Connects AI Automation Hub with GPT-4 chatbot, are available to companies for a reasonable starting price. This means they only cost a fraction of what a self-built solution would cost and can deliver a direct ROI.