StartInsightsArticlesAI in customer service — the complete guide

Artificial Intelligence in customer service — the complete guide



During the past decade there has been technological progress and development in the areas of neuronal networks, machine learning, deep learning and linguistic data processing. Digital assistants now live in every smartphone and businesses are very interested in using chatbots. AI is also promising for customer service. But does it meet expectations?

How realistic is it for you to be able to base your customer service on AI and what can be expected in the near future?
Before we dive into the field of AI based customer service, let us explain the most important terms and concepts, that AI experts love to throw around.

What is AI, ML, DL and NLP?

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:

Narrow vs General AI

AI can be divided into two types: Narrow AI and General AI.

Narrow AI is programmed to do a specific task by using information from a special data set. All AI applications that are being used currently are categorized as narrow AI.

General AI is characterized by human intelligence and could do any task that a human could do, and even more! Right now, this is still something that’s science fiction, and HAL 9000 is a great example of a general AI (taken from the movie 2001 – A Space Odessey). It can learn, plan, weigh up, communicate in natural language and can use these skills for any task, just like a human would.

In our previous example, where a customer sends a question to a chatbot, a narrow AI would check the customer’s question against an FAQ database and find possible solutions from this database. A general AI would instead give personalised, human sounding answers instantly. It could request missing details, scan the internet for relevant background information, and customize the response to the customer within seconds.

Other examples of Narrow AI

“Fixed” AI: Fixed AI is a version of narrow AI that’s rule-based (pre-programmed). They don’t learn from their interactions and work according to predefined decision paths.

A “fixed” AI chatbot can ask your customers multiple-choice questions and then offer answers or trigger actions, such as forwarding a chat to a human service employee, depending on the rules it knows. Everything is based on a selected route that’s already pre-programmed.

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.

The advantage of chatbots that work according to this retrieval-based principle, is that the answers are relatively reliable because the chatbot only provides answers that it knows have been good historically. On the other hand, they can only deal with simple, direct questions. Anything out of the ordinary where it doesn’t have a previous answer becomes a challenge.

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.

The practical chatbot guide for companies

Learn how chatbots work, what they can do for you, how to create one – and whether bots will steal our jobs.

How to use AI successfully in your company

AI can make a big difference in customer service. When implemented and utilised correctly, intelligent systems can enhance your company’s image, relieve your service team and reduce support costs by up to 30%.

These are the best ways to use AI in your organisation.

AI in customer service

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.

Complete recurring tasks

Artificial intelligence can step in for all tasks that are either too boring or time-consuming. Chatbots in particular are true everyday heroes. In addition to answering general questions, chatbots can distribute tickets to employees, forward messages, update customer profiles and suggest suitable products.

Intelligent AI chatbots, such as those from Lime Connect, train themselves almost automatically. Create a central knowledge database, enter basic information and let the chatbot learn with every enquiry thanks to deep learning algorithms and GPT 4 integration.

If a customer asks more complex questions or has enquiries that go beyond the chatbot’s knowledge, the AI chatbot simply forwards the conversation to an available team member.

AI in Marketing

Analyse customer sentiment

Live chat conversations, social media interactions, and review platforms, tell you a lot about what people think about your brand. Machine intelligence helps you to analyse all of this.

In social media, it is often particularly difficult to understand customer sentiment as it is conveyed in unstructured comments and messages. How does AI help with this? It can determines customer sentiment by analysing trends and measuring messaging trends, patterns and word choice. If you are planning to create a Customer Health Score, customer sentiment analysis can be incredibly valuable for prioritising at-risk customers or uncovering upselling potential.

Lead qualification

AI software can help your customer service team focus on qualified leads by assigning scores to different prospects. It can analyse the potential customer risks and behaviours for your team to drive trial closes and sales.

This kind of AI can learn enough about your customer segment to create a customer health score. This saves your team the time of analysing various metrics themselves, which can be a long process.

Does AI-supported customer service also have disadvantages?

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.


How to get started with artificial intelligence in customer service

An “all-in-one” customer messaging and support automation software, like Lime Connect, provides you with an AI toolkit that allows you to build a self-learning knowledge base in just a few steps. The three AI modules of Lime Connects AI Automation Hub enable 24/7 self-support for your website: An AI chatbot with GPT-4, Smart FAQ and dynamic contact forms.

Lime Connects GPT-4 chatbot uses the most advanced AI technology in the world, and is therefore able to creatively combine entries from your knowledge database and provide customers with completely personalised answers. The support bot even remembers the context of the chat so that it can categorise follow-up questions correctly. Another special feature is that it can answer several questions in one message. In this case, the bot recognises that different topics are involved, searches the database for the necessary information and answers both questions in one coherent message. This doesn’t just increase your resolution rate, but also gives your customers a more natural chat experience.

Smart FAQ is a responsive self-service portal that helps customers solve their problems themselves. The AI-based autocomplete attempts to answer the user’s question as they type. It lists popular topics at the top, so users can quickly find what they’re looking for.

Lime Connects contact form enhance your existing contact form with a dynamic suggestion function. The AI attempts to answer the customer’s question based on their input, even before they submit the form. As with Smart FAQ, the answer is suggested to the user as they type. Every customer chat and every interaction feeds your AI with knowledge and makes it continuously stronger.

Lime Connect has a free 14-day trial period, so you can get a feel for their customer messaging platform and AI Automation Hub. Once you’re satisfied, they’ll be happy to help you get ready to start the journey to AI in customer service!

What are you waiting for?

There’s no time to waste! Let’s find the solution that will help you get more customers and turn existing ones into loyal ambassadors today.