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You're getting back into the swing of things after the summer break, and when you look back over the last few months, all you see is prompt; the GPT Revolution, chatbots, voice bots with immediate or future repercussions for your customer relationship center.
That's all we've talked about, you've seen and heard a lot... What's left? Let's take a closer look at how thought trees work in customer relations, or how to divide up the question to answer it better!
Pierre-Eric, before enlightening us on the how, can you briefly explain why artificial intelligence, and particularly chatbots, are increasingly favored in customer relations?
Let's start with the initial need. Customer services are generally overwhelmed with requests, often iterative, due to the wide variety of channels open for customer engagement (from voice to digital and increasingly numerous messaging channels). Processing all these interactions is expensive and mobilizes resources while often, the answer is already somewhere on the website or customer space.
Companies then strive to make the customer as autonomous as possible by offering them the ability to access the information they need on their chosen channel, without the help of an advisor. In this sense, chatbots have quickly proven to be good solutions for addressing this issue, and according to the 2023 CX Trends study by Zendesk, thanks to recent AI advancements, 72% of business leaders plan to use more artificial intelligence in customer relations and experience this year.
Indeed, with the advent of generative AI, chatbots for customer relations respond much better and can be implemented quickly, at a lower cost. The icing on the cake, customers are fans: nearly 75% expect great evolutions for better service (Zendesk CX Trends 2023) But it's not all so simple! The LLM models used by generative AI can make mistakes (they have hallucinations, it is said), and it's not enough to just plug GPT into your knowledge base to improvise as a virtual advisor in a large company, administration, or very active e-commerce.
Time to Open the Hood, I Understand! How Does a Good Chatbot for Customer Relations Work? How Does AI Analyze and Interpret the Question?
1. A good chatbot for customer relations knows how to recognize contact motifs and differentiate:
2. It is based on a broad industry-specific dataset as training data.
Yes, a good chatbot for customer relations is one trained on a very broad industry-specific dataset. By that, I mean that if you rely on generative AI and your knowledge base to respond to your customers, you might end up with an unsatisfactory understanding rate. The unique reference framework of intentions and solutions for customer relations that we have built over time allows the chatbot to be more efficient and, above all, to control and monitor the response provided.
3. It can precisely qualify the request.
Indeed, because this chatbot is trained for customer relations and has a clear view of the typologies of intentions and associated solutions, it is capable of qualifying the user's request with precision. It can therefore ask the user for additional information, so as to remove any ambiguity from the request.
4. It has a rich content database of responses to feed generative responses.
Besides the customer relations data ingested into our customer relations repository, the chatbot for customer relations must be able to refer to a wide database specific to the company (website, contracts, general conditions, FAQ, customer space, etc.) It must be able to understand the data available to it and sort between data that can be useful for the user's expectations and outdated or inappropriate data.
5. It doesn't hallucinate during responses and says it doesn't know when it doesn't know.
Of course, hallucinations are controlled, and responses are monitored. A good customer relations bot must be finely tuned on a quality database to achieve good results.
6. The cost of the request is controlled thanks to prompt settings.
It's a point that is rarely discussed but essential! Indeed, if you want to control the operating costs of the chatbot, it's better to have, in advance:
