How integrating a chatbot into a call centre can increase its efficiency by up to 85%

Editor's Note: This is a guest article written by Ilya Smirnov, Head of the AI / ML Department at Usetech.

As you know, Artificial Intelligence is widely penetrating into all areas of human life. Generative Artificial Intelligence is proof of this. More and more companies around the world are investing in implementing Artificial Intelligence-based mechanisms and platforms.

And this is not just for a reason: generative technologies can improve business efficiency and increase response rates. In addition, according to statistics and numerous studies, Artificial Intelligence continues to evolve and increase the pace of development.

So, why do call centers need to implement Artificial Intelligence?

We often think of call centres as huge halls where operators answer customer inquiries. But rarely does anyone think about the fact that people working in call centres are faced with a large amount of information, which requires huge resources to process. For example: 

• Operator labor
• Equipment costs
• Costs of electricity, room rent, amortization, etc.

Modern technologies of machine learning and artificial intelligence help to significantly reduce the costs of providing these services, as well as increase the efficiency of the centres.

Practice shows that about 70% of requests coming to the hotline are of the same type. Currently, robotic services of the so-called “first line” of support effectively distinguish these requests. In order to provide high-quality services, they only need to recognize the subject of the request and ask the client a few clarifying questions. This allows the company to clearly, quickly and unambiguously satisfy the customer's request.

It is worth noting that a client's request can come in various forms and through various communication channels — messengers, chatbots, voice assistants or an operator working according to a certain algorithm. And in all these cases, machine learning technologies come to the rescue. They allow for determining the most appropriate “mask” of questions and give a more accurate answer to customers.

The most primitive robotic systems are linear chatbots.

We see them in messengers, social networks, mobile apps and websites. The listed chatbots are unlearnable and work according to a certain script. But they are useful — with their help you can order food delivery at home or book a place in a restaurant, specify the cost of sending a parcel or make an appointment at the clinic. At the same time, the average time of request processing will be reduced by about 3 times. These bots allow you to maintain customer loyalty — it is known that more than 50% of people would prefer to solve issues without communicating with people, and prompt and more detailed information undoubtedly leads to an increase in sales.

As an example, let's describe a scenario of working with a chatbot of a company engaged in international delivery.

Not so long ago, there was a need to send a shipment from country #1 to country #2. We went to the company's website and talked to the chatbot. It turned out that due to the non-standard size of the cargo, it was necessary to specify the parameters: length, width, and height of the cargo. As it turned out, a couple of centimetres significantly depended on the number of places in the container, for which it was necessary to pay.

After making a couple more calls, about 20 minutes later, we successfully answered all the questions. We were then switched to an operator. We successfully confirmed all the transferred parameters of the shipment, and he placed the order for delivery in 5 minutes. Thus, the time costs were as follows: about 30 minutes for the chatbot and 5 minutes for live communication with the operator. The efficiency of such a chatbot turned out to be 85%.

Currently, quite a few programs have been developed that allow you to quickly create, implement and configure a simple linear chatbot. The low cost and simplicity of development have made it possible to implement them almost everywhere. Now this service is actively used by clients of telecom operators, banks, insurance companies, delivery, government services, and tourism. Linear robotic systems not only respond to customer requests but also make the distribution of the most relevant offers. They can also provide media files as a response to a request. Also, such chatbots easily form a database of requests, which allows them to respond quickly to changing customer needs.

Will modern technologies based on Machine Learning be able to fully replace call centre operators?

We can already say unequivocally that no. Only a human can answer non-standard complex questions, provide the necessary psychological support, and give an emotional color to a conversation.

Nevertheless, initially received information about the topic of the conversation, collected client data and received answers can allow unnoticeably for the client to transfer the work with him from the machine to a person.

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