Online chat has become an indispensable part of our daily live. In 2018 merely 15% of customer interaction took place via machine learning applications, chatbots and mobile messaging. Research company Gartner is expecting that percentage to grow to 70% in 2022. Apart from that, 80% of the companies that participated in an Oracle research indicated that they prefer chatbots to answer customer questions online. There are several channels to use like WhatsApp and Facebook and Webchat on your website. It is very important to be present, wherever your customers are active online.
3 categories of online chats
Online chats can be roughly divided into three categories:
• Human chat – Communicate with a person directly
• Fully automated – Applications like Alexa and Siri, which are very good at performing specific tasks
• Hybrid – A combination of technology and human
It is quite easy for companies to engage in human-operated chats. All you need to do is hire a person or a company to handle your live chats. You will deliver a great customer experience because it’s a very personal approach. A human-operated chat is especially good to answer more complex questions or to respond to unsatisfied customers. However, using this kind of chat, the wait time for customers might be increased enormously and the costs for a company are often too high.
It’s not as easy to start fully automated chats, because an efficient algorithm must be developed. At the same time, the customer experience is not flawless, because it’s quite hard to reply to questions that are specific to a certain situation. However, the costs per chat are lower than for a live chat.
In the hybrid method, relatively simple questions will be answered by a chatbot, whereas more complicated questions will be transferred to a person. This results in lower costs than fully human-operated chats.
The best of two worlds
We believe that the hybrid chat will offer the best of two worlds. The so-called top questions will be answered by a chatbot. This will involve questions about the status of a delivery, return of goods, or the availability of a different size/color of a product.
If the questions are getting too complex for the chatbot to answer, for instance, if someone has difficulty purchasing online tickets, the conversations will be transferred to a person.
The hybrid chat method could also work the other way around. Suppose that you’re a customer in an e-commerce environment and you are engaged in a live chat with a person about a product that doesn’t meet your needs. If you both come to the conclusion that the product has to be returned, the conversation can be transferred to a chatbot to guide the customer through the return process. This will reduce the costs because there is no human assistance during this process.
Twice as many questions answered
One of the developments adding to more efficiency within organizations is that chatbots are able to reply to long-tail keywords. The computer searches the databank to find out what the appropriate follow-up question would be. The chatbot replies with that question to lighten the helpdesk’s workload and to make sure that the helpdesk will be better prepared for customers’ questions.
A good example is ANWB (Dutch roadside assistance). They have been using Iris since 2012 and the power of this virtual assistant became apparent in the summer of 2019. Before this busy period started, ANWB did some research on how to create interactions between Iris and customers that are as human as possible. As a result, Iris is asking more questions, is including positive affirmation in the replies, and is able to give relevant information. Accordingly, Iris answered twice as many questions about holiday preparations as in the previous years and the negative feedback has been halved to 6%.
The future of chatbots
Technology for chatbots is advancing at a high speed. Thanks to Artificial Intelligence and Machine Learning, chatbots are more and more capable of understanding language and sentiment and are consequently better at assisting people. A computer is already able to analyze sentiments in order to interpret the chat user’s mood. That way answering questions will not only be more efficient, there is also no need to ask the users how they felt about the conversation.
Another interesting development is conversational commerce, which includes instant payments in the chat. Suppose a customer wants to know if a certain product is available in his desired color. If that is the case, the chatbot can ask the customer directly if he would like to pay instantly. If so, the chatbot is able to redirect the customer to an integrated payment solution. Apple is using this technology in Apple Messages for Business. Their customers can make payments with Face ID. Google is using RCS (Rich Communication Services, also called SMS 2.0) and a Google Pay integration.
There is a lot of research on the predictive ability of machine learning to gain more insight into the people in a chat. Suppose someone uses the chat more than once to ask questions about a product, for instance, a certain shoe of a specific brand. If you know that person’s shoe size, you’ll be able to give personal advice as well as targeted offers like fall boots. Machine learning also works the other way around. If a person indicates that he’s not interested in promotions, no offers will be made. Personalization goes hand in hand with privacy.
No matter how the technique will continue to develop in the coming years, always make sure that you find the perfect balance between technology and human. This way, your users will be more satisfied with the communication and will have a more positive association with your brand, which increases the chance that they will come back more often.