According to SEO experts, companies should use full sentences in their website texts and put headlines in the form of questions that potential customers could ask and type into a search engine. This tip is not a surprise: intelligent search engines understand questions as well as complete sentences and deliver relevant results accordingly. Usually, the search engine recognizes what the user wants to know as soon as the first or second word is typed. Today, computers can be programmed to “predict” human intentions, decisions and the associated structures and to output precisely tailored data. And what’s more, computers are capable of learning. They remember previous search queries, decisions and patterns and can use them to optimize future results.
Bots, AI and human agents are important parts of the puzzle
This trend becomes visible in the chatbots, which currently seem omnipresent. On all kinds of websites we see small chat windows where avatars offer help. Behind these windows hide computer programs called chatbots. A chatbot is a software that can have text-based dialogues with people. Using keywords, word combinations and synonyms, it performs rule-based if-then operations. This means it delivers answers or content to specific keywords or word combinations that have previously been put in a library.
If the end of a dialogue path is reached, i.e. the chatbot no longer knows what to do, the software forwards the chat to a person. Or, the AI-based software can perform probability calculations based on predefined classifications, targeted queries to the chat user or keywords and word combinations in order to identify the user’s concern and provide a suitable answer. Here, too, the AI uses a previously defined data pool. And it learns: The validity of its probability calculations increases with the number of calculations.
Chatbot, AI and human agents – these three puzzle pieces in customer service are combined in the service channel “Chat”. Anyone who has ever used a chatbot knows that the technology works surprisingly well. For users, it is usually difficult to tell whether their request is being handled by a human agent or a machine.
Chat in customer service is already mainstream
Rule-based bots answer frequently asked questions or questions that are easy to grasp with rules. In retail, these are questions like “Where can I find short blue trousers for men?”, because the categories “short”, “blue”, “trousers” and “man” can simply be read from the product catalogue with the help of if-then questions. A second example from the retail industry is the question of the status of an order. Here, too, the necessary categories (e.g. the customer number) can be linked easily with a query from the merchandise management system and the corresponding result can be delivered.
Insurers, on the other hand, can implement completely chatbot-based damage reports, for example. All key data, including the insurance number, the date, the amount of costs and even pictures, can be captured just as easily with the help of a text-based dialogue as by telephone.
For companies, shifting simple customer enquiries from expensive communication channels such as the telephone to a cheaper channel (the bot) offers considerable savings potential. If we assume, for example, in the case of insurance that an agent takes an average of ten minutes to record a damage report by telephone and that the price per minute for a query of this quality is around 1.50 euros, the costs amount to around 15 euros per contact. The opportunity costs for the possibly frustrating waiting time for the customer in the waiting loop are not yet included. The cost of working time alone, which is required to record the claim in the system, can be reduced significantly with the help of bots.
Human agents and bots work closely together
Classification, on the other hand, is used for concerns whose direction cannot be estimated precisely and in areas where the product range is changing constantly. The identification of assets in insurance companies is a typical case. The customer must first be assigned to a specific department, such as life, health or motor insurance. A comparable application case from the e-commerce sector would be the question “What are you looking for?” Here, the customer must first be assigned to a product group. If, for example, he is looking for a new vacuum cleaner, it can be operated with a cable or battery, have a bag or not, be from a certain manufacturer or the customer has concrete price expectations.
In both cases, the bot uses confidence queries to identify the right request or product. The so-called hybrid model, i.e. the combination of man and machine, is often used here. If the software is not able to assign the request without any doubt, the chat is given to a person. The agent can then, depending on the requirements, freely continue the chat or fall back on preconfigured answers.
Even with the more complex classification scheme, working time is to be shifted to a more cost-effective communication channel and customer communication is to be made more efficient. The software finds short blue trousers for men sometimes much faster than a human agent.
Chatbots change the Contact Center
The integration of chatbots offers not only economic advantages, but also plays into the hands of consumers and thus has a positive effect on the company’s image. The goal is to stay attractive for target groups that are online affine and experienced. Customers are looking for a simple way to communicate and get quick answers 24/7. This is shown in the “2018 State of Chatbots Report”, for which several companies surveyed a representative sample of the US population. According to this study, the points “round-the-clock service”, “getting an answer quickly”, “getting answers to simple questions” and “a simple means of communication” are at the top of the list of advantages of a chatbot from the user’s point of view.
From a business perspective, customer care must become more cost-effective and efficient. Customers want to use simpler and faster communication channels and be better served at the same time. We are at the beginning of a major development that will transform customer service and the Contact Center. It is possible that Contact Centers that route to human agents will be completely replaced in the long run. Especially regarding AI and machine learning as well as the use of incoming data for analysis, all possibilities have not been fully explored yet.