The Automation Of Customer Relations Through AI

Last updated on May 29th, 2020 at 10:23 am

Last November, during the Web Summit in Lisbon, David Marcus, VP of Messaging Products at Facebook, asked the following question: Who wants to call a brand?

He was referring to the ordeal of having to get in touch with a customer service apparatus. Very often, this process is time consuming, stressful, and leaves the customer frustrated, an extremely negative outcome for any brand.

The current development in AI and machine learning has the potential to solve this situation by bringing a pleasant, time-efficient experience for customers, and efficiency and precious data to the brand.

Automated customer service

Brands need to be more accessible. Increasingly, community managers end up doing customer service nowadays. Whether on Twitter or Facebook, they are often contacted by angry or frustrated clients looking for help or searching for a solution.

Brands and their customer service teams need to adapt to those new customer behaviors. Right now, contacting any brand is nerve-racking. Whether it is a travel agency, tech support, or an Internet access provider, people dread the call. They usually postpone it until they have no other possible option.

Imagine if the experience was as simple as contacting a friend on Messenger. No app to download, no waiting on the phone, just a series of quick interactions with a bot that understands what you want to know.

2016 saw the birth of a major Chatbot trend, but 2017 will see the first useful ones. These bots will be able to handle customer interactions, answer frequently asked questions, and, most importantly, transfer the client to a qualified human if necessary.

Because bots can’t replace humans.

Customer service questions follow the Pareto principle: 20% of the questions make 80% of traffic. A major part of these recurring questions could be answered by an intelligently trained chatbot. This is viable with no downside in terms of customer experience, only if there is a cooperation between the bot and human agents from the customer service team, who can take charge whenever the bot is taken aback.

Furthermore, a customer should always be able to choose if he wants to talk to a member of the team or a bot.

24/7 Client acquisition

Generating commercial leads is a crucial aspect of any business. Allowing a potential customer to slip through your fingers costs money. Given that visitors on a website are all potential customers, an increasing number of businesses are now implementing live chat software like Intercom to respond to visitors’ inquiries.

Many of the questions people ask on a website’s live chat are recurring. It is therefore possible to predict them and prepare an adequate response. In any case, installing a live chat should have two main objectives:

Visitors should leave the website satisfied with the information they obtained;
Visitors should be able to leave their contact information like an email.

An automated live chat can accomplish these two crucial tasks very efficiently if it is built with the right technology. In the midst of the chatbot trend, many people are curious about interacting with bots and are impressed when that experience goes well.

So, how does AI do that exactly?

As it has been for about a decade, the answer comes from data. By processing large volumes of data, algorithms can now recognize what we call Intents: the meaning behind each question, whichever way it was phrased.

The key is to adopt a specialist approach, build bots whose objective it is to answer questions about a specific topic or market. AI technology can identify practically all Intents (what the clients want), in that domain, and push the right answers to the chatbot so that it will be able to answer every question that may be asked. Machine learning techniques can single out user questions that were not answered correctly and compute them once again in order to avoid the same mistakes further down the road. Thus, AI-powered chatbots get better over time.

Access to conversation data sets is key in order to perform semantic analysis, and extract historical user questions that are the backbone of natural language understanding. By analyzing a given semantic field, one can collect and categorize almost every possible question people have asked and teach them to chatbots.

Conversational agents were a much-discussed topic during 2016. The moment Marc Zuckerberg launched Bots for Messenger in April, a chatbot fever got underway. Three months later, more than 11,000 bots had been created on Facebook Messenger alone.

To use a bot as an acquisition or customer service front, businesses need to implement a mix of AI-powered chatbots or live chats and good-old human agents. With natural language understanding AI ensures that bots can answer often-asked questions correctly, and with customer service agents handling more complex situations, businesses can both safeguard user experience and improve the bottom line.

Feature image source: IPG Media Lab