The stories of how robots and artificial intelligence are conquering the world and displacing workers from their jobs, for some, seem like a nightmare, descended from the pages of science fiction. However, if you look closely, AI has long been firmly embedded in our lives. We give no notice that voice assistants, face recognition systems, and smart refrigerators have already become the daily reality. But it’s not only about smart robotic systems for IT giants. Businesses include its implementation into their tactics in many ways. For instance, AI supports Magento PWA development, making progressive web apps fast, charitable, and frictionless.
Also, a technological breakthrough known as the disruption of artificial intelligence has given powerful momentum to the customer service industry. This article dwells on the essence of artificial intelligence: what its role in chatbots is and how conversational AI forms the technical support agenda.
AI embraces several technologies such as natural language processing, deep learning, machine learning, and machine perception. It aims to mimic human interaction as much as possible. In other words, software/hardware perceives the current context and is able to make decisions based on both previous experience and understanding.
It is strikingly incredible that AI has been so successful in copying human behavior that 40% of consumers find a computer as acceptable as a human being. The job of customer support agents leverages the two innovative capabilities such as machine learning and natural language processing (NLP).
Machine learning involves a powerful computing system that processes large amounts of data to learn from it automatically without human intervention and adjust actions accordingly. The AI machine learning process finds its way in Facebook Messenger, request suggestions, and spam folders.
Natural language processing aims to read, decipher, understand, and comprehend a person’s oral/written messages. Siri, Cortana, Alexa work on this technology.
Let’s take a look at a feature provided by Uber based on the Michelangelo machine learning platform. The system offers the most relevant responses to common passenger messages. This allows drivers to respond to messages with a single click in the app, making it easier to do this in a driving environment. You can see on the screenshot how NLP facilitates swift communication between drivers and passengers.
Image credit: Uber
Rule-based chatbots have existed on platforms such as Kik, Line, and Telegram. Following the trend, Facebook also launched the Messenger platform to allow companies to provide automated, rule-based customer support via chatbots.
These chatbots already have a set of predefined answer options, from which users need to select the one corresponding to their requests. This version of chatbots does not involve complex questions and answers. They can be useful, for example, when it comes to booking a table in a restaurant, buying movie tickets, or using online delivery services. Most often, they suggest that the user clicks a specific button to activate further interaction. In other cases, they use a keyword search, which makes them vulnerable to typos and fuzzy phrasing. This often leads to customer disappointment, and either a failed communication or contacting a live operator.
In turn, AI chatbots rely on machine learning models that greatly enhance the bot’s functionality. The conversational bot identifies the intent and understands the meaning behind the question without any human assistance. In doing so, it really performs a task for the customer, rather than just provides a link to self-service instructions. In this case, users can communicate with the AI bot in the same way as if they were communicating with a live agent.
The screenshot below shows the conversational AI chatbot of the Open City restaurant. As we can see, the chatbot not only answers the most frequent questions, but also emulates natural human behavior.
Image credit: Medium
AI chatbots can automate a bigger number of questions, require less data for training, and can solve more complicated issues compared to the rule-based chatbots. Improved NLP algorithms allow the conversational bot to achieve 92% text classification accuracy, confirming intent with the customer. If a prospect asks a question and the conversational robot does not have a high enough confidence level to predict the customer’s intent, the bot asks a specific question.
Here are the 5 reasons why businesses turn to AI chatbots in a customer service routine.
Availability throughout the day, on weekends and holidays. An AI chatbot is able to quickly answer simple questions, regardless of the time of day. This is a sure way to customer satisfaction with no extra waiting time involved, improved commitment level, and enhanced brand reputation.
Support agents no longer have to waste time looking for information: the AI bot will do everything for them. While addressing loads of repeated issues is a painstaking job for a human being, AI scans all the preset scenarios in a glimpse of an eye and displays a solution to a customer. It takes the burden off the shoulders of employees, saves you on recruiting the new ones, and ensures continuous communication with the client.
As technology advances, customers expect companies to provide more exciting collaboration experiences. By acting in a human-like way, a chatbot creates bonds with the brand and brings it closer to ordinary people.
AI can predict what will interest consumers and play ahead by offering them personalized service. By analyzing the customer’s behavior on the website, the views, clicks, and purchases, predictive solutions store up this information and translate it into personalized offers the user might like.
Once it has analyzed emotions, AI may direct customers to the needed agent. For example, satisfied clients are more likely to spend their money and thus the learning bot will route the customer to the sales team. On the other hand, it may suggest directing to a customer retention team if the buyer is dissatisfied with the product.
Nonetheless, developers face a scope of issues to be solved in AI. Some of these problems include:
- the inability to identify the pitches of people’s voices,
- overcome background noise,
- understand varied accents and dialects.
Like everything, the system also takes time to train. AI still struggles with recognizing contextual clues or reading between the lines. On top of that, lack of historical data hinders chatbot performance. All these factors may lead to customer’s frustration and lower conversion rates.
This screenshot illustrates a case when a customer has a unique problem, and providing an article doesn’t serve the purpose. In any case, the key aspect is that it’s learning with time and maybe someday it will measure up to expectations.
Image credit: Userlike
Advanced systems powered by automated solutions pave the way to a better user experience. Businesses strive to provide undisrupted technical support 24/7 while saving service costs. And that’s where applying chatbots may come in handy.
The indisputable proof of the robust implementation of chatbots is that by 2024 the chatbot market is predicted to comprise $9.4 billion. The process of ordering a pizza or booking a hotel room is becoming smooth and easy as AI is taking the business industry by storm.
Nevertheless, AI is a long way off replacing customer support teams. Their primary mission is to make work more effective, quickly gather information, and be of use to clients. Although there’s a plethora of issues that require addressing in conversational AI, sophisticated bots will likely be a dominating trend in technical support and beyond.
And what is your opinion on the performance of the AI chatbots? Have you ever turned to chatbots for assistance? Was your experience successful? Leave your comments below.