How Does Artificial Intelligence Influence Customer Service?

When it comes to Artificial Intelligence (AI) in call centers, most people automatically think of “chatbots”, AI-based tools whose function is to interact with users through chat platforms to improve customer service.

But while they are useful, chatbots alone cannot give organizations the information they need to compete effectively and strategically. After all, chatbots cannot recognize the indicators of user dissatisfaction in time to rectify a situation and retain the user. In short, today’s forward-thinking companies use AI to drive better user experiences.

Chatbots: The New Face Of Customer Service

With users increasingly impatient and less loyal, companies must do everything possible to retain users improving their experience and accelerating the resolution of their problems. A key way to improve the user experience is to improve interactions with contact centers. After all, no one likes to keep waiting or repeat the speech when it is transferred from one agent to another.

For this reason, many call centers are implementing chatbots: intelligent virtual assistants and natural language assistants who can recognize human speech and understand the intention of the caller without requiring the caller to speak in specific phrases. In fact, according to Oracle, 80% of companies already use or plan to use chatbots in their customer service by 2020.

Chatbots improve the user experience by accelerating monotonous and repetitive tasks, such as:

• Request account balances.
• Change passwords.
• Schedule appointments.
• Solve minor problems.

Thanks to chatbots, users no longer have to waste time waiting on the phone to talk to an agent to complete these simple actions. Instead, they can get what they need through simple voice or text commands.

AI Beyond The Chatbots

If users do not receive the level of customer service they expect, they will quickly switch to another provider to get what they need. Chatbots can simplify the most basic interactions of users with a brand, but cannot provide the complex experience that keeps users engaged.

According to Gartner, the priority use for the application of AI (70% of all cases of use at the end of 2017) was related to customer service and call centers. These more sophisticated AI applications extend beyond chatbots: they predict human behavior in a way that allows the organization to take proactive measures to manage agent performance, improve user engagement and back-office operations.

Machine Learning Enhances The Experience

While chatbots can be the faces of modern customer service, machine learning helps companies predict human behavior, how to identify unsatisfied users, and constantly becomes “smarter”, learning from all the new data that arrive.

With machine learning, call centers can take advantage of call recordings and quality management scores. User survey scores, Net Promoted Score (NPS) and Voice of the Customer (VoC) scores, as well as text, desktop and voice analyzes, create mathematical approximations of user and agent behavior, and it is essential to use that information to predict the results that most affect the contact center and the company.

The three key ways in which machine learning is applied in call centers today are: the predictive NPS (Net Promoted Score), predictive evaluation and the analysis of feelings.

While chatbots are a great start, they are only the tip of the iceberg when it comes to what AI can do for the call center and the user experience. AI-based analyzes and the advanced predictive model use current and historical data to make mathematical approximations of the behavior of users and agents, and intelligent predictions about the results that most affect users and the organization that serves them. Unlike chatbots, these more sophisticated AI applications can recognize user dissatisfaction indicators in time to rectify situations and retain users.

Predictive NPS, predictive evaluation, and sentiment analysis that enables AI to organizations can take proactive measures to manage the agent’s performance, improve user engagement, and gain a deeper insight into the user’s process.