Artificial Intelligence In Customer Service: More Than Chatbots

When it comes to Artificial Intelligence (AI) in call centers, most people automatically think of “chatbots.” But while useful, they alone cannot provide organizations with the information they need to compete effectively and strategically.

After all, chatbots cannot recognize indicators of customer dissatisfaction in time to rectify a situation and retain the user. And they certainly cannot discern between customers who threaten to leave a service and those who actually do.

Overall, today’s forward-thinking companies use AI to drive better customer experiences.

Why Chatbots Are The Face Of AI Customer Service

With increasingly impatient and less loyal customers, companies must do everything possible to retain them by improving their experience and expediting the resolution of their problems.

A key way to improve the customer experience is to improve your interactions with the contact center. After all, no one likes to be on hold or repeat his or her case when the call is transferred from one agent to another.

This is why so many customer service centers are implementing chatbots – intelligent, natural-language virtual assistants who can recognize human speech and understand the caller’s intention without requiring the person speaking to do so with specific sentences.

Chatbots enhance the customer experience by speeding up monotonous and repetitive tasks, such as:

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

Thanks to chatbots, customers no longer need to waste time waiting on the phone to speak to an agent to solve simpler actions. Instead, they can get what they need using simple voice or text commands.

How Artificial Intelligence In Customer Service Extends Beyond Chatbots

As we all know, if customers don’t get the care they expect, they will switch to another provider that meets their expectations.

Chatbots can simplify the most basic customer interactions with a brand, but they cannot provide the complex or holistic experience that keeps customers coming back and cannot help predict what will happen in the future. However, there are other AI apps that can do exactly that.

These more sophisticated AI apps extend far beyond chatbots: they predict human behavior in a way that empowers the organization to take proactive steps to manage agent performance, improve engagement, and optimize back-office operations, as well as obtaining deeper information about the customer journey.

How Is AI Driving A Better Customer Experience? 

Machine Learning In Customer Service Is The Future

While chatbots may be the face of modern customer service, machine learning is empowering everything from behind.

Machine learning helps companies predict human behavior, such as identifying dissatisfied customers who are at risk, and constantly becoming “smarter”, learning from all the new data they enter. With machine learning, call centers can take advantage of call recordings and quality management scores, customer surveys, Net Promoter Score (NPS), and Voice of Customer (VoC) data, as well as analytics text, desktop and speech, to create mathematical approximations of customer and agents’ behavior.

Once machine learning has gathered and analyzed the data, you can use that information to predict the outcomes that most affect the contact center and the business. This is invaluable to businesses, as many do not recognize signs of customer dissatisfaction until they lose them.

There Are 3 Key Ways That Machine Learning Is Applied In Call Centers:

1) Predictive Net Promoter Score (NPS)

Predictive NPS uses machine learning to generate an NPS for each customer, regardless of whether they have answered a survey or provided feedback. To do this, it evaluates both the complete customer surveys and the speech phonetics data to determine the characteristics of their interactions that most impact satisfaction scores.

Predictive NPS Can Drill Down Into:

• The amount of time between the first response and subsequent response times.
• Whether text responses with similar wording have provided satisfied customers.
• How much effort the agent puts into solving the customer’s problem.

The technology then uses this information to generate a predictive NPS for all customers: it tells a company if an interaction with the customer will lead to a positive or negative experience.

2) Predictive Evaluation

Predictive assessment uses machine learning to drive specific quality management, working similarly to predictive NPS. Apply a mathematical model to previously rated quality management evaluations and phonetic voice beats to identify the aspects of each interaction that have the greatest impact on quality scores.

The resulting generation of predictive quality assessment scores enables a specific quality management process. With this information, evaluators are equipped to identify and evaluate the right calls and make better decisions regarding which agents need what type of training.

Furthermore, machine learning models are constantly refining and evolving their predictions as they feed more data: the more contacts that are manually evaluated, the more accurate the predictive scores will be.

3) Analysis Of Feelings

The third key way machine learning is applied in a contact center is through sentiment analysis. Opinion analysis takes advantage of personalized call center-focused vocabulary to automatically rate the sentiment of each call, whether positive, negative or neutral.

Managers can spot sentiment trends as they occur, and consequently quickly adjust business areas that impact the customer experience. These sentiment scores are also constantly evolving to identify top opportunities for agent training and decide how to handle emerging issues.

The Future Of Artificial Intelligence In Contact Centers

While chatbots are a great start, they are just the tip of the iceberg when it comes to what AI can do for call centers and customer experience. Artificial Intelligence-based analytics and advanced predictive modeling use current and historical data to make mathematical approximations of both customer and agent behavior, and smart predictions about outcomes that most affect customers and the organization serving them.

Unlike chatbots, these more sophisticated AI applications can recognize indicators of customer dissatisfaction in time to rectify situations and retain customers; and offer organizations the knowledge they need to compete effectively and strategically.

At NPS, Predictive Assessment and Sentiment Analysis, Artificial Intelligence enables organizations to take proactive steps to manage agent performance, improve customer engagement, and gain insight into the customer journey. And this is just the beginning.