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AI in Customer Service: More than Just Chatbots

When it comes to artificial intelligence (AI) in call centers, most people automatically think “chatbot.” Although they’re useful, chatbots alone can’t give organizations the insights they need to compete effectively and strategically. With the rise of stronger AI and machine learning (ML) programs, the opportunities to improve contact centers continues to grow. As customers crave more immediate results and seamless experiences, using AI in customer service is an effective tool companies can use to strengthen brand guardianship.

Taking a step into the future, today’s forward-thinking businesses will use AI to drive better customer experiences and strengthen brand guardianship. Learn more about how to use AI in customer service positions to make it happen.


With increasingly impatient and less loyal customers, businesses need to do everything they can to hold on to customers. One way to improve their experience and expediting the resolution of their issues is by empowering customer service agents with greater technology.

One key way to improve the customer experience is by improving the customer’s interactions with the call center. After all, no one likes to wait on hold or repeat themselves when transferred from one agent to another. But merely throwing more agents at the problem doesn’t work: it’s expensive, it doesn’t scale and it doesn’t increase efficiencies.

That’s why so many call centers deploy chatbots—intelligent, natural language virtual assistants that can recognize human speech and understand a caller’s intent without requiring the caller to speak in specific phrases. Recent developments in natural language processing in the form of ChatGPT, are already being incorporated into customer service chatbots, a trend that shows no sign of slowing.

Where do chatbots excel? Chatbots improve the customer experience by expediting monotonous and repetitive tasks, such as:

• Requesting account balances
• Changing passwords
• Scheduling appointments
• Troubleshooting minor issues

Thanks to AI chatbots, customers no longer need to waste time waiting to speak with customer service agents to complete these simple actions. Instead, they can get what they need by using simple voice or text commands.

But what chatbots can’t do is go beyond assisting with basics tasks. They aren’t able to recognize indicators of customer dissatisfaction in time to rectify a situation and retain the customer. They most certainly can’t discern between customers bluffing they’ll drop a service and those that actually will churn.

How AI for Customer Service Extends Beyond Chatbots

As we all know, if customers don’t receive the level of customer service they expect, they’ll promptly switch to another vendor to get what they need. That’s why chatbots should be viewed as an asset for the customer service team to help deliver exceptional customer service.

Chatbots might streamline the most basic of customers’ interactions with a brand, but they can’t provide the complex or holistic experience that guarantees customer satisfaction. Companies need solutions that keeps customers coming back for more and help predict what will happen in the future. Thankfully there are other applications of AI that can do just that.

These more sophisticated applications of AI extend far beyond chatbots—they predict human behavior in a way that empowers the organization to take proactive measures to manage agent performance, improve customer engagement and enhance back-office operations, as well as gain deeper insights into the customer journey.

Let’s dive into how artificial intelligence is powering improved customer experience.

Why Machine Learning in Customer Service is the Future

While chatbots might be the face of modern customer service, machine learning is powering everything from behind the scenes.

Machine learning helps companies predict human behavior. It can identify dissatisfied customers who are at risk while constantly getting “smarter,” by learning from all of the new data that comes in. With machine learning, call centers can leverage call recordings, quality management scores, customer survey scores, Net Promoter Scores (NPS) and Voice of the Customer (VoC) data. Machine learning also captures text, desktop, and speech analytics to create mathematical approximations of both customer and agent behavior.

3 Ways Machine Learning Helps Contact Centers

Once machine learning has gathered and analyzed the data, it can then use that information to predict the outcomes that most affect the contact center and the enterprise. This type of discernment is incredibly valuable to businesses since many don’t recognize the signs of customer dissatisfaction until after customers are lost.

So how can you apply machine learning to your contact center? Here are three ways to do so:

1. Predictive NPS

Predictive NPS utilizes machine learning to generate an NPS for every single customer, regardless of whether they’ve taken a survey or otherwise provided feedback. It does this by assessing both completed customer surveys and speech phonetics data in order to pinpoint the characteristics of customer interactions that most impact customer satisfaction scores. Predictive NPS can dig into:

  • The amount of time between the first reply and subsequent response times
  • Whether text responses with similar wording have resulted in satisfied customers
  • How much effort the agent puts into resolving the customer’s issue.

The technology then uses this information to generate a predictive NPS for all customers. It essentially tells a business whether a customer interaction will lead to a positive or negative customer experience. For example, “When calls come in that look like this, here’s what will happen.”

Think about how powerful this data can be for a contact center. If you gather post-call surveys from only two percent of your customers, using predictive NPS you then can generate an NPS for the other 98 percent of customer interactions as well.

This comprehensiveness lets businesses make more informed decisions, based on 100 percent customer data. By doing so, it helps to support agents on things like customer outreach or agent evaluations. This AI-powered customer service also enables businesses to deliver different messaging to promoters versus detractors (or those who are neutral), for maximum impact.

2. Predictive Evaluation

Predictive evaluation uses machine learning to drive targeted quality management, working in a manner similar to predictive NPS. It applies a mathematical model to previously scored quality management evaluations and phonetic speech hits in order to identify the aspects of each interaction that make the biggest impact on quality scores.

The resulting generation of predictive quality evaluation scores enables a truly targeted quality management process. Using this information, evaluators are equipped to identify and evaluate the right calls and make better decisions regarding which agents need which kind of coaching.

Without this kind of technology-powered predictive evaluation that can evaluate 100 percent of customer interactions, evaluators have to randomly choose calls to evaluate. And because they only can analyze about 5 percent of all interactions, they need to be sure they’re evaluating the right ones.

Another benefit of customer service AI technology is that machine learning models are constantly refining and evolving their predictions as they’re fed more data. This means that the more contacts manually evaluated, the more accurate the predictive scores will become.

3. Sentiment Analysis

The third way machine learning is applied in the call center is via sentiment analysis. Sentiment analysis leverages custom-designed contact center-focused lexicon to automatically score each call’s sentiment – whether positive, negative, or neutral.

No longer do managers have to wait for lagging feedback sources like sales surveys or post-call surveys to understand the voice of their customer. Instead, they can use sentiment analysis to spot trends as they happen.

It allows them to quickly adjust the areas of the business that impact the customer experience. Managers can also use these constantly evolving sentiment scores to identify prime opportunities for agent coaching and decide how to handle emerging issues.

The Future of Artificial Intelligence in the Contact Center

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

Unlike chatbots, these more sophisticated AI applications can recognize indicators of customer dissatisfaction in time to rectify the situations and retain the customers; discern between customers bluffing they’ll drop a service and those that actually will churn; and give organizations the insights they need to compete effectively and strategically.

The predictive NPS, predictive evaluation and sentiment analysis AI enables organizations take proactive measures to manage agent performance, improve customer engagement and gain deeper insights into the customer journey. And that’s just the beginning. Discover how AI and analytics can drive better human experiences within the contact center.

Terri Kocon is a Software Product Marketing Manager for Calabrio with 20+ years of industry experience spanning product marketing, channel marketing and digital marketing in the high tech industry. Terri leverages her unique skill set and technical knowledge to translate the capabilities of SaaS-based and on-premises solutions into effective product messaging and go-to-market plans across various channels and audiences.
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