Artificial Intelligence has fundamentally altered the landscape for dozens of industries, enabling significant improvements in data collection and analysis to drive sales, boost employee engagement, and improve customer satisfaction.

It’s that last point in particular that has the potential to change the world of call centers forever. It’s no surprise then that nearly 80% of businesses have implemented or are planning on implementing some form of AI in their customer service departments according to a recent Oracle survey.

More than just providing greater insight into the performance of an agent on a call, AI is changing how we approach customer service as a whole. From predicting the intentions of the caller to actually increasing the performance rates and results on an agent-by-agent basis, AI has the potential to reform the call center from the ground up. But it starts with proper implementation. Agents need to be taught how to work with AI systems. AI needs to be catered to the unique needs of an individual call center. And there’s no single one-size-fits-all solution.

Let’s take a closer look at what that transformation looks like and how organizations are already starting to implement systems that generate tangible results.

Technology in the Call Center

There are two major types of AI in the call center right now. The first is able to evaluate huge volumes of data and provide just-in-time insights for agents to improve their performance on a call. This is the AI that will learn how to provide the exact right information at the right time during a call, and that can ensure agents are always one step ahead of the needs of a caller.

The other type of AI is conversational AI – which analyzes the speech of the two participants to identify emotions and ultimately intent. It is able to forecast the impact of a conversation based on the vocal ticks, emotional state, and overall engagement level of both the caller and the agent. The resulting conversational sentiment analytics provide valuable real-time feedback on the emotional state of both the customer and the agent. Instead of waiting for a customer to ask for escalation, the AI system can recognize when intervention is needed and automate the entire process.

This is where call center AI has the opportunity to really transform how work is done. Because of the combination of general and conversational AI, call centers will rapidly see actionable business insights that can be implemented across the entire organization to drive results. By the end of this year, Gartner estimates that as much as 85% of customer service interactions will be powered or supported by AI. Accenture estimates that there will be a 40% increase in business productivity by 2035 from these technologies. 

The Impact of Call Center AI

Artificial intelligence will have a lasting effect on the modern call center in several unique ways. Some examples include:

Revenue Impact – NewVoiceMedia estimates that US businesses lose $75 billion each year because of poor customer service. The lack of data and systems to followup with individual interactions can be incredibly costly. AI is changing that by closing the loop on missing variables and improving our ability to glean insights from every call.

Increased Customer Satisfaction – Traditionally, agent performance scores are based on how long a call takes, whether the issue was resolved and what the customer says in their post-call survey. It’s all quantitative data that can be captured at scale and evaluated automatically. Actual customer satisfaction is often the last thing on this list because it’s so difficult to measure at a high level. AI is changing that by providing insights into dozens of more factors, as well as the emotional state of the caller during the call. What was once only available qualitatively, through the supervisor’s review of calls, can now be captured passively with an AI system. The result is higher overall Net Promoter Scores (NPS) and better customer satisfaction rates.

Increased Debt Repayment – When customers are happier and data more available, it’s possible to drive greater results from calls. This includes a higher rate of debt repayment on all outstanding accounts. Calls are escalated to the appropriate agent almost immediately and potential solutions for a specific debt can be developed in real-time with support from both human and AI agents.

Agent Happiness/Customer Happiness – Customer happiness is the primary goal for any company, but equally as important is agent engagement and happiness. Customer success agents are often overworked, underappreciated, and disengaged at work. And no wonder. They spend hours dealing with upset customers who have been on hold for 30 minutes or longer. By improving the matching between customers and agents, and providing relevant data as a supplement for the agent, a lot of this friction can be removed and agents will feel better about their work. This can lead to lower turnover and higher performance rates.

Behavioral Profile Matching – AI is able to paint a detailed picture of each customer based solely on their purchase activity and previous calls. The resulting behavioral profile ensures faster and more accurate matching of customers when they call in with the right service agents.

Contact Center Reputation – When customers are happy, agents are happier, and resolution rates are up, the contact center’s reputation as a whole will improve.

Predicting Intent to Deliver Results

Deep learning is enabling call centers to better predict the intent of individual callers, mapping the most likely next action a customer will take and if an escalation is required before it happens. Emotion AI does this by analyzing hundreds of data inputs such as the content of speech, volume, inflection, tone, and much more. Combined with past call data from the same customer and the response to specific questions or statements by the agent, AI can more accurately predict what will happen next and present solutions in advance.

Intent prediction empowers call centers to implement new processes that will impact intent and reduce the risk of an angry customer. For example, if the AI algorithm can accurately identify when someone is about to become upset, the instructions given to an agent, the script they follow and even the potential solution offered can be altered in real-time, helping to reduce the likelihood of a negative outcome.

Closing the Loop with AI in Call Centers

Call centers are already data-driven engines. The most efficient call centers are effective because they can accurately identify the most common causes of customer concerns, train agents to respond preemptively, and then evaluate the outcomes of calls to iterate and improve over time.

The problem is that these processes lacked the data to be fully accurate. How the customer feels and actively responds to these tactics can have a huge impact on the eventual outcome. With AI, we can now close the loop and more fully understand where challenges exist and solve them before they impact revenue or customer satisfaction. Forrester quantifies a $1.2 trillion opportunity for insight-driven businesses over competitors who lag behind by 2021. You simply cannot afford to fall behind.

When implemented correctly, call center AI is a game-changer, fundamentally altering how we interact with customers for the better.