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How AI Is Transforming Call Centers with Actionable Insights
Call centers have long been performance-based, relying on a combination of careful scripting and keen oversight to maximize first-call resolution and reduce call times. AI is making it possible to do this at scale in ways never before possible. By providing real-time feedback on customer response, predictive analytics to identify when intervention is needed, and in-depth analysis of call data to improve call matching and overall performance, AI is transforming the modern call center.
This is more important than ever as the majority of the white-collar workforce in America is working remotely during the coronavirus pandemic. With fewer resources, often spread out, companies need to expedite call times, monitor and track performance, and maintain a strong relationship with customers in trying times.
Across industries, call volume is higher than ever. In March, as the coronavirus crisis descended on the United States, brands in the airline and hotel industries saw call volume increase by as much as 96 percent and 130 percent respectively. In industries already stretched thin and eager to leverage technology for real-time improvements to performance and first-call resolution rates, a black swan event is making their shortcomings more visible.
Artificial intelligence is already being leveraged in many call centers to address some of the issues that lead to longer call times and lower satisfaction rates. Now is the perfect time for companies that rely on their customer service to look for rapid improvement options in the form of AI.
This article examines what such a move entails and the benefits enterprises can expect.
A Steady Trend Towards AI Support in Customer Service
Well before the pandemic, companies were leveraging technology to provide a more robust (and often more personalized) experience, and customers generally liked it. Salesforce reports that 69 percent of consumers actively choose chatbots because they are faster for basic queries and can help direct them to the correct customer service agent when more complex problems arise.
AI is effective because it can amplify the customer experience in real time. Data can be delivered to agents as they need it. Customers can access information about their account history and orders without waiting for a human agent. Simple questions can be resolved automatically without long hold times. At the same time, when a call to a customer service agent is needed, AI can help drastically increase responsiveness from agents and close out issues faster.
Consider that the largest corporations in the U.S. spend a combined $250 billion per year on customer support and the opportunity to streamline and improve that process is extremely tempting.
How AI Supports Agents to Provide a Better Experience
Although chatbots and automated phone systems can guide customers to answers for simple questions that may not need a human agent, they are only the front line in customer service technology.
AI can have the most profound impact not by replacing human agents but by supporting them, monitoring them, and extracting valuable information from their calls. As rapidly as AI has advanced, we're still far from a system that can replicate the human touch on a phone call. We need our customer service agents, but we can empower them to do better with the right combination of technology.
Take, for example, how agents are currently measured. Customer service scoring and agent performance scoring is based heavily on human observation. Calls are monitored and listened to only in part, often by a small number of human agents who have their own biases that could impact their evaluations. Call analysis can be inaccurate, and the scoring of agents can be directly impacted. Traditional call monitoring lacks the ability to pull data from multiple sources in real time. Agents only receive feedback and support after the call and there's no automation or assistance implemented to guide customers to the correct agent. Customers may talk to half a dozen agents before they reach the right individual, and by then it's difficult to have a smooth call with a good outcome.
AI improves this situation by performing those basic functions (monitoring, analysis, and support) in real time and at scale. Every call is monitored and recorded. Speech patterns are analyzed to determine the mood and response of both the customer and the agent on each call. The outcome is recorded and compared against the actions taken by the agent on the call.
Much of this data can be delivered in real time to the customer service agent, enabling them to respond in the most effective way. It also ensures that customers are matched with the right agent much faster. This makes customers happier because it resolves their issue more directly. It makes customer service agents happier because they are working on cases that they are uniquely suited to. When they succeed at a higher rate, they will be happier in their work and continue to succeed.
Predictive Analytics and Automation through AI
AI is capable of actively analyzing data from a customer call to predict when someone is likely to get angry and to facilitate a change before that happens. By capturing data points related to their vocal characteristics, their previous call history, the nature of their concern, and the response of the customer service agent, the system can flag a potential issue well before a human operator would and intervene more rapidly.
Imagine being able to analyze every interaction you have across all your customer-facing channels in real time and create a picture of a larger problem. Customers don't just call support anymore. They leave reviews on Google. They tweet at brands. They attempt to use chatbots on websites or submit support tickets. This data can be captured and integrated into a single profile, enabling rapid assignment of the correct customer service agent who can proactively address their problem before it escalates. AI-driven call quality monitoring is making that possible.
Three Key Benefits
To summarize, AI and analytics can offer:
- Automation. AI automatically captures call data, routes individuals to the correct customer service agents based on both input and mood, and creates a profile for future reference that can be used in the call center and other business areas. This requires no human intervention and is done automatically for every call.
- Analysis. Call center AI provides in-depth analysis of individual calls at a scale that human managers and quality control specialists cannot. Each call is measured and compared against performance benchmarks and integrated with outside data to provide a clear picture and actionable insights.
- Support. AI is directly integrated with call center service agent workstations, giving them immediate insight into the data being captured, the potential outcome of a call, and more. The result is faster response times, a higher first-call-resolution rate, and happier customer service agents who now have the tools to do their job at a higher level.
AI-Supported Call Centers During a Pandemic
Call center AI is not new -- the largest companies have been integrating these tools for several years and realizing substantial benefits. The onset of a global pandemic has accelerated plans for many companies.
What was once a centralized process that allowed close monitoring of customer service agents has now been decentralized, with service agents and quality staff often working from home. AI is helping to bridge the gap in efficiency, keep everyone connected remotely, and provide real-time support for both customer service agents and the managers who need to monitor and evaluate call quality data.
Call centers across almost every industry are weathering this storm -- from the travel industry representatives helping upset customers process cancelations and refunds to the IT and software company representatives seeing unprecedented demand for their services as a large percentage of the population works at home. AI allows for 24/7 response rates, social distancing in the office by sending most people home, and balanced responses that would not have been possible even a decade ago.
Rana Gujral is the CEO of Behavioral Signals, an emotion and behavior recognition software company. In his career, Gujral has led the development and exit of various technology companies and held leadership positions at Logitech S.A. and Kronos Inc., where he was responsible for generating billions in revenue through the development of best-in-class products and several award-winning engineering innovations. He has been honored with awards such as "Entrepreneur of the Month" by CIO Magazine and listed as an “AI Entrepreneur to Watch” in Inc. You can reach him via email .