The heart of customer service is the call center. Here, agents directly engage with customers to address questions, problems, and concerns related to a company’s products and services. But by its very nature, the call center has become a bottleneck that can lead to high customer turnover rates, low customer satisfaction, and disengagement among employees. 

A recent State of Service report from Salesforce showed attrition rates between 19% and 35% across different industries. Another survey taken by NICE WEM in 2021 showed a 41% turnover rate during the COVID-19 pandemic among call centers in the US and UK. In an environment that increasingly demands speed, agents are struggling to build human rapport with customers that allows them to engage on a personal level, achieve their goals, and move between calls at an acceptable rate. 

For this reason, many companies are turning to AI as a resource to improve performance. With 63% of service professionals noting AI as a crucial tool to help engage with customers faster and 79% of IT leaders citing AI as helping to reduce burnout and support better engagement, call centers that don’t engage with advanced AI technologies are likely to only fall further behind. This is where AI-mediated conversations (AI-MC) come in. 

Behavioral Signals has developed an algorithm based on years of intensive research in NLP and Behavioral Signal Processing. An advanced automated call routing solution, AI-MC dissects conversations, develops profiles of agents and customers, and brings together the right people for the best conversations. This helps reduce turnover rates, improve customer satisfaction, and increase revenue across all interactions. 

How AI-MC Helps Build Better Rapport Between Agents and Customers

Personalization is crucial for showing customers that their needs are being considered. Hiding the script and conveying a personal connection can help reduce customer attrition and boost revenue, but agents struggle to do this with the bevy of other demands placed on them. Nearly 78% of agents note that they struggle to maintain the balance between the need for speed and the ability to personalize interactions and build rapport with customers on the phone. 

The National Bureau of Economic Research (NBER) reports that conversational assistants are already helping to boost support agent productivity by 14%, and many service agents undergo a number of manual tasks to address customer needs. But there’s another issue that can have an even greater impact—the natural rapport between the agent and the customer. 

AI-MC helps to quantify rapport based on profiling technology that identifies the needs, expectations, and likely outcomes of individual callers in real time. Behavioral Signals’ AI-MC solution profiles every speaker’s voice across 75 dimensions. From the rate at which individuals speak to the intonation they use, dimensional profiling allows AI-MC to create detailed summaries of both agents and customers to facilitate better matching when a call comes in. The result is that things like anger can be detected in more than 80% of cases, allowing earlier intervention and routing to the appropriate agent, instead of further escalation that decreases the likelihood of a positive outcome. 

One of the core elements of successful customer service is the rapport an agent builds with their customers, and AI-MC provides the real-time insights and recommendations needed to do this better. By proactively matching callers and agents to reduce the inherent tension that underlies many customer service calls, AI-MC helps reduce repeat call rates, and helps agents perform better, with higher call success scores, agent performance scores, and overall customer satisfaction rates. 

The Benefits of Behavioral Signals’ AI-MC

Through the implementation of AI-MC, organizations can directly improve the quality of their customer service interactions. CSAT scores increase broadly by 10%, and AHT is reduced by 5%, ensuring a 7% decrease in repeat calls and an 8% increase in positive call outcomes. This leads to higher productivity and performance rates among agents and a general 12-17% increase in revenue for call centers utilizing the technology. 

In a European Power Corporation, AI-MC was implemented to optimize the collections process, resulting in an 11.6% increase in payment recovery and a 95% increase in call success. At a Process Outsourcing organization, sales increased by 18%, and immediate refusals decreased by 8%. Across all Behavioral Signals’ clients, CSAT rates increased by 4.74%- 19.84%, directly reflecting strong rapport being built between agents and customers based on greater insights during the call. 

Behavioral Signals’ core technology provides insights into human behavior and intentions that can fundamentally and substantially improve the performance of agents. By elevating the accuracy of customer-agent matching with an emotionally intelligence algorithm, call centers are seeing substantial improvements in KPIs across the board. 

Improving the Flow of Conversation

Nearly 30% of contact center calls are repeat calls. This significantly increases the risk that a caller will become agitated, which can lead to undesirable outcomes, stress for call agents, and further upset customers.

Behavioral Signals has developed systems that reduce the risk of poor call outcomes, resulting in happier customers, fewer repeat calls, more efficient agents, and lower turnover rates in the long term. It’s much easier to build rapport and respond to a customer’s timely needs when AI-MC automatically routes that customer to the individual who is best suited to resolve the problem.

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People also ask 😊

Why is AI-MC a crucial component of the modern call center? What makes it a critical addition?

 AI-MC is crucial as it measures and impacts customer and agent behavior. It is proven that a better match of customer and agent, both technically and emotionally leads to better business performance in a customer care center.

Compared to other AI solutions, what does AI-MC do differently?

AI-MC is different from others as it only analyzes the tonal voice quality of the human voice (customer /agent). Our data modeling is prior to the AI hype as it is pivoted on analyzing millions of audio samples and deciphering the signal variations. While most of the CX AI solutions parse the word /phrase and infer from speech-to-text and text-to-speech conversions, we work on voice tone quality and offer insights from emotional analysis of each customer interaction.

What is the most common positive response/reaction from customers after integrating AI-MC?

 Customers integrating AI-MC are consistently impressed by the innovative design and the value of speech tone analytics, significantly enhancing customer experience (CX) KPIs. The most rewarding impacts are observed in contact center operational efficiency:

1. Reduced Customer Inquiry Handling Time: AI-MC facilitates frictionless conversations, leading to quicker resolutions. For example, a mere five-second reduction in average handling time (AHT) across all agents in a 100-seat call center can result in substantial annual savings, amounting to thousands of dollars.
2. Improved Revenue Recovery and Assurance: AI-MC enhances the effectiveness of collection agencies by better matching the right agent with the right client, directly boosting revenue recovery. This is measured in “Propensity to Pay(PtP) KPI  which  sees a wonderful 20-30% increase in daily collection operations.

Additionally, many customers are eager to extend our AI-MC solutions to capture employee mental health and wellness, particularly in high-stress situations like 911 calls. Some envision this evolving into a real-time agent efficacy tool, incorporating voice tones and data feeds from wearables for comprehensive analysis in sectors like healthcare and disaster recovery. This highlights the growing trust and expanding applications of our AI-MC solutions in diverse and critical environments.

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