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Using AI in the Contact Center for Greater Brand Guardianship

Why—and How— to Use AI, Machine Learning, and Analytics to Empower the Contact Center as a Brand Guardian

The contact center is still the primary pressure-release valve for customers. And that pressure holds tremendous value.

Customer frustrations reveal early warning signs of large-scale issues and brand risk. These insights can help contact centers rapidly fix problems and protect loyalty and reputation. So how can using AI in the contact center reveal those profitable insights?

The time is now to start capturing those valuable insights to protect brand loyalty and reputation. Discover how to use AI-fueled analytics to strengthen your contact center brand guardianship.

Man celebrating using AI in the contact center

The Contact Center Receives the Earliest Signals of Brand Risk

Our State of the Contact Center 2022 report made it clear that consumers expect more and forgive less. Contact center managers say expectations on customer experience (CX) keep rising. The majority of consumers say they’ll jump ship after just two bad experiences.  This means that brands need to be proactive in ensuring a consistent, high-quality CX.

Taking action starts with analyzing what issues consistently appear in your contact center. They can indicate bigger problems that lead to serious business risk. Keep your eye out for these common issues that may be affecting your brand guardianship:

Product or Service Issues

A surge in customer calls is often one of the first signs of acute or chronic problems with a product or service.

Communication/Connectivity Issues

Interactions can reveal challenges that customers are having connecting with a brand—pointing to issues with the website, social channels, chat apps, or within contact center technology itself.

eCommerce Issues

A flood of customer issues around purchasing or payments can indicate problems with an eCommerce platform or payment gateway/app.

Supply Chain Issues

Customers reporting slow or delayed order fulfillment—or frustrations around out-of-stock products—can signal inventory and supply chain issues.

PR Issues

Customers calling in and engaging on social channels can be one of the earliest tip-offs to a broader public relations issue involving the brand.

Employee Engagement/Satisfaction Issues

Voice-of-employee (VoE) information can tease out rising employee frustrations or acute points of friction and frustration for agents.

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Using Artificial Intelligence (AI) to Find the Signal Amid the Noise

Voice-of-the-Customer (VoC) and Voice-of-the-Employee (VoE) data pours into the modern multichannel contact center every day. The vast majority of that represents routine customer communications. There’s also plenty of customer problem-solving, issue resolution, and venting .

Not every angry customer, however, signals a looming problem under the surface. Most customer interactions stem from issues that do not represent widespread problems or serious brand risks. The challenge is finding those most relevant and urgent signals amid the noise of everyday interactions.

Modern AI, machine learning (ML), and analytics excel in piercing through the piles of data to reveal those issues. AI-powered tools can process the exponential volume of raw VoC and VoE data from every channel, finding patterns and trends in real time.

More and more contact centers are leveraging AI, ML, and analytics to help uncover emerging issues—so brands can investigate and take action before serious damage is done.

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Using AI in the Contact Center to Uncover Root Causes

Here’s a bigger challenge: The VoC on its own doesn’t draw a straight line to the root cause of customer frustration. Only analyzing the VoC in isolation can identify that customers are frustrated about slow order fulfillment. But that customer pain point could be caused by an issue with the eCommerce platform, it could be a supply chain issue, or it could be traced back to some other internal operations problem.

Similarly, the VoE without wider analytics insight can surface warning signs of agent stress and burnout. But that stress could be the result of high call volume, understaffing.or technology gaps. It could also stem from something that could be addressed with additional, focused agent training.

The complexity of modern enterprises means the root cause of these issues could take weeks, months, or even longer to become clear on their own. By then, it’s far too late—the brand damage is incredibly hard to undo, and trust is hard to earn back.

Here’s where the magic of modern AI and ML really comes to life. These smart tools can rapidly correlate the VoC and VoE insights with data points from within the contact center and across the business, including:

  • Sales
  • Marketing
  • Website
  • eCommerce
  • Inventory
  • Supply chain and fulfillment
  • HR
  • IT
  • Finance
  • Other internal systems

Contact center leaders can unravel complexity and uncover root causes in near-real time. Modern contact center analytics tools must have these predictive, ML-fueled abilities to rapidly create those connections and find the cause behind the symptom.

man working at computer - ai in the contact center

AI in Action: Calabrio Analytics Competition Winners

At our 2022 Calabrio Customer Connect (C3) event, we continued our annual tradition of recognizing and celebrating the most innovative and impactful uses of Calabrio’s analytics, speech-to-text, AI, and ML tools among our customers. Many of the winners of this year’s Calabrio Analytics Competition focused on proactive brand guardianship.

Learn more about how 3 contact center leaders did just that below:

1.Turning Negative Sentiment Around with AI-Powered Phrase Optimization

Seeing repeat calls and cost-per-call rising, Idaho Central Credit Union (ICCU) wanted to uncover root causes of these problematic trends. Using sentiment and speech analytics, ICCU identified contact center interactions to showed negative sentiment and high member effort.

Using Calabrio’s analytics powered predictive NPS and quality scores, they were able to further refine their focus on interactions with poor outcomes. Then, ICCU doubled down on their use of AI. They built an AI-fueled Phrase Optimizer that automatically suggests helpful phrases to agents to help improve member issue resolution and maintain positive member sentiment. On top of adding this tech-enabled fix, ICCU focused on agent training where it was needed.

By improving issue resolution and enhancing member satisfaction, ICCU estimates its eliminated 9,000 repeat calls already in 2022. netting thousands of dollars in savings and stopping the rising cost-per-call trend. The credit union is also able to optimize agent staffing to deliver higher service levels with fewer agents.

2. Identifying Bottlenecks to Speed Service and Enhance CX

Early in the pandemic, Peckham, a provider of third-party contact center solutions, recognized service bottlenecks emerging among some of its main clients. Using Calabrio Analytics to dig into the issue, Peckham honed in on a high volume of calls with long pauses. Calabrio Analytics’ AI-powered tools help them trace these long-pause calls back to a combination of gaps in technology and agent training.

To address the issues they found, Peckham:

  • Implemented new IVR messages to rapidly address common service needs
  • Added additional coaching
  • Built an agent FAQ to facilitate faster agent responses
  • Deployed new technologies to streamline their contact center processes

This combination of tech and training is enabling agents to eliminate pauses and speed issue resolution. improving customer experience to protect its clients’ brands. These enhancements also enable Peckham’s agents to handle one additional call per hour. This adds up to a $2.7 million annual increase in top-line revenue.

3. Using Desktop Analytics to Root Out the Cause of Disconnects

Like many businesses forced to make the sudden shift to remote work, Cummins recognized that phone connectivity issues were hurting both its customer and agent satisfaction. The manufacturer of power generation solutions used Calabrio Analytics to zoom in on these disconnect scenarios, correlating them with insights from Calabrio’s desktop analytics tool to trace connectivity issues back to four main “fail points.”

By fixing these technology issues, Cummins significantly reduced connection risks, boosting customer and agent satisfaction while also saving the company roughly $160K annually.

4. Bonus Case: Using Sentiment Analytics to Find Agent Phrases That Send Customer Sentiment South

As part of its continuous efforts to proactively improve patient experiences, the Mayo Clinic used Calabrio’s predictive sentiment analytics tool to focus in on interactions with negative patient sentiment. Calabrio’s AI tools then identified agent phrases that were strongly correlated with negative customer sentiment.

The Mayo Clinic contact center leaders implemented new agent training to directly address these “negative phrases” and equip agents with effective alternatives to improve patient sentiment and enhance overall patient satisfaction.

Learn from leading peers in our Brand Guardian webinar

There are so many ways to use AI in the contact center to reveal valuable insights into customer interactions. We dove deeper into the award-winning VoC analytics use cases mentioned above in a recent customer webinar. Directly hear from your leading peers on how they’re leveraging Calabrio’s analytics, AI, and ML tools to rapidly identify customer issues and connect them to root causes. This initiative proactively protects your CX, brand loyalty, and hard-earned reputation.

Jennifer Docken joined Calabrio’s product marketing team as the QM Product Marketing Manager in 2021. As an experienced B2B marketer she brings with her 10+ years of marketing experience across multiple industries. She is innovated minded with a strong drive to understand customer constraints and how to break down barriers. She strives to better understand the pressures put on the modern Contact Center and how to drive customer satisfaction and agent engagement.
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