In the fast-paced realm of contact centers, 83% of managers claim to have the analytical tools they need, while alarmingly only half are truly reaping the rewards. As more brands realize contact center experiences are shaping brand perception and driving revenue, they are shifting focus to cutting-edge technologies like artificial intelligence (AI) and machine learning (ML).
The latest advancements in AI/ML-fueled technologies open a world of possibilities for both customer engagement and workforce optimization—yet most organizations only think of these capabilities with the customer in mind. It’s time for brands to reimagine these capabilities, not as replacements for agents, but as empowering tools that elevate them to the status of brand guardians—relentless defenders who champion customer service while delivering unmatched business performance.
From virtual assistants to effective quality management (QM) and evaluation, AI/ML-driven advancements are revolutionizing the way contact center agents operate, improving customer experiences, and driving business success. Here’s how each of these technologies strengthen brand guardianship:
Increased automation and virtual assistant technology
In the wake of the COVID-19 pandemic, work as we knew it underwent a seismic shift. Organizations confronted unprecedented challenges, demanding agile adaptations within weeks or even days. Enter virtual assistants—rising to the occasion, these digital champions became indispensable in boosting efficiency and handling the surge of urgent information requests.
But virtual assistants aren’t just for enhanced customer experience. Imagine an evolved use case where these companions enhance the lives of contact center agents, adapting to the complexities of modern, hybrid operations. By granting agents the freedom to shape their work/life balance, virtual assistants become a trusted ally in their quest for personal fulfillment. These tireless assistants automatically notify agents of overtime opportunities or voluntary time off, while also suggesting unique requests tailored to individual schedules and overall staffing needs. The result? Improved morale, reduced attrition, and a more harmonious work environment.
Moreover, virtual assistants enable agents to showcase their true potential, as they automate routine interactions such as account balance inquiries, password changes, appointment scheduling, and minor troubleshooting. By relieving agents of these repetitive tasks, their focus shifts to high-value engagements, complex problem-solving, and delivering exceptional customer experiences.
AI-Driven Quality Management and Evaluations
AI-driven QM presents a viable alternative that addresses the many complexities associated with manual QM and evaluations. In fact, 98.4% of evaluations done by customers in Calabrio ONE are done automatically by AI-driven Automated QM! The latest AI-powered analytics bring comprehensive QM and analysis capabilities to the contact center, including the ability to analyze 100% of interactions rather than a subset. By incorporating these analytics into QM and training processes, organizations are improving quality evaluations and providing more personalized coaching that empowers agents as brand guardians.
Call transcription and sentiment analysis are both examples of AI-supported analytics. While transcription uses natural language processing (NLP), sentiment analysis combines text and speech-to-text analytics to identify emotional tone and provide a sentiment score for every customer interaction—whether positive, negative, or neutral. AI-powered sentiment analysis uses advanced text and speech-to-text analytics to identify key words and phrases that indicate emotional tone and underlying sentiment behind a customer’s words. It spots trends as they happen and reveals customer perceptions of a brand’s customer service agent performance.
Instead of waiting for the customer to tell you they’re unhappy with their experience after the call ends, AI-fueled analytics highlight those negative experiences in real time. Agents can flag all negative interactions for instant, high-priority review and enables the company to drive more successful customer outcomes—whether that means reaching out to a dissatisfied customer, addressing a new product or service issue, or providing additional training.
Predictive Analytics Equals Proactive Service
Predictive analytics leverages AI and ML to analyze historical data and identify patterns in customer behaviors. Predictive insights range from predictive quality evaluation scoring and predictive NPS for 100% of customer interactions, to predicting which agents will most benefit from specific training.
Predictive evaluations generate a quality score for every customer interaction with remarkable accuracy and then applies new insights to every interaction going forward. This allows for a more accurate, holistic view of agent performance—leading to more targeted quality management initiatives that don’t rely on random, sporadic, manual monitoring.
Predictive NPS assesses both completed customer surveys and transcriptions to create a predicted Net Promoter Score for each customer interaction. In other words, this score tells a business whether a customer interaction will lead to a positive or negative experience. This type of analysis helps to support agents with customer outreach and enables contact centers to make more informed decisions regarding the level of coaching each agent needs.
A brand’s reputation is on the line in every customer interaction. And today, perception of the brand is all-important. By leveraging AI and ML, organizations gain actionable insights from the tremendous amount of information streaming into their contact centers every day and drive strategies for coaching and training to help agents be better brand guardians. AI/ML- driven advancements can transform your agents into brand guardians, improving their work behaviors as well as their aptitude and skills. Elevating your contact center agents into brand guardians is not a small undertaking, but the results will speak for themselves.
