5 Moves CCOs Need to Make with AI for Contact Center Success

Author:

Level AI Team

Last updated:

March 13 2023

Blog /Artificial Intelligence / 5 Moves CCOs Need to Make with AI for Contact Center Success

Contact centers have witnessed a significant transformation in recent years, thanks to the integration of artificial intelligence (AI) technology. Companies and their CCOs (Chief Customer Officers) are turning to AI to improve and enhance various call center functions, including streamlining processes, data analysis, forecasting, agent-customer interactions, and the overall customer experience.

This article discusses five moves that Chief Customer Officers (CCOs) need to make with AI for call center success, including:

AI-driven quality assurance automation
AI-powered contact center and business analytics
AI-powered real-time agent assist
AI-powered agent coaching
Generative AI at scale

The 5 Moves CCOs Need To Make with AI

Move #1: AI-Driven Quality Assurance Automation

Quality assurance (QA) is critical to call center success. Without it, contact center agents may provide less than ideal customer service, leading to dissatisfied customers and lost business. However, implementing call center quality assurance at scale can be time-consuming and resource-intensive, especially when it involves the manual review of call recordings and other interactions between agents and customers.

Call center AI solutions offer powerful tools for automating quality assurance in call centers. Such systems can analyze massive volumes of call center data, including call recordings, transcripts, and customer feedback, to identify areas for improvement and provide real-time feedback to agents.

Some of the benefits of AI in call center quality assurance are:

Data analysis at scale that analyzes 100% of customer data– to isolate common customer pain points and agent shortcomings, providing call center managers with the insights and tools to implement accurate fixes
Reduction of costs associated with manual quality assurance processes, such as the need for additional staff to review call recordings and provide feedback– automating these processes improves call center efficiency and reduces the resources required to monitor agent performance
Increased efficiency through analyzing large volumes of data quickly and accurately– providing real-time feedback to agents and allowing them to address issues more efficiently, reduce call handling time, and improve productivity
Accuracy and consistency of agent feedback reduce the potential for bias or subjectivity in the quality assurance process, ensuring agents receive fair and consistent performance evaluations.

Use Case Example

Substantive Supervision of Call Center Agents: AI can contribute to the substantive supervision of call center agents by analyzing customer interactions and providing real-time feedback to agents.

As a result, call center agents can improve their performance by identifying critical areas like tone of voice, use of empathy, or providing additional information.

Additionally, AI can help supervisors identify high-performing agents and give extra training and coaching to improve their performance.

Move #2: AI-Powered Contact Center And Business Analytics

Today’s highly competitive business landscape requires quick and personalized service, with contact centers playing a critical role in meeting these expectations.

The second move CCOs should consider to achieve this is to leverage AI-powered contact center and business analytics to gain deeper insights into customer behavior and optimize operations.

AI-powered contact center analytics solutions like Levels AI empower businesses to gain a competitive advantage by improving customer satisfaction, reducing costs and increasing operational efficiency.

Some of the benefits of implementing AI in contact center and business analytics are:

Real-time insights into customer interactions allow businesses to identify and respond to customer issues quickly. Real-time analytics also help contact center agents personalize customer interactions, increasing customer satisfaction.
Improved customer experience by assisting businesses to gain a deeper understanding of customer behavior and preferences, generating insights to offer personalized experiences, and boosting loyalty and retention.
Significant cost reductions associated with manual data analysis and quality assurance processes. Automating these processes with AI improves efficiency and reduces the resources required to monitor performance.
Increased efficiency from AI’s ability to analyze large volumes of data quickly and accurately, providing valuable insights to businesses in real-time and at scale. Such insights enable companies to optimize operations and increase efficiency, reducing customer wait times and improving agent productivity.

Use Case Example

Churn Prediction: AI can contribute to churn prediction in call centers by analyzing customer behavior and identifying patterns that may indicate a customer is likely to churn.

By analyzing customer interactions and feedback, AI can identify critical factors contributing to customer churn and provide call center agents with insights and recommendations to prevent it, for example, by offering personalized incentives or addressing customer complaints more quickly.

Move #3: Real-Time Agent Assist

Efficient call centers are critical to business success because they are pivotal in managing customer relationships and cultivating customer satisfaction.

Similarly, accurate and timely agent interventions can help avoid frustrated customers and missed sales opportunities and prevent damage to a company’s reputation.

AI-powered real-time call center agent assist enables better service, improved customer satisfaction, and increased agent productivity.

It provides call center agents with real-time insights and guidance during customer interactions using algorithms that analyze customer interactions, identifies patterns, and generates automated advice and suggestions.

CCOs can expect the following benefits from implementing AI-powered real-time agent assist:

Enhanced scalability by handling vast numbers of customer interactions, enabling businesses to scale call center operations without increasing staffing levels.
Increased efficiency by automating manual tasks such as data entry and call disposition, reducing agent workload and increasing productivity.
Improved customer experience by providing call center agents with real-time guidance and suggestions, enabling them to provide personalized and timely customer responses, resulting in increased customer satisfaction.
Cost savings due to a reduced reliance on many human supervisors and quality assurance analysts.

Use Case Example

Intelligent interactive voice response (IVR) System: AI can contribute to an intelligent IVR system by analyzing customer interactions and using machine learning to improve the accuracy of automated responses.

This can help call centers reduce wait times and improve customer experience. For example, an intelligent IVR system can recognize returning customers and provide them with personalized options that match their previous interactions.

Move #4: AI-Powered Agent Coaching

Traditional agent coaching methods, such as one-on-one coaching sessions and post-call reviews, can be time-consuming, resource-intensive, and subjective. In many cases, issues go unnoticed or unresolved due to scalability issues.

AI-powered agent coaching addresses these limitations by analyzing and providing real-time agent performance feedback, providing objective and real-time insights that enable them to make immediate improvements and enhance their performance.

Some performance metrics AI-powered agent coaching analyzes are call quality and duration, sentiment analysis (e.g., mood detection), customer satisfaction (e.g., retention and churn), and compliance with call center policies.

When implemented, CCOs can expect these benefits:

Personalized feedback to agents based on per-agent criteria like call quality, tone of voice, and adherence to call center policies. Providing real-time feedback and suggestions for improvement at this scale helps agents identify strengths and weaknesses and informs them of immediate actions to enhance performance.
Continuous improvement of agent performance based on ongoing feedback and suggestions for improvement enables agents to make incremental improvements to their performance over time.
Agent productivity rises when agents receive real-time feedback and suggestions, enabling them to address issues promptly and effectively during customer interactions, resulting in fewer call transfers, reduced call times, and improved first-call resolution rates.
AI-powered agent coaching can significantly lower call center costs by reducing the number of training and coaching sessions required to upskill agents after the fact. Additionally, improved agent performance can lead to higher customer satisfaction rates, driving down churn rates and providing upsell opportunities.

Use Case Example

Empathetic CX: AI can analyze speech and sentiment data to identify the mood of customers, helping call center agents tailor their approach to better match the customer’s perspective. For example, if a customer is angry or frustrated, an AI-powered system can alert the agent to take a more empathetic approach and offer a solution tailored to the customer’s specific needs.

Move #5: Generative AI at Scale

Generative AI is artificial intelligence capable of generating original content, such as text, images, or music.

Unlike traditional AI systems designed to recognize and classify existing data, generative AI uses machine learning algorithms to create new content based on learned patterns and data it has learned.

Generative AI for call centers can offer personalized responses to customer inquiries, automate specific tasks, and improve overall efficiency and customer satisfaction.

For CCOs considering deploying generative AI in their call centers, these are some of the value-based outcomes expected:

Improved customer service by delivering faster and more accurate responses to customer inquiries. It can also help personalize the customer experience by analyzing customer data to provide tailored recommendations and solutions.
Increased efficiency by automating repetitive tasks such as data entry, appointment scheduling, and simple queries, freeing call center agents to focus on more complex and high-value jobs.
Reduced staffing and operational expenses costs due to more queries being handled by AI. Additionally, such solutions can help reduce the need for manual quality assurance and training, further lowering costs.
Enhanced agent performance by providing real-time guidance and support to call center agents, helping them deliver better customer service and improving performance. Automated QA can also identify areas for improvement and provide personalized coaching and training to improve agent skills.

Use Case Examples:

Customer Support Automation

Automating customer support processes like ticket routing, status updates, and information retrieval help reduce response times and improve overall customer satisfaction.

Predictive Analytics

Analyzing all customer data to identify trends and patterns in customer behavior and preferences allows call centers to deliver personalized customer experiences.

Quality Assurance

Generative AI can analyze recordings and transcripts to provide real-time feedback and coaching to call center agents, helping them improve their performance and ensuring quality standards are met.

Conclusion

As the AI space continues to accelerate, businesses are increasingly angling for ways to turn the newest technological advances available into a competitive advantage. CCOs implementing these five moves utilizing AI for the contact center will set their companies up for success to deliver much improved customer experiences.

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