KPIs (Key Performance Indicators) in call center automation are crucial in delivering superior customer service and enhancing operational efficiency. By understanding and leveraging the right call center KPIs for automation, managers can make data-driven decisions that significantly improve automation performance and customer satisfaction.
Understanding Call Center KPIs
Call Center KPIs are quantitative measures that assess the performance and efficiency of a call center. These KPIs are vital in customer interactions and play a pivotal role. Real-time data tracking of these KPIs, including customer satisfaction, agent performance and KPIs for automation, helps call centers to improve when working with call center automation.
Top Call Center KPIs in Call Center Automation
Several key KPIs are essential for monitoring call center automation. These KPIs include:
Automated Contact Resolution Rate
What is Automated Contact Resolution Rate? Automated Contact Resolution Rate measures the percentage of customer inquiries that are resolved through automated systems without escalation to a human agent. It quantifies the effectiveness of automation and AI technologies in resolving customer issues, indicating how well these systems meet customer needs independently.
Average Speed of Answer by AI Systems
What is Average Speed of Answer? Average Speed of Answer by AI Systems tracks how quickly automated systems, like IVR or voice chatbots, respond to customer inquiries. This KPI assesses the responsiveness of AI-driven services, highlighting the efficiency of technology in delivering timely customer support.
AI-Assisted Upselling/Cross-selling Conversion Rate
What is AI-Assisted Upselling/Cross-selling Conversion Rate? AI-Assisted Upselling/Cross-selling Conversion Rate measures the effectiveness of AI tools in identifying and converting upselling or cross-selling opportunities during customer interactions. It reflects the capability of AI technologies to enhance sales strategies within call centers by leveraging data and customer interaction insights.
Call Abandonment Rate
What is Call Abandonment Rate? Call Abandonment Rate tracks the rate at which callers disconnect before reaching an agent.
Call Containment
What is Call Containment? Call Containment refers to the ability of automated systems, such as Conversational IVR, to resolve customer queries within the automated system without transferring to a live agent. This KPI highlights the effectiveness of automation in managing and resolving customer inquiries, reducing the need for human intervention and optimizing resource allocation.
Call Completion Rate
What is Call Completion Rate? Call Completion Rate reflects the proportion of successfully connected and completed calls, indicating operational efficiency.
Call Deflection
What is Call Deflection? Call Deflection measures the percentage of incoming calls redirected away from live agents to other channels, like self-service options or AI chatbots. It indicates the call center’s success in utilizing digital channels to manage inquiries, aiming to reduce live agent workload and operational costs.
Call Routing Accuracy
What is Call Routing Accuracy? Call Routing Accuracy evaluates how precisely customer intents are identified and routed to the appropriate department or agent. This shows the efficiency of system effectiveness, directly impacting customer loyalty and the overall quality of service.
Chatbot Conversation Success Rate
What is Chatbot Conversation Success Rate? Chatbot Conversation Success Rate evaluates the effectiveness of chatbots or voice chatbots in handling customer inquiries, based on the proportion of conversations successfully resolved. This KPI assesses the quality and effectiveness of AI-driven chatbot interactions in resolving customer needs without escalation to human agents.
CSAT – Customer Satisfaction
What is Customer Satisfaction with Automated Services? Customer Satisfaction with Automated Services measures customer satisfaction specifically with automated interactions, separate from overall CSAT scores. It reflects how well automated services and AI meet customer expectations and needs, crucial for assessing the impact of technology on the customer experience.
Error Rate of AI Interactions
What is Error Rate of AI Interactions? Error Rate of AI Interactions quantifies the accuracy of AI systems by measuring the percentage of interactions that result in errors or misunderstandings. In AI this is often referred to as AI accuracy. This KPI is essential for evaluating the effectiveness and reliability of AI technologies in understanding and processing customer inquiries accurately.
FCR – First-Contact Resolution (FCR) Rate for Automated Services
What is FCR for Automated Services? First-Contact Resolution Rate for Automated Services focuses on the percentage of issues resolved in the first interaction through automated systems alone. It highlights the effectiveness of automated technologies in resolving customer queries at the first point of contact, contributing to overall customer satisfaction.
Net Promoter Score (NPS)
What is NPS? NPS is a crucial metric for gauging customer advocacy and loyalty.
Self-Service Usage Rate
What is Self-Service Usage Rate? Self-Service Usage Rate measures the proportion of customers who use self-service options compared to those seeking live agent support. This KPI indicates the effectiveness and customer preference for self-service tools, reflecting how well these options meet customer needs.
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Using Call Center Automation KPIs to Drive Improvement
Monitoring call center KPIs enables managers to make informed decisions in areas such as workforce management, process optimization, and enhancing customer experience. There is a key tactic to improve one of the most important (and costly) KPI´s in call center automation and that is to improve FCR in voice AI (also known as voice chatbots, IVR and Conversational IVR).
Impact of improving FCR, First Call Resolution
To improve FCR in automation, accuracy across customer self-service is very important. Accuracy is a metric that shows how well a system understands humans. Let’s show this as an example. Improving the FCR rate from 70% to 95% in a contact center with a monthly call volume of 1,000,000 can lead to substantial savings of $15,000,000 annually.
ROI 70% FCR rate:
- Monthly Call Volume: 1,000,000 calls
- Average Cost per Call: $5
- Calls Resolved on First Contact: 70% of 1,000,000 = 700,000
- Calls Requiring Follow-up: 300,000
- Total Monthly Cost: 1,000,000 calls * $5/call = $5,000,000
ROI 95% FCR Rate:
- Improved FCR Rate: 95%
- Calls Resolved on First Contact: 95% of 1,000,000 = 950,000
- Calls Requiring Follow-up: 50,000
- Reduction in Follow-up Calls: 300,000 (initial) – 50,000 (follow-up calls) = 250,000 calls
With a 95% FCR rate, a call center automation improvement equals $1,250,000 in monthly savings and $15,000,000 per year. The savings not only underscore the FCR operational efficiency gains but also enhance customer satisfaction through more effective first-contact resolutions.
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Real-World Application of Call Center KPIs
Circle K collaborated with Teneo to develop a voice chatbot in multiple languages. The chatbot significantly improved customer service by handling a wide range of requests and questions, demonstrating Teneo’s scalability and flexibility. The solution, named ‘Kay’, covers Swedish, Norwegian, and Danish, and is integrated with MS Teams for internal use. It led to a 57% increase in the volume of voice requests handled and a 95% increase in project speed delivery. This case exemplifies how conversational AI can enhance customer satisfaction and operational efficiency in retail.
How to use AI in KPI Tracking
AI technologies facilitate real-time monitoring and provide valuable insights, enabling managers to make data-driven decisions for optimizing call center performance. Looking ahead, the future of call center KPIs is poised to be significantly influenced by technological advancements, particularly in AI and multi-channel support. AI is transforming customer interaction analysis, offering deeper insights, more accurate predictions and if a great system, at least a 95% resolution rate.
Call Center Automation FAQs
Is Accuracy an important KPI for IVR?
Accurate voice routing in call centers is a critical KPI for automation because it ensures that customers questions are resolved. This enhances the overall customer experience by reducing wait times and frustration, in self-service and resolution in the first call. Effective routing with an accuracy of 95% with Teneo Accuracy Booster helps optimize the use of agent resources and can lead to increased operational efficiency in the call center.
What is Customer Effort Score (CES)?
The Customer Effort Score (CES) is a vital measure in assessing service quality in call centers. It gauges how much effort a customer must exert to have their issue resolved, request fulfilled, or question answered. A lower CES implies that the service is efficient and customer-friendly, enhancing overall satisfaction. By tracking CES, call centers can identify areas where the customer experience can be streamlined, thereby improving service quality and potentially increasing customer loyalty.
How can Generative AI improve a call center KPI?
Generative AI, including tools like Teneo RAG, play a crucial role in enhancing the tracking and effectiveness of call center KPIs by enabling more sophisticated data analytics and insights. Generative AI help in understanding and predicting customer behavior, streamlining processes, and providing actionable insights to improve overall call center performance. This leads to a more efficient operation, where customer needs are met more effectively, and key performance indicators are continuously monitored and optimized for better service quality.