In customer service, top-performing companies understand the paramount importance of having a comprehensive view of the customer journey. However, a startling fact remains: fewer than 10% of companies possess a 360-degree view of their customer journeys. This deficiency directly impacts contact center performance, leading to suboptimal results in key performance indicators (KPIs) such as quality score, average handle time, and contact resolution rates.
Addressing this gap is crucial for businesses aiming to enhance contact center experience and customer experiences, stimulate business growth, and optimize contact center operations. Leveraging contact center analytics emerges as a powerful solution in this pursuit, providing actionable insights to drive positive business outcomes.
Types of Analytics in Customer Service
Contents
- Types of Analytics in Customer Service
- Six Principles for Smarter Business Decisions through Analytics
- Best Practices for Contact Center Analytics
- Key Contact Center Metrics
- 1. Abandoned Call Rate (ACR)
- 2. Agent Utilization
- 3. After-Call Work (ACW)
- 4. All Trunks Busy (ATB)
- 5. Average Call Transfer Rate (ACTR)
- 6. Average Handle Time (AHT)
- 7. Average Speed of Answer (ASA)
- 8. Agent Schedule Adherence (ASA)
- 9. Available Time
- 10. Average Delay of Delayed Calls
- 11. Average Sales per Agent
- 12. Call Completion Rate (CCR)
- 13. Cost per Call (CPC)
- 14. Call Center Shrinkage
- 15. Call Arrival Rate
- Conclusion
1. Contact Center Analytics: Analyzing standard contact center metrics to pinpoint trends, impacts, causes, and outcomes.
2. Customer Analytics: The analysis of multiple customer-related data sources, including the voice of the customer, behavior data, demographics, and purchase data, to identify customer trends and interaction opportunities.
3. Speech Analytics: Analyzing transcribed voice and text interactions, along with metadata, to uncover trends, customer sentiments, performance influencers, and other valuable insights.
4. Predictive Analytics: Leveraging big data, artificial intelligence, sophisticated algorithms, and machine learning methods to forecast future outcomes from past data. This involves predicting customer lifetime value, foreseeing consumer behavior trends, and pinpointing at-risk customers who may churn.
Six Principles for Smarter Business Decisions through Analytics
1. Get to the Root of the Interaction
– Utilize root cause analytics to highlight fundamental issues, reduce overall interaction volume, call times, and increase customer satisfaction.
2. Intelligent Self-Service
– Implement advanced self-service tools powered by artificial intelligence and machine learning to provide accurate responses, reducing the number of calls.
3. Empower Frontline Contact Center Associates
– View contact center associates as brand stewards, empowering them with emotional intelligence, technology, and data to excel in critical moments and drive positive outcomes.
4. Discover the Right Channels
– Use analytics tools to determine the appropriate channels for customer interactions, considering customer preferences, profiles, interaction complexity, and moments of truth.
5. Follow and Inform the Customer Journey Across Channels
– Break down contact center silos by deploying scalable, cloud-based analytics software. This solution offers real-time and batch data ingestion to offer a comprehensive view of the customer journey.
Read more: If you’re looking for cloud-based customer service solutions that also analyze data, then learn about CCaaS (Contact Center as a Service).
6. Pre-empt Customer Interactions with Predictive Analytics
– Use predictive analytics to identify patterns and predict interaction reasons before customers reach out, reducing interaction volume and improving customer satisfaction.
Best Practices for Contact Center Analytics
1. Don’t Mistake the Part for the Whole
Data serves as a gateway to valuable insights but can also pose a challenge in finding solutions. Contact center analytics software, designed to analyze both structured and unstructured data, empowers employees to articulate information effectively and drive data-informed decisions.
2. Seek Assistance from Contact Center Analytics Experts
Data-rich firms may benefit from assistance in maximizing insights. Customer-focused call center analytics companies can consolidate both digital and offline customer data, forming a comprehensive understanding of customer requirements, actions, and inclinations.
3. Prioritize Insights with a Framework
Instead of pursuing broad goals for better insights, prioritize using a framework. Evaluate team needs, immediate versus future results, available data/channels/sources, and identify areas that require attention.
4. Ensure Employee Readiness to Act
Valuable insights are meaningful when they lead to action. It’s crucial to prepare employees to integrate analysis, data, and evidence-based reasoning into their decision-making.
Key Contact Center Metrics
1. Abandoned Call Rate (ACR)
– Identify and address issues causing high ACR through practices like announcing estimated wait time and playing engaging music during wait times.
2. Agent Utilization
– Measure agent productivity by calculating the ratio of calls answered within the first minute to the total number of calls answered.
3. After-Call Work (ACW)
– Streamline post-call processing by using a well-designed CRM system, training agents to multitask, and optimizing internal processes.
4. All Trunks Busy (ATB)
– Control the All Trunks Busy metric by deploying more agents, building a task force, and allowing customers to connect with specific agents directly.
5. Average Call Transfer Rate (ACTR)
– Improve ACTR by training agents to handle multiple types of calls, providing access to knowledge databases, and focusing on first-call resolution.
6. Average Handle Time (AHT)
– Enhance AHT by improving agent training, monitoring calls, accurate call routing, and providing a rich knowledge base.
7. Average Speed of Answer (ASA)
– Optimize ASA to improve customer satisfaction, reduce abandonment rates, and enhance overall efficiency.
8. Agent Schedule Adherence (ASA)
– Increase ASA by leveraging tracking tools, using annualized breaks, rewarding agents for adherence, and communicating consequences for non-adherence.
9. Available Time
– Use available time metrics to ensure a balance between agent workload and work-life balance.
10. Average Delay of Delayed Calls
– Measure and manage the average duration of delayed calls for efficient allocation of resources and improved customer experience.
11. Average Sales per Agent
– Gauge agent performance by tracking the average number of sales closed by each agent within a specified period.
12. Call Completion Rate (CCR)
– Monitor CCR to ensure successful call connections and identify factors influencing completion rates.
13. Cost per Call (CPC)
– Reduce CPC through quality training, call monitoring, effective scheduling, proper management, and the use of knowledge base management systems.
14. Call Center Shrinkage
– Address both internal and external shrinkage to maximize agent efficiency and minimize wasted time.
15. Call Arrival Rate
– Gauge call arrival rate to anticipate service requests, prepare agents for increased load, and ensure customer experience and brand reputation are not compromised.
Conclusion
In customer service, contact center analytics emerges as a strategic tool to unlock business success. By understanding and implementing the best practices outlined in this comprehensive guide, organizations can harness the power of analytics to drive meaningful insights, optimize operations, and elevate both agent and customer experiences. The journey towards a 360-degree view of customer interactions is not only a competitive advantage but a necessity for businesses aspiring to thrive in the era of customer-centricity.