Data-Driven Decisions: Using ServiceNow Performance Analytics to Optimize Your Processes

In today’s fast-paced digital landscape, organizations need to make informed decisions quickly and effectively. Guesswork and intuition, while valuable, are no longer sufficient for sustainable success. Data-driven decision-making, the practice of basing decisions on hard data rather than gut feelings, has become paramount. ServiceNow Performance Analytics is a powerful tool that empowers organizations to achieve this by providing visibility into key performance indicators (KPIs), identifying bottlenecks, and enabling continuous improvement of processes.

What is ServiceNow Performance Analytics?

ServiceNow Performance Analytics is a built-in platform feature that provides a comprehensive view of organizational performance across various ServiceNow applications like ITSM, HR Service Delivery, Customer Service Management, and more. It goes beyond simple reporting by enabling you to:

• Collect Data: Automatically collect data from various ServiceNow tables and other sources using data collectors and jobs.
• Analyze Data: Transform raw data into meaningful metrics and KPIs using indicators and breakdowns.
• Visualize Data: Present data in interactive dashboards, scorecards, and reports for easy understanding.
• Monitor Performance: Track performance trends over time, identify deviations from targets, and trigger alerts when needed.
• Take Action: Drill down into underlying data to identify root causes and implement targeted improvements.

Key Components of Performance Analytics:

Before diving into practical applications, let’s define the core components of ServiceNow Performance Analytics:

• Indicators: Quantitative measures that track performance over time. Examples include:
  • Number of resolved incidents
  • Average resolution time
  • Customer satisfaction score
  • Number of HR cases closed
• Breakdowns: Dimensions used to categorize and filter data. They allow you to analyze performance by various factors such as:
  • Assignment group
  • Category
  • Priority
  • Location
  • Department
• Dashboards: Interactive visual displays that present KPIs and trends in a user-friendly format. They provide a holistic view of performance and enable users to drill down into specific areas of interest.
• Scorecards: A summary of indicator performance against targets. Scorecards visually highlight areas where performance is exceeding or falling short of expectations.
• Data Collectors: Scheduled jobs that extract data from ServiceNow tables and other sources.
• Jobs: Configuration items that drive the collection and processing of data for Performance Analytics.

How Performance Analytics Drives Data-Driven Decisions: Practical Examples

Let’s explore how Performance Analytics can be used in different scenarios to optimize processes and improve performance.

1. Improving Incident Management:

Imagine an IT Service Management (ITSM) department struggling with high incident volumes and long resolution times. Performance Analytics can help identify the root causes and implement targeted improvements.

• Problem: High incident volume, long resolution times, and low customer satisfaction.

• Implementation:

  • Indicators:
    • Number of new incidents
    • Average resolution time
    • First call resolution rate
    • Customer satisfaction (CSAT) score for incident resolution
  • Breakdowns:
    • Assignment group
    • Category
    • Priority
    • Configuration Item
  • Analysis: By analyzing the data, the ITSM department discovers that a specific type of configuration item (e.g., network printers) is consistently generating a disproportionately high number of incidents. Furthermore, they find that a particular assignment group is consistently taking longer to resolve incidents.
  • Action:
    • Investigate the root cause of the printer-related incidents. It may be a known hardware defect or a configuration issue. Implement a permanent fix.
    • Provide additional training and resources to the underperforming assignment group. Implement a knowledge base article specifically for the printer issues.
    • Monitor the impact of the changes using Performance Analytics dashboards.

• Result: Reduced incident volume, faster resolution times, improved customer satisfaction.

Flowchart visualizing this process:

2. Optimizing HR Service Delivery:

HR departments can leverage Performance Analytics to improve the efficiency and effectiveness of their service delivery.

• Problem: High volume of HR cases, long resolution times, and inconsistent service delivery across different locations.
• Implementation:
  • Indicators:
    • Number of new HR cases
    • Average resolution time
    • Employee satisfaction (eSat) score
    • Case backlog
  • Breakdowns:
    • Category (e.g., Benefits, Payroll, Onboarding)
    • Location
    • Department
    • Assigned to
  • Analysis: The HR department discovers that onboarding cases are taking significantly longer to resolve in one particular location compared to others. They also find that employees are consistently expressing dissatisfaction with the payroll process.
  • Action:
    • Investigate the onboarding process in the location with longer resolution times. Identify bottlenecks and inefficiencies. Implement process improvements and provide additional training to HR staff.
    • Review and streamline the payroll process. Identify and address common employee complaints. Consider implementing self-service options.
    • Monitor the impact of the changes using Performance Analytics dashboards.
• Result: Reduced case volume, faster resolution times, improved employee satisfaction, and more consistent service delivery across different locations.

3. Enhancing Customer Service Management:

Performance Analytics can help customer service teams improve customer satisfaction, reduce support costs, and increase agent productivity.

• Problem: Low customer satisfaction scores, high call volume, and long wait times.
• Implementation:
  • Indicators:
    • Customer satisfaction (CSAT) score
    • Average call handling time
    • First call resolution (FCR) rate
    • Call abandonment rate
  • Breakdowns:
    • Product
    • Channel (e.g., Phone, Email, Chat)
    • Agent
    • Reason Code
  • Analysis: The customer service team discovers that customers are consistently expressing dissatisfaction with the support provided for a specific product. They also find that calls are being abandoned at a high rate during peak hours.
  • Action:
    • Investigate the issues with the product that is generating low CSAT scores. Involve product development teams to address the root causes.
    • Adjust staffing levels during peak hours to reduce call abandonment rates. Implement a call-back option.
    • Provide additional training to agents on the problematic product. Create knowledge base articles to help agents resolve issues more quickly.
• Result: Improved customer satisfaction, reduced call abandonment rates, and increased agent productivity.

Detailed Reporting with Performance Analytics

Performance Analytics allows for highly detailed reporting, going far beyond basic summaries. Here’s a breakdown of the reporting capabilities:

• Trend Analysis: Track indicator values over time to identify patterns and trends. This helps in understanding whether performance is improving, declining, or remaining stable.
• Comparison Reporting: Compare performance across different breakdowns. For example, compare resolution times for different assignment groups or CSAT scores for different products.
• Threshold Monitoring: Set thresholds for indicator values and receive alerts when performance falls below or exceeds those thresholds. This allows for proactive identification and resolution of issues.
• Forecasting: Use historical data to forecast future performance. This helps in planning and resource allocation.
• Drill-Down Capabilities: Drill down into the underlying data to understand the root causes of performance issues. This enables targeted and effective problem-solving.
• Scheduled Reporting: Automate the generation and distribution of reports to key stakeholders.

Example of a Detailed Report:

Imagine you are analyzing incident resolution times. A detailed report might include:

• Overall average resolution time: A benchmark figure.
• Resolution time by priority: Showing if high-priority incidents are resolved faster than low-priority incidents.
• Resolution time by assignment group: Identifying groups that are struggling with resolution times.
• Resolution time by category: Showing which types of incidents take the longest to resolve.
• Trends over time: Visualizing how resolution times have changed over the past month, quarter, or year.
• A list of incidents exceeding a defined threshold: Incidents that have been open for longer than a specified time, requiring immediate attention.

References

• ServiceNow Performance Analytics Documentation: https://docs.servicenow.com/bundle/vancouver-performance-analytics-and-reporting/page/product/performance-analytics-and-reporting/concept/c_PerformanceAnalytics.html 
• ServiceNow Community - Performance Analytics: https://community.servicenow.com/community?id=community_topic&topic_id=114 

Conclusion

ServiceNow Performance Analytics is a powerful tool that enables organizations to make data-driven decisions and optimize their processes. By collecting, analyzing, and visualizing data, it provides valuable insights into key performance indicators, identifies bottlenecks, and facilitates continuous improvement. From improving incident management and HR service delivery to enhancing customer service, Performance Analytics empowers organizations to achieve greater efficiency, effectiveness, and customer satisfaction. Embracing a data-driven approach through Performance Analytics is essential for success in today’s competitive landscape.