How to Analyze AI Phone Screening Metrics in 30 Minutes (2026)

In 2026, the landscape of talent acquisition has evolved significantly, with AI phone screening becoming a crucial tool for efficiency and effectiveness. Surprisingly, 75% of organizations still struggle to analyze their AI screening metrics effectively. This quick guide will help you, as a Director of Talent Acquisition, to glean actionable insights from your metrics in just 30 minutes, optimizing your hiring process.

Prerequisites for Quick Analysis

Before diving into metrics analysis, ensure you have the following:

• Access to Your ATS: Ensure you have admin rights to access the reporting features.
• AI Phone Screening Tool: Confirm your organization uses a reliable AI phone screening solution, such as NTRVSTA, which integrates seamlessly with popular ATS platforms like Greenhouse and Bullhorn.
• Time Estimate: Allocate a solid 30 minutes for this analysis.

Step-by-Step Metrics Analysis

Step 1: Gather Your Data

Access your AI phone screening tool’s dashboard to gather the following metrics:

• Candidate completion rates
• Average screening time per candidate
• Drop-off rates at various stages
• Scoring metrics for resumes and interviews

Expected Outcome: A comprehensive overview of key metrics that indicate the effectiveness of your screening process.

Step 2: Analyze Candidate Completion Rates

Review your completion rates, which should ideally be above 90%. If it's lower, identify factors causing candidate drop-offs.

Expected Outcome: Understanding of where candidates disengage, allowing for targeted improvements.

Step 3: Evaluate Average Screening Time

Calculate the average time taken for screening. If your AI tool reduces screening time from 45 to 12 minutes, make sure this is reflected in your metrics.

Expected Outcome: A clear picture of efficiency gains and potential areas for further reduction.

Step 4: Investigate Drop-off Points

Dive deeper into the drop-off rates. If you notice a significant drop-off at the resume scoring stage, it may indicate issues with the AI’s parameters or candidate qualifications.

Expected Outcome: Identification of specific stages needing refinement or additional support.

Step 5: Review Scoring Metrics

Examine the scoring metrics provided by your AI tool. Look for trends in resumes flagged for fraud or discrepancies, which could indicate a need for better credential verification processes.

Expected Outcome: Insights into the quality of candidates progressing through the pipeline.

Troubleshooting Common Issues

1. Data Discrepancies: Ensure all systems are properly integrated and syncing correctly.
2. Low Completion Rates: Review candidate feedback to address barriers in the screening process.
3. High Drop-off Rates: Analyze the AI's questioning strategy; simplify if necessary.
4. Inconsistent Scoring Results: Adjust scoring parameters based on historical data and candidate performance.
5. Integration Problems: Work with your IT team to ensure all tools are appropriately linked.

Timeline: Most teams complete this analysis in 30 minutes, but resolving issues may take additional time depending on complexity.

Conclusion: Actionable Takeaways

1. Monitor Completion Rates: Regularly track and address any significant drop-offs.
2. Optimize Screening Time: Aim for continuous improvement in screening efficiency.
3. Adjust Scoring Metrics: Use insights from scoring to refine candidate qualifications.
4. Integrate Feedback Loops: Establish processes for candidate feedback to enhance the screening experience.
5. Leverage Real-Time Data: Utilize tools like NTRVSTA to maintain a competitive edge with real-time analytics and insights.

By implementing these actionable steps, you can ensure your AI phone screening process is not only efficient but also effective in identifying the best candidates for your organization.