A multi-site financial services organization with client assets over $250 billion is focused on hiring front-line customer service representatives that will perform better in production. Several specific goals were highlighted during the needs analysis:
- Better evaluate job candidates prior to hire for performance potential across two critical metrics (average handle time and customer satisfaction)
- Reduce new hire attrition by hiring candidates with the greatest potential for long-term job success;
- Develop a best in class, data-driven hiring model in line with key contact center performance goals
Based on the job reviews conducted by FurstPerson, two primary assessments are being used to predict job performance. These two assessments are the CC AIMS which measures work attitudes, and CC Audition® which measures work ability. Using these assessments as the core pre-hire evaluation tools, FurstPerson conducted a validation analysis with current job incumbents to establish the baseline scoring model based on the client’s critical performance metrics.
From there, the web-based hiring model was established.
Using the client specific scoring model developed during the validation phase, FurstPerson was able to determine that the assessments do demonstrate that the two primary assessments are predicting job performance. Candidates who score higher on the CC AIMS and CC Audition outperform the baseline performance provided by the client. The chart below demonstrates the performance improvement for Customer Satisfaction (CSAT) and Average Handle Time (AHT).
In summary, the client was able to improve the candidate experience by deploying a web-based, automated hiring process that resulted in a more accurate hiring decision. The validation analysis provided the right foundation to create a specific scoring model for the client that allowed the client to select job candidates who performed better in two critical performance metrics for the client.