China Gorman, in a recent post titled Quality of Hire: A Vaguely Valid Metric? offers a broad overview of approaches to measuring the quality of hire by global regions. Taking that broad approach to a very local business discipline might be the contributing factor to her assumption about vagueness.
Quality of hire is not a singular, standalone metric. Just as new hire performance is not one data point. But there is a systems approach to documenting the way new hires demonstrate their value to the organization.
China’s article cites three metrics: Hiring manager satisfaction, turnover, and performance evaluation.
- Satisfaction can be very subjective and may not be related to actual performance.
- Turnover is more related to the hiring decision, so it is a quality of decision metric, not of the hire.
- Performance evaluation from common corporate practices does a poor job of documenting differences among people.
Documenting quality of hire requires a measurable and meaningfully different methodology based upon rigor, objectivity, and fairness. We use an approach that defines and documents the quality of hire by capturing data in three categories: Opinions, Observed Behaviors, and Objective Metrics. We affectionately refer to these as the Three Os of Quality of Hire. Let’s explore each one in more detail.
Opinions use a structured rating form to capture the hiring manager’s point of view according to several key standards, including:
- Top performer
- Among the best ever hired
- Would rehire
- Learns faster than others
- Observed Behaviors
Observed Behaviors rely on a behaviorally anchored rating scale based on the job’s competency model. Capturing a high level of detail for each competency is critical. These are examples of observed behaviors based on the competency of decision making:
- Involves others in making decisions
- Uses data to inform decisions
- Considers risk in making decisions
- Develops contingency plans when making decisions
- Able to make timely decisions
- Objective Metrics
Objective Metrics are performance data captured as a number and common across all performers in the job. These can be time-bound, showing progress to performance targets or point in time data to document a level of achievement. Calls per hour, sales, up-sell, cross-sell, and after call work are a few metrics common to phone-based sales positions.
To make a change in the quality of hire requires a change in the data used to make the decision. Therefore, recruiters need talent analytics that document the relationship among candidate evaluation metrics and the Three Os. Below are two examples. One is a case study that documents the differences in the Three Os of new hire metrics based upon their evaluation scores. The other example documents differences in performance related to learning and productivity. These data present the facts that the candidate evaluation is able to predict observable, measurable differences in how a new hire performs on the job.
Predictive modeling is an analytic method that documents the relationship among the variables you measure in a candidate and the variables you collect about their contribution and on-the-job behaviors.
If you would like your quality of hire metrics to go from vague to valid, talk to us. It’s what we do, every day of the year.