“Quality of Hire” is one of those terms – like “engagement” – that we all use and all mean different things when we use it. And there is no standard definition. Directionally, we’re probably all in the same ballpark. But there is no precise, function-wide, commonly agreed-upon, global definition.
That’s why I read with interest Joe Murphy’s Quality of Hire: Data Makes the Difference. It was published by Wiley in the Summer 2016 issue of Employment Relations Today.
Joe believes that Quality of Hire is not an abstraction or a myth. He believes that “It is a practical measure, comprising core talent acquisition processes and hiring outcome variables. Its factors can be identified, tracked, and reported in both qualitative and quantitative terms.” And then he shows how.
There’s a wealth of critical information in this article if you are not really comfortable with analytics – including predictive analytics. It breaks it down simply. I like the Talent Analytics Maturity Model and the way it is introduced:
There are 4 phases in the model that progressively advance in terms of the analytics
Primitive
“Primitive analytics is the use of simple methods to organize random, text-based data.” Like that from a resume.
Evaluative
“Evaluative analytics is the mathematical analysis of relevant data.” Assigning numerical values to experience, or skills, or employers and adding them up.
Speculative
“Speculative analytics involves the complex analysis of largely random data and some element of relevant work-related data.” Like that from analyzing “verbal responses, converting spoken words to text to explore patterns and relationships.”
Predictive
“This method is characterized by experiment design and the conducting of correlational analysis with two or more sets of highly structured, job-relevant data.” These can be collected through work product samples and surveys about experience and work style.
The bottom line is this:
The growing use of data and analytics in all stages of the hiring process helps companies make more educated decisions about the people they hire and lessen the randomness of personal judgement in making these hiring decisions.
Moving beyond trying to make sense of random data (like resumes, LinkedIn profiles and notes from an interview) to using relevant data and advanced analytics really will make a difference in hiring outcomes and improve the quality of your hiring. Take a look at this article. Joe does a great job of making the case for the use of analytics to improve quality of hire – and to do it consciously and continuously.
chinagorman.com 