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Performance-based Hiring Learning Systems: Implementing Performance-based Hiring

Lou Adler , Founder & CEO
Having worked in the recruitment industry for over 40 years, Lou Adler, the founder and CEO of Performance-based Hiring Learning Systems discovered that a great candidate for a job vacancy is not always a good hire. Adler contends that it doesn’t matter whether an applicant’s skills and first impression make a positive impact or not. What matters is the person’s track record of past performance doing comparable work. Worse, too many companies today employ a hiring process that is little more than a collection of independent steps glued together that too often excludes some of the best and most diverse talent available. As a result of these on-going challenges, Adler developed Performance-based Hiringsm as a means to find, attract, interview, and recruit top-tier talent for any job. The program is designed for recruiters, hiring managers, and anyone who is part of the interviewing process.

“Our new online learning platform addresses the entire hiring process from beginning to end including on boarding and performance management,” says Adler.

Performance-based Hiring is based on a scarcity of talent strategy that involves attracting the best people with the primary goal of improving the quality of hire. Adler contends this approach is essential when the demand for strong talent exceeds the supply. The scarcity model for selecting candidates focuses on defining success as a series of performance objectives and attracting the right people with targeted and compelling messages. This small-batch, high-touch process allows companies to enhance their ability to attract high potential talent and increase their interviewing accuracy by assessing the person’s past performance in comparison to the actual performance requirements of the job.
The four-step Performance-based Hiring process consists of creating a performance-based job description, sourcing highly-qualified prospects, conducting a performance-based interview, and negotiating the terms of an offer emphasizing career growth rather than trying to maximize the compensation package.

“We often initiate the performance-based hiring process by conducting a mini-audit of the client’s hiring process to benchmark the company against best practices. As part of this, we also review the company’s job descriptions and postings, the skill set of the recruiters, and the quality of the interviewing process,” explains Adler. “Doing it right is essential to attract, assess, and hire the best people.” After conducting this audit, the company develops customized training, and processes that best meet the company’s hiring needs and capabilities.

The company provides three types of training, including, online, onsite, and a newly developed self-paced learning platform. These options allow companies to train their hiring managers and executives in order to implement the Performance-based Hiring process on a just-in-time basis.

Performance-based hiring has been making great strides in the recruitment industry by revamping the hiring process of several organizations. One of their many success stories is Paycom, an organization that specializes in HR and online payroll services. By implementing Performance-based Hiring, Paycom was able to change its traditional HR processes by training their hiring managers on how to write job descriptions, and via comprehensive training, Paycom was also able to increase interviewing accuracy. This resulted in stronger candidates being seen and hired. “Our solution has had a major impact on organizations of all sizes. We have helped firms ranging from start-ups to small and large-sized organizations such as Lincoln Financial Group using a train-the-train model to train hundreds of hiring managers over the past few years,” explains Adler.

Continuing on this path of success, Performance-based Hiring Learning Systems envisions addressing all of the challenges related to hiring top talent in a talent-scarce market. This starts by shifting the focus from hiring great candidates based on their personality and presentation skills to hiring them based on their past performance and future potential. And with the Hiring Machine learning platform, this can now be done at scale.

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