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9 New Toolkits With Salary and Skills Data to Focus Recruiting Efforts

January 9th, 2017 Comments off
candidate search

Whether you’re a recruiting novice or a seasoned sourcing pro, starting a new candidate search can be overwhelming. You want to be targeted with your search, yet you don’t always have the time or resources to spend on pulling data or perfecting your sourcing techniques. But if you go into a search unprepared, you may end up spinning your wheels – and taking a long time to fill the position.

So, how can you better focus your recruiting efforts? By starting your candidate search with a few key insights and shortcuts:

  • Average earnings data: It’s not enough to just know what your company has historically offered in terms of compensation for the open position. By having median earnings data for the specific occupation as well as other similar occupations, you’ll get a better sense of what your competitors are paying and how your compensation compares.
  • Top skills: What are the hard and soft skills to look for in a candidate for your open jobs? Having a list of desired skills on-hand while sourcing candidates will help you more easily narrow down your pool of prospects.
  • Boolean search basics: If you want to quickly and effectively source candidates for your open positions, you need to know how to perform a Boolean search.

New Industry-Specific Hiring Toolkits Available Now

Here is the good news: CareerBuilder has done a lot of this legwork for you. We have created nine industry-specific hiring toolkits filled with key earnings and skills data and Boolean shortcuts to save you time and help you hire the best talent more effectively. You’ll find toolkits for the following industries: sales, retail, light industrial, IT, insurance, hospitality, health care, engineering and transportation.

Download your industry-specific toolkit today

The Benefits of Algorithmic Candidate Recommendations

November 29th, 2016 Comments off
Algorithmic recommendations

Do you have a hard-to-fill job? Not enough qualified applicants? You could search through a candidate database. Or, even better, save yourself the work and rely on algorithmic candidate recommendations. This is the message from a newly published paper in the Journal of Labor Economics by my economist colleague John Horton, from New York University’s Stern business school.

Algorithmic recommendations can increase the job filling rate by 20 percent

John collaborated with the ODesk (now Upwork) online work platform to do some experimentation in 2011. Employers were randomly assigned to a control group (business as usual) and to a treatment group (algorithmic recommendation). Without the recommendation, employers can either look at candidates who applied to their jobs, or search candidates on the ODesk database. With the recommendation, employers got up to six candidate recommendations based on their job opening. Employers with technical jobs (e.g., web programming and mobile development) typically had a hard time filling their vacancies, but the job filling rate was 20 percent higher for those who saw candidate recommendations.

Interestingly, candidate recommendations did not dissuade employers from hiring other non-recommended workers. This shows that candidate recommendations were not duplicating employer efforts, but were a real value added to employers.

Algorithmic recommendations are especially effective for jobs with few applicants

It is interesting that, in the ODesk experiment, algorithmic recommendations only had an effect for technical jobs and not for nontechnical jobs. When digging deeper into the data, John found that part of the difference could be explained by the fact that nontechnical jobs already had plenty of applicants. It is for the hard-to-fill technical jobs that the algorithmic recommendations were more effective, increasing job filling rates.

Recommended candidates are as good as employer-sourced candidates

When comparing recommended candidates and those sourced by the employer themselves, John found that recommended candidates were just as skilled and performed just as well on the job. Therefore, employers can rely on algorithmic recommendation to do at least as good of a job as themselves when sourcing candidates. This is especially remarkable given that the recommendation algorithm used at the time of the experiment (2011) was quite simple, and more recent algorithms are much more sophisticated.

For example, CareerBuilder now has a Talentstream match product that uses advanced natural language processing techniques to match jobs with the most suitable candidates. The product is currently available to staffing companies and other companies that use the Bullhorn ATS. The search engine allows employers to search not only in their candidate database but also across other sources like LinkedIn. Furthermore, search can be refined by specifying the relative importance of different skill requirements.

In conclusion, if you regularly find yourself with hard-to-fill jobs, you could benefit from algorithmic candidate recommendations.

Get in touch with your sales representative to learn more.

 

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