Recruiting with Big Data: Finding Your Best Candidates through Statistical Models & Predictive Analytics
In the Trenches with Search & Big Data – A Blog & Video Series
Watch the video story on how our multinational recruiting and staffing customer uses big data to search, match, and score the best candidates, reducing fill time by 50% and boosting fill rate by 6%.
A Recruiter’s Quest for the Best Talent
Finding the right candidates quickly is an enormous competitive advantage in the recruiting and staffing sector. Now with the growth of recruiting channels, job complexity, and candidate diversity, recruiters are truly in a race to identify and hire talents before competitors snatch them.
Modern recruiters may be "armed" with HR and recruiting software that could bring efficiency and results. But the rapid expansion of candidate and job databases may become a challenge when timeliness and accuracy are essential. The traditional, widely-used Boolean searches might no longer be efficient or comprehensive enough to help recruiters uncover the most qualified candidates from the massive pool of talents, even if those candidates' profiles exist in the recruiters' databases.
No More Guess Work – Knowing Your Candidates’ Success Metrics with Big Data
Let's put ourselves in the shoes of recruiters or hiring managers and consider the effort it'd take to get a candidate on board. Then the candidate's likelihood of success in the new position would really be your key evaluation factor, wouldn't it?
And so, will that resume be the best indicator of a candidate's success? Sure, it's still a valuable quick glance, but scanning resumes for keywords alone can't provide sufficient success metrics. Think of the employees in your organization – do you assess their success by looking at a 360-degree view or just what's on their resumes? Given the time constraints, the large amount of data, and the challenge of evaluating a candidate's holistically, our recruiting customer started leveraging statistical models and predictive analytics to:
- Look beyond keywords and into semantic analytics for both structured and unstructured content, extracting metrics like industries, companies, job titles, skills, experiences, certifications; and then compare them to the job descriptions.
- Further evaluate a candidate's "fit" from a recruiter's perspective, including past hiring data, preferences on the successful candidates, salary levels, and other unique organizational metrics.
This big data recruiting framework processes all that granular data to automatically score and present the top candidates with the highest probabilities of success in the position, speeding up the hiring process and boosting fill rates.
The same search and match architecture, a very large, complex, yet powerful process, has also emerged as a new analytics approach in data intensive areas like legal precedent search, pharmaceutical search, and real estate search.
Machine Learning & Multilingual Search for a New Age of Recruiting
Today's companies and talents are going global; so is recruiting. Multinationals seeking for local experts will see opportunities to take the same search and match platform one step further: using multilingual text analytics to perform intelligent search and scoring of candidates' data that involves multiple languages and cultural nuances.
And while big data predictive analytics brings proactive recruiting insights, machine learning can continuously “learn” feedback from recruiters and key influencers, systematically improving future candidate search and match.
Recruiting with big data is a use case in our “In the Trenches with Search and Big Data” series – a deep dive into six prevalent applications of big data for modern business. Check out our complete list of six successful big data use cases and stay tuned for more video stories of organizations that found success from these use cases.
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