Improve operational efficiency and talent management with data
Recruitment professionals in the technology field will often say that top talent is tough to find, for two reasons. The first is that it's challenging for organizations to match volumes of applicants with open roles. Second, top candidates are in short supply. According to the MRINetwork's most recent Recruiter Sentiment Study, the percentage of recruiters who say candidates are steering their own experiences has jumped 29 points to 83 percent. Success in recruiting will depend on an organization's ability to improve operational efficiency. Data can help.
1. Talent communities
Recent research from LinkedIn points out that that at any given time, only 12 percent of workers are actively looking for a job, but only 15 percent are completely satisfied with their positions. The others are casually looking, open to talking to recruiters and staying in touch with their personal networks. That's why it's important for organizations to develop pipelines of passive candidates. Thanks to big data, recruiters and hiring managers can optimize these candidate relationships into full-fledged resources for sentiment analysis, behavioral targeting and predictive analysis to reach job seekers when they're ready to switch careers.
One online retailer, for example, has created an insider community in which candidates can learn about the company culture, share their interests and follow company developments long-term. This platform allows the business to build data-driven talent relationships at scale. Recruiters can monitor and respond to candidate interactions, cultivating lasting relationships.
2. Happiness predictors
Employee disengagement is one of the biggest challenges affecting the workforce today. According to research from Gallup, 68.5 percent of the workforce considers itself "not engaged" or "actively disengaged."
Good.Co, a San Francisco-based startup, believes that the solution to this challenge lies in predicting the likelihood of employee happiness before the candidate accepts a potentially poor-fitting role. Through predictive analytics, Good.Co assigns a likelihood score for a candidate's happiness potential in a particular role. The company's algorithm looks at 48 separate traits across 17 personality aspects. Each employee, according to Good.Co, falls into one of 16 personal and eight organizational archetypes that can be combined to predict the strength of a match. When employees enjoy being in the workplace, their positive attitudes are more likely to improve efficiency and customer interactions.
3. Quality-of-hire predictions
PricewaterhouseCoopers (PwC) asserts that recruiting today is based on guesswork. Recruiters and managers will often make decisions based on trends and past performance, qualifiers that may not be transferable between organizations. While interviews and qualitative research will always be essential to the process, organizations can streamline their efforts by forging stronger connections between hiring decisions and long-term business results. According to PwC, organizations can achieve this goal using the following methodology:
- Identify pivotal roles within an organization.
- Define success criteria for these roles.
- Build company and role-specific hypotheses that explain why some hires perform better than others.
- Craft statistical models to test these assumptions.
Through data modeling, companies will be able to predict the quality of hires over time and eliminate guesswork from their candidate-sourcing processes.
Recruiters rely on assumptions to make their best judgment calls. While these perspectives are important, they're only part of the hiring equation. Predictive analytics can improve operational efficiency while helping uncover hidden gems, missed opportunities and all-around great people.
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