Managing people, in all their diversity of capabilities, needs and ambitions, is a complex affair. Enterprises collect a large amount of data about their people – demographic and educational information, compensation, training, job history, wellness and much more. Previously, HR teams struggled to extract this information from disparate spreadsheets and client applications, and to make sense of the data to fulfill the organisation’s strategic objectives.
That struggle is now pretty much ancient history, courtesy HR Analytics – known variously as people analytics or employee analytics, this is an umbrella term for new-age software systems that help HR professionals make better data-backed decisions. Usually cloud-based, the systems use predictive analytics to provide real-time information on how you’re managing your workforce.
HR analytics systems perform five key functions:
- Data collection
- Real-time report generation
- Providing insights.
Hiring is an important function of HR teams. Accelerated by both technological advancements such as chatbots, recruitment automation, and virtual meeting spaces, as well as real-world happenings such as the pandemic, the hiring process has moved online in a big way recently. HR analytics systems are proving to be strategic enablers of the hiring function, providing HR teams unparalleled degrees of efficiency and economy.
Data-backed decisions to make the right hire
Data-derived decisions can turbocharge a company’s recruitment team’s efforts. For starters, they can help in writing targeted job descriptions. More importantly, data helps build intelligent recruitment strategies based on market compensation numbers, supply and demand data, turnover rates and objectives such as diversity and inclusion.
Data-backed decisions make sound business sense: as per Kathy Enderes, VP of Talent and Workforce Research at Deloitte Consulting, 55% of high-maturity, high-performing organisations base business decisions on people analytics, thus giving them a strategic edge in the marketplace.
Leveraging the right insights, HR teams hiring remotely can swiftly find answers to questions such as:
- Which job boards should we post on?
- Where should we best make use of the budget to ensure the right people see our JDs?
- What is the quality of my recent hires? Were there any ‘quick quits’ within six months?
Metrics and models to track hiring efficiency
How long does it take to hire an average employee? This is a metric called ‘time-to hire’ – the number encapsulates the efficiency of a long list of activities including job posting, screening candidates, interviewing, background checks, making the offer and onboarding. This number needs to be as low as one can make it. Remote hiring can add complexity to this workflow.
HR analytics can track the entire workflow, highlighting possible points of inefficiency and providing tools to optimize. For example, are sending out emails to candidates taking inordinate amounts of time? Use automated emails to make this happen within a few clicks.
Outlining key metrics for success such as this, building trackers and optimizing workflows are part and parcel of employee analytics systems. Mature platforms such as Hirepro offer targeted workflow solutions that make remote, high-volume hiring (such as campus recruitment) a breeze. Hirepro’s automated, AI-powered campus hiring workflows, and fraud-proof assessment and interviewing systems have been validated by a network of over 10,000+ employers and educational institutions.
Consider another, more amorphous metric: cultural fit. Lack of cultural fit is a key reason for new hires to fail, per research by Robert Half Talent Solutions. Surprisingly, machines are better at psychometric profiling than humans. Analysis of split-second facial expressions during video interviews can yield tremendous insights and build models that help better answer cultural fit questions.
Perhaps the most compelling reason to make HR analytics adoption part of your roadmap is this: it helps save money. Leveraging big data, optimisations in recruitment workflows, automation of previously-manual efforts, better efficiency in data handling, and predictive analytics and modeling supporting better hiring decisions translates into reduced cost of recruitment, and ultimately reduced turnover costs for the enterprise.
Although deploying HR systems can be a significant drain on time and effort initially, the payoff in strategic hiring can be big, impacting even the company’s culture positively. Companies can accelerate this payoff via timely training of HR personnel on the analytics systems being deployed.
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