According to a McKinsey & Company poll, CEOs think their HR organizations lack the ability to use predictive analytics to improve hiring data in a meaningful way in their day-to-day HR processes. They worry they are squandering the proven predictive power of data analytics to make better hiring decisions.
Bringing your data into focus with predictive analytics can change, not just recruiting metric outcomes but also the core of your recruiting metrics.
Traditional recruiting metrics like time to fill, interview to hire ratio, and cost to hire are essential because they provide visibility into how your process functions. Metrics allow you to spot trends and demonstrate improvement over time. They represent the way the hiring process has always been measured.
Using predictive analytics can be complicated if you’re using one (or a few) point solutions. Multiple point solutions can cause confusion among your hiring team and may not be able to communicate with other programs seamlessly, which can cause massive delays in the hiring process.
Leading organizations are finding that full hiring platforms that integrate artificial intelligence (AI) and predictive analytics to improve hiring enable them to take strategic recruiting to new levels and impact their key performance indicators (KPIs).
Download our white paper to learn more about the value of using predictive analytics to improve hiring, the benefits of a scientifically valid, data-driven hiring process, and get answers to questions like:
- How do you use data to predict a candidate’s potential for success?
- How can a business (or a candidate) really know if the role is a good fit?
- How do you measure the economic value of each new hire?
Critical questions like these and more need to be addressed for enterprises to benefit from the power of predictive analytics to improve hiring.