Many leaders focused on talent acquisition are looking at predictive analytics (aka - talent analytics, workforce science) as a way to improve the talent acquisition process. At a high level, predictive analytics uses historical data and current inputs to make predictions about future behavior.
In the talent acquisition and management industry, predictive analytics is receiving lots of press, focus, and investment. Within the industrial organizational psychology community, they might argue that the last 40 years or so have been all about predictive analytics. The big news is the technology advancements that are enabling predictive analytics to take off. Some examples:
- The growth of cloud-based applications is one technology factor enabling the growth of predictive, data-driven models for hiring. Platforms can be easily connected to create interoperability. So, now employee lifecycle data can be integrated into one data warehouse.
- Data can be captured and stored at a much lower cost with new applications making “big data” easier to manage, process, and analyze.
- Business intelligence engines are more powerful and cost effective to deploy.
How can predictive analytics benefit your pre-employment hiring process?
Predictive analytics can provide you with three key benefits, among many, to help you improve your pre-employment process.
1. Predictive analytics will help you improve your quality of hire
Probably the most important use of predictive analytics is to improve quality of hire. By linking the hiring process to production performance, attrition data, engagement survey information, and other data from the employee lifecycle, models can be developed that will predict the potential future performance of a job candidate. While big gains may be possible, the continuous adjustments to your hiring model will drive marginal improvement. Hiring managers need to make sure their hiring models have measurable processes and objective data that are reliable. Examples include pre-employment assessment results, interview results, engagement survey data, recruiting sources, turnover data, and performance results. The old adage, “garbage in, garbage out” still applies.
2. Predictive analytics will help you source more efficiently and effectively
Tying recruiting sources into a predictive analytics model can enable hiring managers to improve the source of hire. For example, hiring managers can trace the hire back to the original hiring source and then link that to quality of hire. This enables recruiting managers to optimize their recruitment marketing and eliminate poor sources. In addition, this same approach can be used to evaluate in-house recruiters, third-party recruiting firms, job boards, and other recruiting sources. Recruiting leaders can significantly improve the return on investment for their recruitment marketing programs and use the data to negotiate better pricing terms. As a recruiting leader, you now possess significant pricing leverage. Think of meeting with any job board and sharing your quality of hire and cost of hire index with the job board. Those sources that don’t make your top tier either will have to reduce their pricing or be cut from your budget.
3. Predictive analytics will help you improve your speed of hire
A third benefit is speed of hire. As you build your quality of hire models, your understanding of which candidates are the best hires for your company will improve. You will then be able to deploy tools that serve as leading indicators of potential job performance. Once a candidate hits your hiring radar and fits your job models, you will be able to move quickly to connect with them and advance them forward. Because you will have confidence in your hiring model, you will also be able to focus on candidates that are right for your business.
A few things to understand about predictive analytics
Predictive analytics offers tremendous opportunities to use historical data in a way to predict future behavior. Recruiting leaders need to understand that:
- The amount of data involved with predictive analytics requires investment in technology and business intelligence applications to store it and manage it.
- Organizations also need to have a team that understands data modeling, technology infrastructure, and the appropriate experience and credentials to develop and review content. Or, work with teams that can support your modeling needs.
- Models need to be built on reliable data that is captured in a consistent way.
- Data may not tell the entire story. For HR purposes, understanding the story around the hiring process is critical.
Predictive analytics can improve your pre-employment hiring process helping to improve your quality of hire, recruitment sourcing, and speed of hire. Predictive analytics is not new, just faster, better, and cheaper now.