The hiring process is broken. Do you believe me?
When was the last time you went through the hiring process? From beginning to end—from application, scheduling, and interview, through start date, and to the completion of the probationary period? If it was anytime in the recent past, you most likely learned what candidates, recruiters, and hiring managers already know: hiring is broken.
How is the hiring process broken?
First, it's fair to note that the traditional hiring process is broken for both employers and candidates. HR departments are constantly asked to do more with less. They are struggling to provide the data that proves how they have a financial impact on the company's bottom line. According to McKinsey, 83% of Fortune 500 executives do not trust the effectiveness of their own hiring processes.
Candidate's find the traditional hiring experience to be slow and cumbersome. They feel lost and often wonder what the next step in the process is. According to Career Builder, only 32% of candidates would rate their experience as “very good.” In fact empxtrack reports, that it takes an average of 27 business days to fill an open position and that top candidates are usually out of the job-seeking market within ten days. Ultimately, a broken hiring process results in missing the top talent who could add significant value to your organization.
Unsupported marketing claims about how to “fix” the broken hiring process are not in short supply. HR leaders are skeptical about what to trust. Is a solution that boasts a reduced cost per hire the answer to the broken hiring process? What about the solution that says it can reduce the time to hire by half?
So, how do you measure good hiring? The fix to a broken hiring process is in the data.
It is easy to mistake simple hiring-process metrics, such as time to fill and cost per hire, as indicators of good hiring. But, what truly matters is whether the new hire improves the organization. The problem is that simple metrics are easy to find, while actual indicators of effectiveness, efficiency, and fairness are rare.
How do you measure the effectiveness (quality of hire) of individual hires? There are many ways, some better than others. Common types of metrics are supervisor ratings and objective performance metrics. There are limitations to both, but together they often give a well-rounded view of new hire performance. Of course, some jobs have clearer objective performance metrics than others, so that must be taken into account.
A second way to judge a hiring process involves process efficiency. This is where typical metrics, such as time to fill and cost to hire, come into play. It is essential to note the most useful metrics must take into account contextual information, such as the type of position or new hire value.
Finally, ensuring that hiring processes are fair is crucial. The path to removing bias uses scientifically and legally designed selection procedures complemented by AI. Technology must be implemented in a continuously updated and automated AI-powered SaaS platform. (The technology is effectively the “smarts” making sense of all the data behind the scenes.) With this, AI can effectively eliminate both protected-class adverse impact and other types of common human decision-making bias. Substantial research has demonstrated that without it, decisions continue to be based on biased information that only leads to lower organizational performance.
Effectiveness, efficiency, and fairness are significant, but measuring them in isolation can lead to inaccurate conclusions. If we only look at efficiency, we might conclude that a process is ideal because cost and time to hire are falling. But without knowing the quality of hire and fairness we can't be sure that overall outcomes are improving. The Modern Hire platform is designed to provide such insight.
Make data meaningful
Data are collected in increasing amounts all around us, but their meaning is largely unknown. Data on their own are neither good nor bad, and it is only in its relation to other information that data take on meaning.
Deep learning powers most of the significant advances in technology today. Its ability to make sense of complex and unstructured data is a tremendous advance over historical methods. And on top of that, there is an exponential growth of available data from a variety of HCM systems.
The path to better decisions about people is through the collection and study of data. By combining data from sources across the hiring process and beyond, we can create massive datasets of linked and related data points. And by applying advanced machine learning and deep learning to the dataset, we are able to discover even more powerful relationships in the data. This enables organizations to make reliable, predictive decisions about people that impact organizational performance. In short, the mission is to make data meaningful.
Imagine a world where we can:
- Offer candidates a fast, rewarding, transparent, and engaging hiring experience
- Move from taking hiring practices on faith to having results that are scientifically proven, continuously updated, and monitored
- Give talent acquisition the data it needs to hire the best candidates at a dramatically faster pace.
- Manage the complexity of your unique workflows through a single configurable SaaS platform.
With Modern Hire, these goals are within reach. Find out how Modern Hire clients continually improve hiring results through a more personalized, data-driven experience for candidates, recruiters, and hiring managers.