In talent acquisition (TA), predictions are part of life.

Our clients and our delivery teams rely on a view into future hiring volumes, talent supply, and market conditions. That view helps them accurately determine the number and type of resources needed to deliver on time and within budget. Our Sevayo® data integration and predictive analytics platform makes this possible.

Forward-looking data like this is important, and clients should expect such predictive capabilities from a talent solutions partner. But not all predictive analytics platforms are equal. Even in an AI-driven environment, many recruiting organizations remain stuck with information that isn’t reliable or specific enough to act on.

For our teams and our clients, a turning point came a few years ago, and it still sets our work apart today, even as we’ve grown with advances in AI. That’s when we launched a new project to address a stubborn TA challenge: aging requisitions.

Can you predict if a requisition is going to age?

Even in the best talent operations, problem vacancies arise. If left unaddressed, even a few of those unfilled assignments create outliers that can reduce TA performance and leave the business without the critical talent it needs.

Our strategy for the initiative was to do more than predict a market or provide general data to influence budgets. We wanted to make granular forecasts that:

• Informed decision-making with detailed and relevant insights
• Delivered a measurable level of reliability
• Offered a path to a specific action that could improve business outcomes

With these parameters in mind, our initiative focused on three fundamental questions:

• Can you predict whether an open requisition will likely age (remain unfilled past its time-to-fill objective) before it even goes live?
• What can you do about it?
• Can you create and demonstrate positive outcomes from your results?

Developing and launching this initiative involved business, TA, and technical expertise. Our innovation team applied machine learning to analyze historical data from multiple clients in our Sevayo platform, ranging from six months to several years (including up to 12 years from a long-term client).

The platform learned from that data to predict when a role was at risk of aging. We identified factors contributing to that risk, set thresholds to monitor current open roles, and then assigned specialist resources specifically to high-risk openings. By predicting and addressing problem areas, we:

• Reduced aging in a challenging clinical hiring effort from roughly 40% to 6%
• Cut overall time-to-fill by 18 days
• Increased the hiring manager Net Promoter® Score (NPS®) to 90+

Now, we apply this predictive modeling practice across our portfolio to drive greater results.

Can you predict whether a requisition will be at risk? Can you do something about it? Can you achieve a positive impact? The answer to all three questions is “yes.” With forward-looking intelligence, TA leaders see the hazards ahead and can steer around them while building a more reliable workforce strategy.

Building predictability into your TA function.

This successful process enhancement highlights advances in predictive analytics that are reshaping what employers expect from their talent solutions partners.

Effective predictive technology depends on more than simply reporting data. It relies on the breadth of data, the integration of sources into one environment, and the predictive analysis of that information to deliver detailed projected outcomes.

Technology alone won’t create solutions to new challenges, but our people couldn’t do it without technology and the work of a dedicated innovation team. Make innovation a priority for your TA function, your workforce solutions partner, or both.

Continuous innovation makes the difference for a lasting and impactful TA function in a business environment that never sits still.