How Can Advanced People Analytics Be Used In Recruiting?
Our guest on this week’s episode of the Digital HR Leaders podcast is Ian O’Keefe, Global Head of Workforce Analytics at JPMorgan Chase. Ian is one of the most knowledgeable, experienced and insightful leaders in the people analytics space.
Building a people analytics function in a global Fortune 50 company is as wonderful an opportunity as it is a significant undertaking. Throughout the interview David and Ian discuss his journey building the people analytics function at JPMorgan Chase, while reflecting on some of the challenges and experience he’s gained working in this space over the last 10 years.
This episode is a must-listen for anyone in a workforce or people analytics role. HR and business professionals interested in how people data can drive business outcomes. And CHROs looking to build or scale their people analytics headquarters.
In this extract taken from their conversation, which you can watch in the video below, Ian shares how, at JPMorgan Chase, they’ve taken a recruiting challenge and turned it into an opportunity leveraging advanced people analytics.
Finding the right people for the right role at the right time, as fast as you can is a challenge faced by almost every organisation. Since people are the biggest and most important drivers of every company’s success, the importance of attracting talented individuals is huge.
Not knowing what the reasons for not finding, attracting and hiring the right people are, makes it impossible to fix and improve the process. This is why many employers have turned to data-driven recruiting and HR analytics.
Data-driven recruiting has been proven to improve some of the most common hiring metrics such as time to hire, cost to hire and quality of hire.
This is exactly what JPMorgan Chase decided to do under Ian O’Keefe’s watch. While speaking with David, Ian shares that they recognised quite early on the challenge that they were faced with, and made the decision to work at turning this challenge into an opportunity. This has been a key focus of their People Analytics function over the last year.
One big push that we've had over the last year or so and we've turned into a product is a problem that I think ... It's an opportunity, and a problem that many orgs face is finding the right people for the right roles at the right time as fast as you can. And the speed and the way that you cut through perhaps a really big pile of applications is difficult. And often that's manual, and the processes by which you do that are often almost working in opposite of quality of candidate. And we partnered pretty early on with our recruiting leads, and our global head of recruiting to attack that problem.
When you’re inundated with ‘millions of applications a year across as many job families as you can count’ the ability to identify potential value becomes increasingly difficult. Being able to sort the resumes, to ‘understand where there might be hidden value… is just really hard to examine and interrogate by human beings when you're talking thousands, tens of thousands, hundreds of thousands’
According to ideal, integrating predictive analytics into a recruitment process can save recruiters up to 23 hours of manual labour a week. Reducing the amount of time taken to shortlist and pre-screen candidates.
And this is exactly what Ian and his team did.
What is predictive analytics?
Predictive Analytics is the use of historical data to make predictions about the future. Essentially looking at information or data from the past in order to determine the likelihood of a future outcome.
Combining predictive analytics with your hiring process creates predictive hiring. Predictive hiring leverages algorithms that have been trained with this historical data to recommend best-fit candidates to recruiters and hiring managers.
How have JPMorgan Chase leveraged predictive signals?
During the conversation with David, Ian describes the machine learning models that were built to understand basic decisions across the applicant flow:
We built a few machine learning models to understand basic decisions across the applicant flow. Is a candidate likely to be reviewed and passed along to a manager and then hired? And then stay in his or her seat longer than 180 days, which is roughly a productivity breakpoint on a lot of the high-volume, high churn, in some cases, roles that we have that are in our call centre environment, that are in our operations environment. That's been a really successful one for us.
The models designed at JPMorgan Chase were not created to make decisions for people, but instead they’ve been designed to give their recruiting organisation another signal to work with. A predictive signal.
When leveraging analytics and technology to streamline processes or remove challenges, not only is it important to create a seamless experience for those leveraging the tool but effective communication is a key element to driving success. Communicating and training the organisation to be able to confidently leverage these predictive signals to support them, is imperative.
We've trained our org to understand what they're looking at in the tools that they work in. We embed a signal in the tools they use every day. It's not a manual offline clunky cross-reference. It's embedded in the tools they use every day. And we've built I think a pretty well-thought experimental design construct to understand how fast and how much quality candidates are passing through the system by recruiters who see a signal versus those who don't see a signal.
Finally, understanding how you’ll be able to tell if your predictive signals are working is extremely important. Ian O’Keefe highlighted that as they developed their predictive signals model during both the implementation and deployment, they ensured measurements of success was something they paid particular detail to.
While predictive analytics and hiring have some significant benefits when enabling organisations to be able find the right candidates, for the right role, at the right time and quickly, it’s important to remember that a model can only take you so far in solving for this challenge. Any model that you build to support your recruitment needs should only ever act as a means of providing data to allow recruiters and managers to make informed decisions, they should not make decisions for them. They should act as a guidance system for recruiters to determine which candidates should be assessed for the role first.
Predictive analytics will definitely improve your time to screen KPI’s, however, determining whether a candidate is the right fit for the team, or whether they have the personality traits and skills required, cannot be identified by a predictive hiring model. Such models will indicate whether candidates have some of the basic requirements and a few other things that are probabilistic of their success in the role, however the need for human interaction is still very much required.
If you’re interested in learning more about how to build a People Analytics strategy, then you might be interested in our online training courses that focus on People Analytics. They cover a range of topics that walk you through the critical areas to include in your People Analytics strategy in more detail.
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ABOUT THE AUTHOR
Ian Bailie is the Managing Director of myHRfuture.com and an advisor and consultant for start-ups focused on HR technology and People Analytics, including Adepto, Worklytics and CognitionX. In his previous role as the Senior Director of People Planning, Analytics and Tools at Cisco Systems, he was responsible for delivering the tools and insights to enable and transform the planning, attraction and management of talent across the organisation globally. Ian is passionate about HR technology and analytics and how to use both to transform the employee experience and prepare companies for the Future of Work.