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2 years ago
Wise Practitioner – Predictive Workforce Analytics Interview Series: Mike Rosenbaum at Arena

 

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Real World Lessons in Predicting Employee Retention and Engagement, we interviewed Mike Rosenbaum, Mike Rosenbaum IMAGEFounder and CEO at Arena. View the Q-and-A below to see how Mike Rosenbaum has incorporated predictive analytics into the workforce at Arena. Also, glimpse what’s in store for the new PAW Workforce conference, May 14-18, 2017.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: Our predictions are primarily used by recruiters and hiring managers in hospitals and senior living facilities to identify the applicants who are most likely to provide certain outcomes, like stay in their role, be an engaged employee, provide high quality care, increase patient satisfaction, or be involved in a medical incident. The benefits of these outcomes are primarily felt by a number of business units, including nursing, patient care, food and nutrition, and housekeeping. Our platform is integrated with the client’s Applicant Tracking System (ATS) and potential employees are asked to interact with us through a portal that is also hosted on our platform. We use the application data, some limited third party data, and the candidate’s behavior on the platform to customize our models for each client, location, and role and to generate predictions for each applicant.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: At Arena our mission is to use data to illuminate talent. We aspire to transform the way the healthcare labor market works in a way that makes employers more efficient and employees more fulfilled. We feel that our focus on hiring is a great place to start, and we are expanding through the employee lifecycle to address areas such as team assembly, promotion decisions, time and attendance, and incumbent attrition. Ultimately, we aspire to help organizations transform themselves to address the challenges of a rapidly changing environment. For example, today health care delivery is going through massive changes, with much of the services that have been provided within the four walls of a hospital moving to clinics, retail outlets, offsite medical labs, and ambulatory surgical centers.  Instead of continuing to have people do work that is no longer needed; we investigate whether they might be a good fit for new the roles that are needed.

Q: When do you think businesses will be ready for “black box” workforce predictive methods, such as Random Forests or Neural Networks?

A: In our experience, business people are more interested in the results than in the predictive methods. Our clients are not as interested in reviewing the models or the statistical techniques as they are in seeing how changing their behavior and decisions will affect their outcomes. Of course we continue to internally investigate additional predictive methods to improve accuracy and outcomes.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: The work of a data scientist is undoubtedly complex, but we have found that it is rarely effective to try and explain that complexity to a business person. We have found a focus on results to be the most persuasive way to convince a business person to use predictive analytics. Many times a simple before and after comparison is enough to get over the first hurdle, and providing a well thought out business case with investments, benefits, and return on investment solidifies the case. Sometimes it can help to use an analogy, like credit scoring or voice recognition or book recommendations to show how complex predictions can easily become a part of simple every day decisions. We also find that giving clear guidance on how to use the predictions can help with adoption; many of our predictions are expressed as percentiles, so explaining that the predictions are mean to rank candidates so the most likely to provide the outcome will be the highest, and the least likely will be the lowest.

Q: What is one specific way in which predictive analytics actively is driving decisions?

A: Many of our clients have open positions that attract dozens, if not hundreds, of applicants. The traditional approach to this would be to have recruiters or hiring managers review every applicant and use their individual judgement to decide which are the best to engage in a hiring conversation. Our platform is being used to replace these judgements (which typically contain biases) with predictions to help them engage with the applicants that are most likely to provide the best outcomes. And by using our platform our clients are also able to remove the personal biases and judgements from their hiring process.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: While many parts of the business are comfortable with data and analytics, HR is often behind the curve in these capacities. The typical HR employee does not have ready access to data, does not perform their own analysis, and may not be familiar with the key performance indicators that are used to measure their performance. In order to fully recognize the benefits of predictive analytics, the HR workforce itself will need to develop competencies in data and analytics. Luckily they are not the first to make this journey; their colleagues in marketing have been making a similar journey over the last several years and provide an excellent roadmap.

Q: Do you have specific business results you can report?

A: At Arena our clients use our platform in 400+ organizations to process over 4 million applications per year. The median reduction in first year employee turnover all of our clients has been 38%, and when compared to control groups (either other roles in the same facility or the same roles in other facilities) the median improvement is 162%. Because we have a 100% success rate in improving retention, it is easy for us to provide a guarantee to our clients, and so we provide a guarantee to all clients that if we do not reduce employee turnover by 10% in our initial implementation we will refund all money paid to us.

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Don’t miss Mike’s conference presentation, Real World Lessons in Predicting Employee Retention and Engagement, at PAW Workforce, on Wednesday, May 17, 2017, from 3:30 to 4:15 pm. Click here to register for attendance.  Use Code PATIMES for 15% off current prices (excludes workshops).

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce

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