Cloud, Mobile and Analytics Help Retain EmployeesBy Ariella Brown Print
Concerns about retaining the staff prompted the Anderson Center for Autism to seek a tech solution that was compatible with the center's people-first philosophy.
The Anderson Center for Autism is a nonprofit organization based in Staatsburg, N.Y., that operates a school and 100-acre residential campus comprising 20 buildings, as well as 25 adult houses across three counties. The center uses more than 400 desktops and data centers connected by fiber optics. The entire IT infrastructure has been in the cloud since 2012.
The picture for this organization was very different in 2001, when it was suffering from a lack of funding and inefficient processes, as well as high turnover. Then a new CEO took the helm and brought about a major transformation. Over the next decade, the center set up a cloud-based IT infrastructure and predictive analytics, which streamlined processes, cut down paperwork by 95 percent and reduced turnover significantly.
In 2003, Gregg Paulk was hired as the first IT manager for the center and was tasked with building out the infrastructure. He recalls, "We were in the dark ages." They had around 30 desktops with no funds for expansion.
Paulk explains that the application process for the federal reimbursement program is very IT-intensive. Consequently, his first challenge was making sure the center got the funds to which it was entitled, which represents 90 percent of their spending on students. It's a sizable amount that covers the costs for 550 staff members on the campus and 350 people who care for the adults the center houses.
Retaining Staff and Reducing Turnover
Concerns about retaining the staff and reducing turnover prompted the Anderson Center to seek a technology solution, which they found in UltiPro products from Ultimate Software. Paulk says they were drawn to the company because it shared the center's people-first philosophy.
"We wanted a company that would be able to take advantage of mobile applications and provide an online experience for a geographically dispersed staff," Paulk says. As there are people spread over three counties in a rural area, he adds, "We were concerned about being able to get to our data if the internet went down and being able to process payroll."
Moving to the cloud enabled the center to open up more modules, such as time and attendance, via mobile devices. Paulk describes the mobile app as being "really cool."
There are consultants clocking in at all different times and in various locations, so the app uses a Google map to show where they are and provide an accurate and verifiable trail. It's not just a matter of getting the data in for payroll that makes the difference; it's using it to improve staff retention.
Turnover is costly for the organization, which has to spend $15,000 to $20,000 to train each person hired. In addition, it's disruptive for the students. Consequently, there is significant value in retaining staff.
Sometimes it's a matter of increasing salaries to be more competitive, but the center also seeks out "creative way to identify potential high performers who are at risk of leaving and find ways to engage them and entice them to stay," Paulk explains.
That's where the UltiPro Retention Predictor algorithm comes into play. It identifies staff members who are at risk for leaving so managers can proactively engage them and address their concerns, whether they are personal, financial or workplace-related.
The algorithm is based on more than 30 data points developed over a 12-month period. At the end, an employee is given a score that identifies if the person is a top performer and how likely he or she is to leave the company. It also identifies the departments with the greatest number of staff members who are at risk for leaving. With that information, management can look into what factors increase the risk and what steps can be taken.
The program also offers 52 suggested leadership actions that the center tweaked with its own modifications and additions, such as writing personal thank-you notes for a good job, etc. Proof that the algorithm works has been substantiated by the center's two-year review of the data, which shows that the predictions were accurate.
The key difference is that in the past, managers who were handed a resignation they didn't want to accept didn't have any warning—or any tools to turn the situation around. Now they have both, and, as a result, they have much better control over staff turnover.
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