Tools for Smarter Staffing

Until recently, staffing and scheduling of employees at financial institutions hasn’t usually been very scientific. There were so many compelling places for CUs to apply technology that the potential for improving staffing often escaped notice.

Raise your hand, for instance, if you recognize yourself in this statement, from CUES member Stephanie Graves, teller operations manager at $530 million/55,000-member Gulf Winds Federal Credit Union, with 155 employees in Pensacola, Fla.: “We would basically just fill up all of our teller stations, for lack of a better explanation. There was no staffing to meet member need; it was ‘We’ve got a spot open from 9 to 1, let’s put somebody in that spot.’ That was true for the contact center, the FSR side, and the teller side.”

Beginning in 2010, Gulf Winds FCU changed all that. Graves says that as face-to-face transactions declined—as they are doing at most CUs—she and her colleagues began to notice a lot of people standing around. Rather than make busy work for them to do, the CU contacted Alpharetta, Ga.-based Financial Management Solutions Inc. for an audit.

“They ran baselines for about three months’ worth of transactions and pointed out the numbers that really were high,” says Graves. “One of ours was that 25 percent of the time, our employees were waiting for a member to come in.”

“We do a free trial for prospective clients, focusing initially on the teller side,” explains FMSI Chief Operating Officer Meredith Deen. “We get transaction data from their core system and also some human resources information, and we’re able to show them ... how big of an opportunity they have to reallocate staff.”

Prior to contacting FMSI, $700 million/95,000-member Michigan First Credit Union, with 265 employees in Lathrup Village, Mich., had no productivity data at all, and no notion of how many transactions employees were handling in the branches or in the call center.

Michigan First CU AVP/Branches Jeff Fitrzyk says Lobby Tracker from FMSI gives him and his colleagues clear details on when they need people on the floor. “It gives us the ability to more efficiently staff and schedule lunches,” he points out, “and that helps a lot. It’s allowed us to schedule late days for some team members because we may not need as many team members the first two or three hours of the day. So that saves us, obviously, on hourly pay.”

Similarly, Birmingham, Ala.-based Bancography, a CUES Supplier member, helps financial institutions maintain the appropriate levels of staffing.

“We do a download of detailed transactions with dates and times, and create for them a 30-minute-increment report by average Monday, average Tuesday, average Wednesday,” explains Jamie Eads, senior project manager at Bancography. “If their expectation is that every teller should be doing about 15 transactions an hour, then they can quickly divide it out and know how many people need to be sitting there.”

Eads says her company can help recommend how many tellers a CU should have, what the mix should be between part- and full-time, and what schedules should look like. But some of it comes down to culture. If a CU wants its tellers to talk more and be more engaged, it might lower the transaction expectation; if it would prefer to get members out the door quickly, the expected transactions per hour might be greater.

Bancography customer $2.2 billion/ 198,000-member WSECU, with 500 employees in Olympia, Wash., started out with a transaction report to give managers an initial idea of what staffing levels should be. Now, SVP/Service Delivery Gary Swindler, a CUES member, says his department pulls reports on a quarterly basis and makes staffing adjustments based on changes in FTEs, branch hours, traffic and a number of other factors.

“Eighty or 90 percent of a decision is very quantitative and data-driven,” he says. “Before, one manager’s perception of what is busy vs. another’s might be very different. This has helped normalize that. We set benchmarks for efficiency, and now they self-manage to those reports. When they see that transactions are down, they know they might have to reduce an employee through attrition. If they have a newer branch and it’s growing, [they might be able to] still be efficient if they add half a body or a body.”

Reworking the Work Day

Swindler says his credit union doesn’t generate schedules directly through its Bancography tools, but rather uses the data from those tools to make better decisions. All the transaction data from the CU’s core platform from Symitar - a Jack Henry Company is broken down into half-hour increments by branch, so he can see peak volumes by day, by week and by month. Using this information, branch managers use Excel spreadsheets to schedule their member consultants. The institution cross-trains all its staffers as universal employees (or “member consultants”), so there’s no worrying about the mix of employees.

WSECU gets a little bit of additional help in figuring out peak-time schedules from a piece of software called Better Lobby, from Concord, Calif.-based Better Branches Technology.

“When you check in as a member, you’re put into a queue and you’re routed to somebody,” Swindler explains. “It timestamps everything. You can check yourself in and out at lunch as a member consultant, or when you’re on a break or when you’re with a member. Leaders can see all that going on on their screen, and so do the other employees. The employees can say, ‘Oh, I see two people are checked out to lunch. I can’t go right now.’ So they end up self-managing.”

Deen says FMSI’s Staff Scheduler can incorporate employee profiles into the scheduling process: their availability, their lunch preferences, their skill sets, their vacations, and any task assignments they’re responsible for.

“What happens in the scheduler is it matches the forecasted demand of the members to all these preferences of the employees and creates a schedule for each day of the month,” she says.

Michigan First CU’s SVP/Branch Operations Janet Ososki, says her team alters schedules based on historical data around marketing campaigns. And they’ll do the same when they finish crunching the data from a major schedule change.

“We just went 24/7/365 [in the call center] at the end of March 2014, so we’re still playing with holidays,” she says. “Is Memorial Day going to be busy, or not? Until we get through a full year and it’s tracked ... we won’t know, really, what kind of staffing we’re going to need.”

Flexing Schedules

The incorporation of technology into the staffing and scheduling process coincides with a sea change in the way employees work. Gone are the “bankers’ hours” of the past; now, at many CUs, the mix is changing to a higher percentage of part-time workers. Even full-timers are beginning to work when they’re actually needed rather than when it’s customary to do so.

There’s almost always some pushback from employees when credit unions change to more flexible staffing, but Deen says that can generally be overcome with incentive plans. When employees find they can make more money by working efficiently, they start to have a change of heart. And over time, as new employees come in, the flexible schedule is all they know.

CUES member Ashley Jansky, VP/operations at Gulf Winds FCU, says part-time tellers are more efficient on busier days because they’re additive. If you only have three full-time tellers, that’s the most people you can have working on a busy day. But if you have, say, two full-timers and two part-timers, then you can have four people working through peaks.

Graves says because the CU sees value in part-time work, it has actually increased benefits for part-time workers. “We see that it is the most cost-effective,” she says. “We’re not having to pay out full-time benefits, but we’re able to give our part-timers a good benefits package that does draw them here and keep them here. It’s [attractive to] college students that are looking for a good, stable working environment.”

Time and Money

By changing its staffing mix and keeping an eye on peak times, Gulf Winds FCU has increased teller efficiency from a worrying 23.5 percent waiting-for-members time to an average of 2.76 percent. Transactions per hour are up to an average of 20, from an earlier 15.9 percent.

In addition to helping the organization staff and schedule more efficiently, the tools are useful for training and coaching.

“Sometimes you have a teller [who] you may have thought was working very steadily, and then you get the numbers and you see ... their utilization is low,” Graves says. “What’s going on here? How can we coach to this? It’s really been, for me, an excellent resource for those coaching conversations.”

Michigan First CU’s Fitrzyk agrees. “We use the information to develop team members as well,” he says. “At the end of the month we get details about how many members they’ve seen in an average day and how much time they spend with a member, on average. It gives us an opportunity to sit team members down who are kind of outside the norm, outside the average, and talk to them about some of the struggles or challenges they are having.”

Michigan First CU was tracking member service meticulously before it started using high-tech tools for the job. Making the process automatic has saved the CU countless person-hours.

“One of our overall corporate goals is that all members in the FSR area must be serviced within 10 minutes, and we measure that monthly,” Ososki says. “Before we had this tool, we manually tracked member service on a spreadsheet and wrote the time in and time out. Probably in an eight-hour day we’d spend at least a couple of hours manually tracking it. And then at the end of the month, the managers spent at least four or five hours putting their reports together for me. So this was a way to automate that process.”

There’s also a major improvement in the immediacy of data. Before, managers would see patterns in member service at the end of the day. Now, they see them as they happen. If a member has been sitting for nearly 10 minutes, managers get an email flash, and they can step out onto the floor to lend a hand.

At WSECU, Swindler has seen real gains in efficiency, measurable in dollars. Before the audit, the CU had 146 front-line employees across 18 branches. In 2013, the first year of monitoring sales patterns, it was able to slim down by 16 FTEs. In 2014, it’s on track to reduce by about 10 more.

“That will put us at about 26 or 27 over a two-year period,” he says. “That equates to, in dollars, about $1.4 million gross, and then when you net out that the member consultants [the universal employees] make a little bit more than a teller did before, it’s about $1.1 million net in savings. That will carry us forward forever. [In the future], we might have one employee reduced here, a half there. It just depends on consumer behavior for how many transactions we’re doing. But the model will keep guiding us through.”

Jamie Swedberg is a freelance writer based in Athens, Ga.