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Bryan Briscoe, the senior director of compensation at Marriott International, is a self-described “nerdy guy.” He has turned his obsession with numbers and spreadsheets into a career in data analytics that examine compensation.
But the ﬁeld has evolved dramatically since Briscoe, a political science major, joined the human resources ﬁeld 14 years ago. Back then, a basic knowledge of spreadsheets could get you in the door. Now a data analyst needs experience with ever more sophisticated programs like Python or Qlik, Briscoe said.
“Ten years ago, it would have taken months to build things that now you can build with an automatic wizard and three mouse clicks,” Briscoe said. “So what sets people apart is being able to work with bigger data sets than what Excel can handle.”
Briscoe, a member of WorldatWork’s Total Rewards Expert Council, also puts his skills to use in his personal life. Buying a new car means calculating how many months the purchase would delay retirement. Paying to install a hot tub at his family’s home near Atlanta, Ga., took years of weighing the costs and beneﬁts. One thing that couldn’t easily translate into a spreadsheet? The family dog.
“That became an intangible value,” Briscoe said.
What drew you to data analytics?
I was born with a passion to analyze things and ask questions. As you become an adult, you have this itch to know how things work. Why is that that way? Analytics becomes a way to scratch that itch.
Is there any conclusion you’ve reached that you didn’t expect?
When you follow the data, you sometimes ﬁnd that your assumptions are not true, or they might not be true for the reason you thought they would be true. In a prior organization I worked with, I remember looking at data about incentive plans for managers to reduce turnover. To get paid, they had to either keep their turnover below a certain number or improve on their turnover from prior periods.
What we found was that if managers knew they were missing their target, they would try to ﬁre people or make them quit before the month ended. You would see more separation notices in the last week of the month when managers were already having a bad month. Then they could coast for a few months showing “improvement.” Essentially the second part of the plan became an incentive for managers to do a periodic purge.
So instead of having a number that everybody had to hit, we focused on the drivers of higher turnover. At each store, the more revenue you did, the more staff you needed on each shift, and the more staff you had, the more of them quit. Also, there were stores in more seasonal areas, where stafﬁng up and letting people go was the natural cycle. So, we created a customized matrix of acceptable turnover that was based on business factors such as the seasonal nature of revenue and the volume of revenue.
The lesson learned was even if you couldn’t get leadership buy-in to phase out components that are particularly ineffective in bonus plans, you can at least try to make a more level playing ﬁeld and remove counter-incentive metrics.
Where do you see technological advances taking your ﬁeld?
In compensation, we’re going to have to be more agile and more adaptive. The ﬁeld has relied on certain sets of surveys, or certain data sources, or certain tried-and-true methods that everybody can agree are fairly reliable. But the world is getting more comfortable with information coming from different sources and ﬁnding a more amorphous truth. I think people are going to demand faster information, with something more relevant to them.
There is a lot of emphasis now on tailoring compensation and incentives. And on the other hand, there is more pressure to be transparent, fair and standardized. You’re on the forefront of those competing pressures. How do you manage that?
There’s a balancing act that’s going on. It has escalated in the last few years as more state laws and federal laws focus on equality of pay for all people, and as we look at the world going forward, there is more of a push for transparency. That all goes against the grain of the very private, personalized way of traditional compensation approaches. With compensation, there are two directions it could go.
One direction is that incentive plans could be speciﬁc to the individual. And what will become more apparent is the need for transparency and perceived fairness, but also being able to maintain what you do, because you could spend a year designing 400 custom bonus plans for 400 different people, and then the next year they may have different objectives and need different things. A lot of companies can’t necessarily say now what beneﬁt they get from their bonus plans. You’re going to have to explain how having a custom plan for Person A versus Person B is going to be in the best interest of the business in the long run, and that it’s not discriminatory, or unfair toward any particular individual.
On the other side of the spectrum, Daniel Pink and other researchers would say that you just pay people enough money, and if you do that, they will go forward and do their work, because they are mature, grown adults who are engaged with their work. In terms of pay equity and transparency, incentive design, and pay programs, that really simpliﬁes the story.
The reality is companies are going to have to ﬁnd a balance or mix of what works for them on that spectrum between intricate personalization and just giving people money.