Sneaky Numbers
My latest assignment was to analyze a bunch of HR data for a hypothetical company. I was given every employee’s sales goals, earned revenue, base pay, and the rate of commission they’d earn based on how much money they brought in over their sales goal, or quota.
I’ve analyzed plenty of HR data for federal government contracts. A branch of the US military needed demographic data for their annual reports; an agency’s director needed to know whether or not they should re-direct field officers to different regions. In the world of public service, HR data is used to manage tons of people all at once, get them their resources and benefits, and report their general situation up the chain.
HR in the for-profit world, it turns out, is a completely different beast. My course walked me through an example analysis of this data where we increased the number of employees as well as their quota/sales goals. The result? More profit!
I was then instructed to come up with my own analysis and recommendation. The message was clear: I was to use this data to come up with a pitch that would make the company more money. Sure, we’d want to keep employees happy enough to stay and perform, but it all boiled down to net revenue in the end.
I gave it a try. I set up my data model in Excel so I could change some of the numbers and see how it all played out in costs (how much the company paid their employees) and revenue for the company. I shifted the commission structure to increase the amount of money a sales person would get, on top of their salary, for how much money they brought in above their quota. But that just increased the amount of pay going out to employees. That wasn’t the assignment; I needed to make the company more money. So, I increased the quota they had to reach before getting that commission.
That increased quota snuck up on my calculations quick. Assuming the employees sold about the same going forward, it would be much more difficult for them to make a lot in commission by the end of the year. Meanwhile, with the company paying out less commission, my non-existent C-suite made a huge jump in profits for themselves.
I couldn’t help but realize how easy this would be to pull off. You could market the idea to the executives, and if any employees notice the change, just remind them that the commission rate went up as well. Sure, you need to bring in more money, but you get much MORE pay for the extra money you bring in! It’s really the employee that determines how much they make, right? Just get above that quota and you’re golden!
Meanwhile the company is rolling in extra cash. All by shifting just a couple of little, tiny, sneaky numbers at the start of the fiscal year.
This right here is why I care so much about data. People here have a tendency to separate the human element from raw numbers on a spreadsheet while the people on that spreadsheet get screwed because of it.
None of this is new, either. Before I started my Thinkful course, I read “How To Lie with Statistics” written by journalist Darrell Huff in 1954. It’s a plain-English translation of the ways raw stats are used and abused to tell a skewed story. From ad companies to union-busting corporate executives, from door-to-door salesmen to powerful politicians, statistics are a popular tool for anybody with something to sell.
Examples from that book were way too familiar. Misleading charts, biased samples, common misunderstandings about how percentages work… Every kind of tactic is still employed today, spreading misinformation and exploiting the attention of the general public for profit and personal or political gain.
Yet here’s what gives me hope: That book? It’s a classic now. People read it in schools and universities everywhere to get a feel for what NOT to do and what to watch for in their daily lives. Sure, the liars are getting more creative, but that’s because we keep finding them out. Nobody likes to get taken in. When someone shows you how an ad is misleading or compares your salary to the net worth of your company and the pay your boss gets, well, that’s pretty difficult to un-see.
There’s not much I can do for my hypothetical company’s employees. As much fun as it would be to work out how to distribute that company’s profits to the entire workforce, this is homework, and I’m on a deadline. I’ll keep my head down for now. But I’m learning. I’ll learn all I can about how those sneaky little numbers tip the scales for those in power. And you can be sure I won’t keep that insight to myself.
