We’ve all experienced it—utter disbelief when someone who is obviously ill equipped for a role, typically in management, gets it, or snagged a client we wanted. How do such things occur?
To find the answer, look to the recent Olympics shellacking Russia took at the hands of the Canadians in hockey. Russian goalie Nabokov was pulled less than halfway through the game, after the score became 6-1. Instantly, everyone from broadcasters to people who don’t know anything about hockey denounced Nabokov’s play. Even knowledgeable forums for the sport, like HF Boards, put up threads suggesting Nabokov was the biggest disappointment in the Olympics.
In effort to achieve a goal, businesses and individuals create tools of measurement. Frequently, we find ourselves focusing so much on the measurement that we lose sight of the goal, causing us to make bad choices.
Nabokov stopped 17/23 shots, earning a save percentage of 74% (you want a goalie to be at 90-100%). If you’re a busy decision-maker in Russia, you take a glance at the goalie stats from the game and base future decisions on them, much like a removed corporate or individual decision-maker. You respond to the quantity. You made a bad choice, though, because you failed to interpret the data. (When we interpret data we examine the quality behind the quantity.)
On the other hand, if you watched the actual game, you observed the quality behind the quantity and realized Nabokov’s save percentage reflected a breakdown in defense, not a slump in his performance. In hockey, the goalie’s job is to focus on the shooter (the player with the puck) while the rest of the team’s job is to take away the passing lanes by playing defense and to make sure an opponent is never left alone around their net. Canada was scoring goals left and right off elaborate, uncontested passes and players standing all alone in front of Nabokov at the net. Soon Nabokov couldn’t afford to just focus on the shooter; he had to consider the passing lanes and the player standing in front of the net. This caused Nabokov to neglect aspects of his own job.
Sound familiar? Ever let some of your own responsibilities slide because you have to pick up the slack for someone else, only to later be reprimanded while they are rewarded?
I once consulted for a corporation who wanted to determine why they were promoting so many morons into admissions management at the proprietary colleges they owned. A key tool they used to measure an admissions representative’s effectiveness was how quickly a rep responded to prospective students’ inquiries (leads). They wanted leads responded to the same day they contacted the school. (Leads typically contact their schools via a form on the Internet.) If a lead wasn’t contacted within 48 hours, it was given to another admissions rep; however, the date the lead contacted the school was not changed when the lead was transferred.
The result? Transferring leads made effective reps appear to be inept while making ineffective ones appear to be top performers. For instance, in more than one case, a rep continually failed to contact leads within 48 hours. The leads were then transferred to other reps, who contacted them immediately. When the corporation ran reports, the lead changes did not show. So the best reps, the ones the schools transferred non-contacted leads to the most, appeared to be the worst because many of their leads weren’t called until 72 hours after they contacted the school, showing their Average Contact Time (ACT) to be 2.83 days; however, these reps didn’t get many of their leads until 72 hours after the leads contacted the school, so the reps were actually contacting leads immediately, but that didn’t show in the report! Conversely, reps losing those leads didn’t have them included in their stats, so it appeared they were contacting leads well within 24 hours because their ACT was .87 days.
Guess what? The corporation promoted the latter while reprimanding the former! Hello moronic managers (and low morale).
How did I get the client back on track? What can you do to make sure you stay on track?
Address your real intent. A hockey team’s goal is to win. To what end does a goalie’s save percentage predict a win? A school’s goal is to enroll and graduate students. Does measuring how long it takes admissions reps to respond to new leads predict enrollment and graduation numbers? Dwelling on such stats results in losing sight of the real goals—suddenly we become concerned with save percentage and calling leads quickly. The quantity becomes more important than the quality. Focus on your real intent by considering a broad range of quantitative factors and examining the quality behind the quantity. How did I identify the problem at the corporation? I noticed the reps with a high ACT had a lot more total leads assigned to them. I then visited a sample of their leads. In many of their leads’ personal information it was noted under “comments” that the lead was transferred from another rep. The date of the transfer as well as the other rep’s name was provided. I noticed many of the transfers came from the same few reps, yet noted these reps had the lowest ACT’s. Some of these low ACT reps had been promoted into management positions while none of the reps with high ACT’s had been.
Speaking of ACT’s, when I was in high school, many of my friends enrolled in study courses to improve college entrance exam scores. The courses focused on quantitative data—answer “b” or “c” if you have to guess because they are most frequently correct, etc. I skipped the courses, instead reading texts and studying math. I scored higher than any of my friends—a 32—because I addressed my real intent . . . having the knowledge to be correct.
What tools do you use to measure? I know entrepreneurs who make five cold calls a day. Does that tool of measurement address their real intent of getting new clients? Or would speaking with five new potential clients each day be a better tool? (That could mean many more than five cold calls each day.)
Interpret data by observing, processing, and concluding. When you examine the quality, you must see, listen, process, and conclude. My friend failed to do this at a party once. A pretty woman entered with a guy. She spent most of the night separate from the guy, speaking with my friend. At the end of the night, the guy told her he was ready to leave. She replied, “In a minute.” He went to the front door and waited. She said goodnight to my friend for several minutes then left. My friend didn’t ask for her number because she came and left with the same guy. He reacted to the data instead of interpreting it; he didn’t observe, process, and conclude—she didn’t spend any time with the guy, had an engaging conversation with my friend most of the night, and indicated to the guy he should wait for her while she said goodnight to my friend. To me it was clear she and the guy weren’t a couple. I checked with the hostess, who confirmed my conclusion, informing me the two were friends who lived near each other, sharing rides. Who knows, maybe my friend missed out on his true love?
Data often shifts attention from where it should be, on large scales to small ones. Does it matter what causes global warming? Should we be arguing over data differences or seeking a solution, no matter what the cause?
Make certain data doesn’t deter you from your real intent!
Ian Coburn
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