Survivorship Bias: Don’t ignore the losers

Vincent Frisina Profile Image


Director, Customer Marketing & Programs

I bet you don’t know just how helpful failures can be. It’s pretty typical for humans to learn about the world through failures. Touching a hot stove, falling out of an unsafe tree, or walking through a burr-patch with bare feet all present clear opportunities to learn from failure in a painful way. The human brain is programmed to learn from the pain, and avoid the situations in the future. But what about times when the pain is less pronounced? Are there groups of failures we should be revisiting, but aren’t?

We’re continuing our series on the Psychology of Telecom Expense Management with a look at Survivorship Bias (if you missed the last article on Confirmation Bias, you can check it out here) In the second of a three part series, we continue our examination of our intrinsic biases. Our brains are pre-programmed with these biases whether we like them or not. Through understanding, however, we can avoid certain categories of mistakes and potentially deliver better products and services to our customers.

Survivorship Bias depends on our tendency to use evidence of success as the primary measure for planning for future successes. You may recognize it as a kind of sample bias, or how some data points get ignored because they don’t prove the theory. Laszlo Bock, head of People Operations at Google, discusses how survivorship bias impacted Google’s hiring practices on an episode of You Are Not So Smart. After years of tricky hiring practices and insane interview questions, the human resources teams at Google decided to use some statistical analysis to see just what a great job they’d done.

“When failure becomes invisible, the difference between failure and success may also become invisible.”

Here’s the problem. Although the interview process predicted the candidate’s failure, Google never hired the person to find out if they actually failed. All measures of success were based on performance of employees who made it through the interview process successfully. Google went back and hired some of the washed out candidates. The interview washouts performed as well as the successful interviewees. Google discovered that while its hiring process was newsworthy, it did not actually predict success and that by eliminating failure data from its sample set, it made the difference between failure and success harder to see.

One place “survivorship bias” rears its ugly head in our world is in zero usage device management. Unused and active device counts certainly drive unnecessary business expenses each month. Telecom Expense Management (TEM) and Wireless Expense Management (WEM) firms focus on keeping this key performance indicator (KPI) down. We consider the zero total usage metric our success pool; if the number is very low, we have a high success rate in keeping zero total usage devices down. What about the lines with only 10 minutes of usage in a billing period? How about those with just a few kilobytes of data usage? These lines have survived the culling of unused lines, but the logic used to select the sample is biased.

Visage Mobile’s MobilityCentral product (shameless plug), you don’t have to choose between analyzing your customers’ successes or the failures, MobilityCentral will do both for you! Zero usage metrics on the Spend dashboard deliver instant access to candidates for disconnection. MobilityCentral also keeps track of contract and termination fee information for every line, so you can see if it’s cost-effective to cancel any active line of service.

It’s always important to review the criteria used to determine your successes. When you know what your failures can tell you, you can better see success!

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