Statistics in Everyday Life: Eliminating “Negative Outcome Variables” - Jennifer Deitsch - ContactCenterWorld.com Blog
Author: Max Simkoff, Evolv-on-Demand CEO
A few weeks ago I flew back to San Francisco from Montrose, CO—a small/remote mountain town. My flight was scheduled to leave Montrose at 8 pm, and get to San Francisco at 11 pm—with a one hour layover in Denver. What actually happened was a 2:30 am arrival at Oakland airport (for those of you not familiar with the Bay Area, it’s about 30 miles from SF Airport) and then a very stressful few days spent undoing the damage done by what I like to call “Negative Outcome Variables”. I’ll explain.
The first NOV that impacted my travel experience was that I chose to take the last flight of the day out of Montrose—and connect through Denver to SF on the last of those daily flights as well. This introduced the potential of missing my flight home. Secondly, I checked a bag, adding another negative to the table. Finally, I had driven to the SF airport and parked my car there, another NOV—adding even more potential for a nasty travel experience.
When the Montrose flight got delayed by an hour and a half and I missed my flight from Denver to SF, it meant that I got to witness this NOV play out in all of its ugly glory. All in all, this meant that I got next to no sleep upon arriving home, and spent the better half of the next morning getting my car and bags.
Had I simply taken some time before travelling to consider the most likely NOVs that drive bad travel experiences, I could have eliminated these variables and improved the odds for a good travel experience considerably.
What does this have to do with hiring? There are many known NOVs that can be identified within the hourly employee hiring process. Here are a few: (1) Proximity of the applicant to the job, (2) inability to commute to the job via reliable transportation, and (3) lack of understanding about the job’s real requirements.
The most extreme example of how all three of these variables could compound on one another to drive a nasty outcome for you: hiring someone who lives 20 miles from your location via freeway (with no other transportation alternative), they have to rely on their roommate to drive them to work, and they accept the job without knowing that it requires them to talk with customers who are oftentimes upset and rude. All three of these NOVs are statistical contributors to a bad outcome—namely, the person getting fired for the punctuality issues that arise when their friend can’t get them to work on time, or the person just quitting because their expectations of the type of work were incorrect.
If you want a better outcome, start by eliminating these variables. Identify whether applicants live close to your location or not. For those outside an acceptable/reasonable distance, help them understand in your pre-screening process how challenging it may be for them to keep the job without reliable transportation. Then help applicants get a realistic picture of what the job actually entails. Ensure they’re aware of what they’re signing up for.
We’ve found at Evolv that eliminating just these three variables can drive down early stage attrition by 20-30%. Evolv’s hiring intelligence platform helps companies identify the most likely NOVs that impact attrition, then weed out candidates most likely to quit early or underperform – improving the odds for a good hire considerably. And that’s just the beginning. Evolv’s solution helps contact centers select for traits that create a center’s most successful employees – those who have the attitude, personality and skills for the job.
Delays, layovers, missing luggage – these problems are a headache. So is a contact center that’s plagued by the re-work of hiring and training replacement workers. It’s time put statistics to work to produce better outcomes, and avoid the turbulence caused by uninformed decision-making.
Publish Date: October 24, 2011 8:53 PM