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11.5 Weeks: How Advanced Technology Helped Our Participants Land Jobs in 2020

March 08, 2021 by Josh Hrala

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In 2020, the outbreak of COVID-19 and subsequent shutdowns forced countless companies to furlough and permanently layoff large sections of their workforce, leaving millions without work.

According to a report in December by the US Bureau of Labor Statistics, Americans who lost their jobs last year were unemployed for an average of 23.4 weeks, or nearly half the year.

At Careerminds, last year was a true test of our outplacement technologies. In the end, what we found was that our placement rate remained steady despite these new challenges, and our participants landed new jobs in just 11.5 weeks on average.

“2020 was a tough year for many industries as the pandemic caused many companies to send employees to temporary furlough and permanent layoff,” said Careerminds CEO Raymond Lee of the success the firm has had over the course of the tumultuous year.

“COVID put our contemporary global delivery model to the test as clients looked to us to get their employees back to work despite the economic challenges. I’m incredibly pleased with our placement results.”

So what led to this success? Let’s take a deeper look into the technological backbone that seriously aided our participants during one of the roughest years in market history.

Artificial Intelligence and Machine Learning Is Disrupting the Outplacement Industry

Here at Careerminds, we’re always looking for ways that advanced technology can be used to help our participants during their job search. These technologies are one part of our ‘blended approach’ to outplacement that combines next-gen tech with dedicated, one-on-one coaches who work with participants through their entire journey.

(We’ve talked about our coaching strategy in the past. If you want to learn more, you can read this article for a detailed overview of the blended approach.)

AI and machine learning are taking a lot of the guesswork out of many aspects of the outplacement process by allowing complex algorithms to smooth out the process from start to finish.

Today, we’ll talk about two areas that we have already tackled with these technologies and one more that is currently under development.

Reverse Engineering Applicant Tracking Systems With AI

As you know, applicant tracking systems (ATS) are used by companies to filter job applicants so that hiring managers do not have to manually sift through resumes in order to find a good candidate. While ATS technologies save time for employers, they also hinder a candidate’s ability to land new roles by becoming ‘black holes’ that devour applications with no feedback as to why.

Typically, ATS platforms accept or reject candidates based on keywords found within resumes, meaning that many candidates are passed over without a human actually evaluating their skill sets or meeting with them in-person.


Careerminds has reverse-engineered these systems with a tool called PerfectMatch. PerfectMatch uses artificial intelligence technology to scan resumes and job listings so that candidates can fully understand what keywords they are missing in order to get their application through ATS platforms and into the hands of a hiring manager.

Armed with this knowledge, Careerminds’ participants are more likely to land interviews than those who are submitting resumes blindly, removing a large barrier to entry for job-seekers.

Machine Learning Provides Personalized Training For Participants

Besides tackling ATS platforms with AI, Careerminds also employs machine learning technologies to tailor its learning platform to meet the needs of the individual participant.

Typical outplacement learning platforms are structured in a linear way that all participants progress through. In order to speed up this process and meet participants where they are in their transitional journey, Careerminds has implemented machine learning tools that can better serve learning content when it is actually needed and useful to the participant.

Similar to machine learning algorithms used by content giants such as Netflix and Amazon Prime, the Careerminds learning platform suggests relevant content that is specifically related to the needs of the individual at a specific time.

By doing so, Careerminds is able to better support participants as they progress through the learning platform’s curriculum at a rate that is quicker than if they were to simply follow a linear lesson plan that starts all participants at the exact same place.

Next: AI-powered Interviewing Tools to Give Participants Data-backed Tips

Careerminds will continue pushing the boundaries of the outplacement space by implementing even more AI and machine learning-based tools to better serve participants.

One of the most disruptive of these tools is an AI-based tool that will boost interview performance by examining body language, speech, and overall performance to give data-backed suggestions for improvement.

This technology is also specifically adapted to the changing world of work that is increasingly remote and utilizes popular telecommunications apps like Zoom, which have become the standard interviewing platforms as the job market transforms.

Wrapping Up

Improvements to AI and machine learning are now fostering disruption in the outplacement space by empowering job seekers to land new roles quicker than ever before.

For Careerminds, these technologies have enabled us to help participants land new roles in half the time of the national average even with the challenges the pandemic created in the job market.

We are always pushing the boundaries when it comes to what an outplacement firm can do, and these technologies are just one part of a larger approach that helps us support our participants.

Want to learn more about our outplacement approach? Click here to receive more information.

Josh Hrala

Josh Hrala

Josh is an HR journalist and ghostwriter who's been covering outplacement and offboarding for over six years. Before pivoting to the HR world, he was a science journalist whose work can be found in Popular Science, ScienceAlert, The Huffington Post, Cracked, Modern Notion, and more.

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