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The latest news, trends and information to help you with your recruiting efforts.

Posted June 28, 2017 by

Sourcing and evaluation: Employers’ flawed assumptions, and how mobile recruiting changes everything

 

This blog is an excerpt of Steven Rothberg’s white paper, “How Employers Evaluate Career Services, Job Boards, and Other Sources, and How Mobile Recruiting Changes Everything.”

Read the entire white paper here (no need to register to download).

Few employers properly track candidate sources

The technology that allows an advertiser to track a consumer from their click on an ad to the advertiser’s website, and ultimately to a purchase, has existed since the mid-1990’s. For example, when College Recruiter began using this technology in 1998, within months, one of the world’s largest hospitality companies was paying us $0.05 per click in return for driving thousands of students and recent graduates a month to apply on their career website. (more…)

Posted June 19, 2017 by

Pre-hire assessments: pros and cons

 

Pre-hire assessments are becoming increasingly more common in the recruiting world — but that might not necessarily be a great idea for the HR space.

The rise of pre-hire assessments

Traditional hiring processes involved an HR-led screen of candidates, followed by phone screens, then in-person interviews, perhaps full-team meetings, and ultimately candidate selection.

As recruiting increasingly became digital, though, there was a bit of a supply-demand problem here. For example, in 2012 7 million people applied for 260,000 British call center jobs. Companies in multiple industries began seeing a need for lower-cost, less-time-consuming hiring processes that yielded quality results. (Additionally, some statistics indicate 50% or more of candidates — it varies by country — embellish their resumes and reflect skills they don’t have.) (more…)

Posted June 09, 2017 by

Strategies for recruiting data analytics and related skills

 

Do employers truly understand their own dire need for data analytics, or more broadly, data science and analytics skills? A new report says that by 2020, new job postings that require these skills will hit 2.72 million. There is a concerning gap between the expectations of educators and the expectations of business executives when it comes to getting students ready for the job market. That is according to a study released by the Business-Higher Education Forum and PwC.

If you are like most employers, in the next several years you will prefer job candidates with data science and analytics skills. And yet, only 23 percent of educators believe their graduates will possess those skills.

The report makes concrete suggestions for both employers and higher education. Here, we will highlight the recommendations for employers who need to harness skills in data science and analytics.

What exactly are data science and analytics skills?

According to the report, “The term analytics refers to the synthesis of knowledge from information. It’s one of the steps in the data life cycle: collection of raw data, preparation of information, analytics, visualization, and access. Data science is the extraction of actionable knowledge directly from data through either a process of discovery, or hypothesis formulation and hypothesis testing.”

People who need to make data-driven decisions include directors in Human Resources, Marketing, IT, and the C-suite. Data science jobs include systems analysts, data administrators, business intelligence analysts, data engineers and much more.

This skills gap affects much more than just data scientists. Jobs from the C-suite to the frontlines are increasingly affected by the need for analytics. According to the report, this is a revolution. “As with the revolution in work brought on by the personal computer (PC) 30 years ago, data science and analytics, hand in hand with machine intelligence and automation, are creating a new revolution in work.”

Businesses who do not attract and retain talent in data science and analytics will eventually be outcompeted.

What does a business do to attract and retain skills in data science and analytics?

The report details four recommendations to employers:

  1. Look beyond the diploma and hire for skills, too.

It’s time to admit that a degree is only a proxy for skill sets. While recruiters can argue the effectiveness of using proxies, it just doesn’t work with DSA skills. The market for these skills is full of disconnected dots. STEM grads are not necessarily prepared to use DSA in business, and business grads are not necessarily taught DSA skills. There is a growing number of DSA degrees, but they haven’t been around long enough for many recruiters to trust their viability, let alone assume they will make the list of annual campus visits.

Where does this leave us? According to the report, “It is left to hiring managers and recruiters to determine how candidates meet skill requirements in this changing environment. To do that they need two things: 1) a common nomenclature to trade in DSA competencies and skills; and 2) a closer, more collaborative relationship with higher education aimed at creating programs that will provide job candidates with the skills they need.”

Researchers have identified skills common to data science jobs across broad skill groups. Those are:

  • Applied domain skills (research or business)
  • Data analytics and machine learning
  • Data management and curation
  • Data science engineering
  • Scientific or research methods
  • Personal and interpersonal communication skills

Employers shouldn’t expect to find all of the above skills in one individual. Rather, they should use these skill groups as a guide to forming teams whose members collectively have a full skill set.

These skills fall into three categories that employers should assess: data analysis, decision-making and problem-framing: (more…)

exaqueo.com

Posted January 06, 2017 by

Takeaways from College Recruiting Bootcamp at the U.S. Securities and Exchange Commission

Photo from exaqueo.com

We asked a few people who attended last month’s College Recruiting Bootcamp about their takeaways. Several weeks after the event, they are still thinking about our conversations regarding relationships, data and metrics, and work culture.

Cassandra Jennings, University Relationship Manager, FDM Group: The greatest takeaway from the bootcamp experience is that no matter the industry or company, we have a shared need to connect and build campus relationships that are successful and make a difference to the bottom lines at our firms.  Though technology is ever changing, students still need to connect and we need to wade through all of the external noise and help students understand who we are, what we do and how we work in an honest and down-to-earth voice.

Along with the challenges of messaging, we also need to keep an eye on meaningful metrics to help us communicate the importance of university relations and the positive impact it makes on the business.

We are a few weeks away from the bootcamp and I’m still thinking about how our company, FDM Group can convey our brand on campus in a meaningful way.  We hired more than 600 students in 2016 and anticipate that our campus recruitment numbers will increase exponentially this year as our business continues to grow in North America. This is an exciting time at our firm and we need students to understand that this is a great opportunity to get valuable work experience and a great place to launch a career with us.  (more…)

Posted December 28, 2016 by

Talent Acquisition in 2017: Q&A with the Experts

In today’s “Q & A with the Experts”, College Recruiter spoke with Ashley White, Human Resources Director for The American Productivity & Quality Center. We asked Ashley about how 2017 might look the same or different regarding their recruitment strategy.

What does your recruitment strategy look like for 2017?

Ashley White: For 2017, our employee engagement and retention strategy is based on “manage and measure.” Management for us means managing the employee experience from the very beginning of their employee experience. In my experience, engagement is different for each individual and organizations that “do” engagement effectively create opportunities for their teams to connect with the organization’s mission and each other in different ways (team building, social events, charitable efforts etc). We expect to continue providing all of these in 2017. For example, our managers are expected to budget for and carry out team building events each quarter with their teams. With any strategy, measurement is important to justify expenses, make improvements and chart progress. APQC will utilize an employee satisfaction survey done twice annually to capture this data. The ongoing challenge with surveys is ensuring that you’ve crafted the questions so that you receive valuable feedback that creates actionable results. With that said, we will spend time utilizing best practice research to guide our question selection.

 

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Ashley White is the Human Resources Director for APQC (The American Productivity & Quality Center). She manages all aspects of human resources including benefits, compensation, recruiting, and strategies. She also leads the APQC operations team that focuses on developing next-generation leaders within the organization. APQC is a non-profit that produces some of the leading benchmarking and best practices research around talent management and other business topics. Connect with Ashley on LinkedIn.

 

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Posted October 17, 2016 by

The hidden problem with big data

1547174HR has long measured recruitment success. Now, in the age of “big data”, we are generating so much more to measure. One benefit of analyzing big data is that with more information we’ll have better decision-making and reduce the stubborn subjectivity that comes with using human brains.

Right?

We should be cautious to assume that human bias will disappear just because we have more analytical tools at hand. In fact, big data can expose our bias and force you to walk the walk. Once you track all those numbers, some unconscious bias and unintended discrimination may emerge and will now be in plain sight. Ultimately, this accountability is a great step forward in recruitment. You’ll just want to make sure your company is ready to respond. Here are three examples of where it’s wise to examine your data practices.

Scraping personal data from online sources. It wouldn’t be too hard to discover a candidate’s race or sexual orientation, given how much personal trace we all leave on the Internet. We’d love to assume those factors make no difference, but too many studies have shown otherwise. Some minority job applicants have even resorted to “whitening” their resumes. A study published this year showed that minority applicants were more successful if they deleted information from their resume that hinted at their race, for example, if they attended a Historically Black College or were a member of Hispanic professional association.

Key word searching. Keyword searching can be a great way to sort out quality candidates among the thousands of real or potential applicants. However, employers must “apply the same rigor that they would use when creating job advertisements. For example, avoid any terms that could be considered directly or indirectly discriminatory (e.g., ‘‘recent graduate,’’ ‘‘highly experienced.’).”

Hiring tests. Many companies give candidates a test at the interview stage to help them make decisions based on qualitative data. It sounds great, and can be, if it’s administered fairly. If you use these tests, you must use them for all applicants. And you must—gasp!—actually pay attention to the data. For example, it wouldn’t be fair to only give the test to minority candidates (this happened), or ignore White candidates’ bad test results (this happened too).

Using big data can be used to make good hires. Just don’t forget to be honest with yourself. If you analyze a big pool of data to select qualified candidates, and they all end up being of one race and one gender, this is a sign you may have accidentally inserted your own bias. Go back to the steps in your process. Ask yourself, “Are my words or actions appealing to only certain demographics?” (This recruiting tech company uses their own big data to help you look at wording in your job postings, for example.) As one of America’s most popular economists, Stephen Dubner of Freakonomics fame, puts it:

We believe that if you get a pile of data representing a million decisions, that that’s better than asking three people what decisions they made. While I very much believe that to be true, and I very much applaud all the instincts for all of us to work with data in aggregate to distill the biggest truths, I also know that we’re humans and that …we’re biased in a lot of ways.