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Table of contents
- 1. Why Communicate with Job Applicants
- Communicate Like a Pro
- 3 Rejection Letter Templates You'd Be Happy to Receive - Employment Solutions
- Should you give a candidate feedback after rejecting them?
- Wait, Where Did Our New Hire Go?
Can she do simple programming tasks? And so forth. But just doing the tests is not enough. The economists Mitchell Hoffman, Lisa B. Kahn, and Danielle Li found that even when companies conduct such tests, hiring managers often ignore them—and when they do, they get worse hires. The psychologist Nathan Kuncel and colleagues discovered that even when hiring managers use objective criteria and tests, applying their own weights and judgment to those criteria leads them to pick worse candidates than if they had used a standard formula.
What are they doing instead?
1. Why Communicate with Job Applicants
Seventy-four percent do drug tests, including for marijuana use; even employers in states where recreational use is now legal still seem to do so. Into the testing void has come a new group of entrepreneurs who either are data scientists or have them in tow. They bring a fresh approach to the hiring process—but often with little understanding of how hiring actually works.
John Sumser, of HRExaminer, an online newsletter that focuses on HR technology, estimates that on average, companies get five to seven pitches every day —almost all of them about hiring—from vendors using data science to address HR issues. These vendors have all sorts of cool-sounding assessments, such as computer games that can be scored to predict who will be a good hire.
That aside, these assessments have spawned a counterwave of vendors who help candidates learn how to score well on them. Lloyds Bank, for example, developed a virtual-reality-based assessment of candidate potential, and JobTestPrep offers to teach potential candidates how to do well on it. Especially for IT and technical jobs, cheating on skills tests and even video interviews where colleagues off camera give help is such a concern that eTeki and other specialized vendors help employers figure out who is cheating in real time.
The amount of time employers spend on interviews has almost doubled since , according to research from Glassdoor. How much of that increase represents delays in setting up those interviews is impossible to tell, but it provides at least a partial explanation for why it takes longer to fill jobs now.
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Just winging it and asking whatever comes to mind is next to useless. More important, interviews are where biases most easily show up, because interviewers do usually decide on the fly what to ask of whom and how to interpret the answer. For example, does the fact that an applicant belonged to a fraternity reflect experience working with others or elitism or bad attitudes toward women?
Should it be completely irrelevant? Letting someone with no experience or training make such calls is a recipe for bad hires and, of course, discriminatory behavior. Think hard about whether your interviewing protocols make any sense and resist the urge to bring even more managers into the interview process.
Communicate Like a Pro
Culture fit is another area into which new vendors are swarming. Typically they collect data from current employees, create a machine learning model to predict the attributes of the best ones, and then use that model to hire candidates with the same attributes. As with many other things in this new industry, that sounds good until you think about it; then it becomes replete with problems.
Given the best performers of the past, the algorithm will almost certainly include white and male as key variables. Machine learning models do have the potential to find important but previously unconsidered relationships. Machine learning, in contrast, can come up with highly predictive factors.
Research by Evolv, a workforce analytics pioneer now part of Cornerstone OnDemand , found that expected commuting distance for the candidate predicted turnover very well. And even that question has problems. The advice on selection is straightforward: Test for skills. Ask assessments vendors to show evidence that they can actually predict who the good employees will be.
3 Rejection Letter Templates You'd Be Happy to Receive - Employment Solutions
Do fewer, more-consistent interviews. You must have a way to measure which employees are the best ones. Why is that not getting through to companies? Surely this is a prime example of making the perfect the enemy of the good. Some aspects of performance are not difficult to measure: Do employees quit? Are they absent? Virtually all employers conduct performance appraisals.
Would you hire him again? We have removed the company names after learning that the specifics of their subcontracting practices had not been verified. Peter Cappelli is the George W. He is the author of several books, including Will College Pay Off? The newest development in hiring, which is both promising and worrying, is the rise of data science—driven algorithms to find and assess job candidates.
By my count, more than vendors are creating and selling these tools to companies.
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- Job Offer Life Cycle: Overview.
- Job Offers (Chapter 12) R13 (update 18B).
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- Should you give a candidate feedback after rejecting them?;
Unfortunately, data science—which is still in its infancy when it comes to recruiting and hiring—is not yet the panacea employers hope for. Vendors of these new tools promise they will help reduce the role that social bias plays in hiring. And the algorithms can indeed help identify good job candidates who would previously have been screened out for lack of a certain education or social pedigree.
But these tools may also identify and promote the use of predictive variables that are or should be troubling. Because most data scientists seem to know so little about the context of employment, their tools are often worse than nothing. But a failure to check for any real difference between high-performing and low-performing employees on these attributes limits their usefulness. Furthermore, scooping up data from social media or the websites people have visited also raises important questions about privacy.
Furthermore, is it fair that something you posted as an undergrad can end up driving your hiring algorithm a generation later? Another problem with machine learning approaches is that few employers collect the large volumes of data—number of hires, performance appraisals, and so on—that the algorithms require to make accurate predictions.
Should you give a candidate feedback after rejecting them?
Yet another issue is that all analytic approaches to picking candidates are backward looking, in the sense that they are based on outcomes that have already happened. As Amazon learned, the past may be very different from the future you seek. So the algorithm looked for people just like them. Unable to fix that problem, the company stopped using the algorithm in Nonetheless, many other companies are pressing ahead. The underlying challenge for data scientists is that hiring is simply not like trying to predict, say, when a ball bearing will fail—a question for which any predictive measure might do.
Hiring is so consequential that it is governed not just by legal frameworks but by fundamental notions of fairness. The fact that some criterion is associated with good job performance is necessary but not sufficient for using it in hiring. Take a variable that data scientists have found to have predictive value: commuting distance to the job. According to the data, people with longer commutes suffer higher rates of attrition. However, commuting distance is governed by where you live—which is governed by housing prices, relates to income, and also relates to race.
Wait, Where Did Our New Hire Go?
Picking whom to hire on the basis of where they live most likely has an adverse impact on protected groups such as racial minorities. Unless no other criterion predicts at least as well as the one being used—and that is extremely difficult to determine in machine learning algorithms—companies violate the law if they use hiring criteria that have adverse impacts. Even then, to stay on the right side of the law, they must show why the criterion creates good performance.
That might be possible in the case of commuting time, but—at least for the moment—it is not for facial expressions, social media postings, or other measures whose significance companies cannot demonstrate. Not only is there no free lunch here, but you might be better off skipping the cheap meal altogether.
Dane E. Holmes is the global head of human capital management at Goldman Sachs. Executive Summary Tony Roslund. Related Video. Save Share.
Speaking to a rejected interviewee by phone can be the better way to communicate feedback. It reinforces the personal touch. Plus it reduces the possibility of having your exact words used against you. Emotional connection with a candidate can influence the hiring decision. Behaviours can be changed.
Stay clear of feedback that touches on personal factors. The interviewee is looking for constructive insights. Commence the discussion with some affirmative observations about their behaviour, skills or knowledge. This buffers the blow when you tell them what they need to improve on. Be truthful, up to a point. Just a small portion of applicants will ask for detailed feedback.
Yet everyone you interview deserves an authentic, appreciative reply. At a minimum, try to send all rejected interviewees a gently worded note.
Let them know how much you appreciate their effort. If you genuinely want to keep their resume on file for future opportunities, let them know. For the select few you wish to maintain in your pipeline, make that timely phone call. Your email address will not be published. Job applicants make decisions about your company based on how you treat them. Classic Letter Dear [ candidate name ], Thank you for taking the time to meet with our team about the [ role title ] role at [ company name ]. Feedback Letter Dear Ms.